The document discusses streaming data pipelines and includes information about:
- The FLaNK stack which is comprised of Apache NiFi, Apache Kafka, Apache Flink, and Java.
- SQL Stream Builder which allows developers, analysts, and data scientists to write streaming applications using standard SQL without needing to write Java or Scala code.
- Apache Kafka as a distributed, partitioned, and replicated publish-subscribe messaging system.
- Apache Flink which is a framework for distributed stream and batch data processing.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
[DSC Europe 22] Overview of the Databricks Platform - Petar ZecevicDataScienceConferenc1
Databricks' founders caused a seismic shift in data analysis community when they created Apache Spark which has become a cornerstone of Big Data processing pipelines and tools in large and small companies all around the world. Now they've built a revolutionary, comprehensive and easy-to-use platform around Apache Spark and their other inventions, such as MLFlow and Koalas frameworks and most importantly the Data Lakehouse: a concept of fusing data warehouse and data lake architectures into a single versatile and fast platform. Technical foundation for Databricks Data Lakehouse is Delta Lake. More than 7000 organizations today rely on Databricks to enable massive-scale data engineering, collaborative data science, full-lifecycle machine learning and business analytics. Come to the talk and see the demo to find out why.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable.
Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.
This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents.
We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse.
Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scal...Databricks
The reality of most large scale data deployments includes storage decoupled from computation, pipelines operating directly on files and metadata services with no locking mechanisms or transaction tracking. For this reason attempts at achieving transactional behavior, snapshot isolation, safe schema evolution or performant support for CRUD operations has always been marred with tradeoffs.
This talk will focus on technical aspects, practical capabilities and the potential future of three table formats that have emerged in recent years as solutions to the issues mentioned above – ACID ORC (in Hive 3.x), Iceberg and Delta Lake. To provide a richer context, a comparison between traditional databases and big data tools as well as an overview of the reasons for the current state of affairs will be included.
After the talk, the audience is expected to have a clear understanding of the current development trends in large scale table formats, on the conceptual and practical level. This should allow the attendees to make better informed assessments about which approaches to data warehousing, metadata management and data pipelining they should adapt in their organizations.
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Scaling and Modernizing Data Platform with DatabricksDatabricks
Today a Data Platform is expected to process and analyze a multitude of sources spanning batch files, streaming sources, backend databases, REST APIs, and more. There is clearly a need for standardizing the platform that scales and be flexible letting data engineers and data scientists focus on the business problems rather than managing the infrastructure and backend services. Another key aspect of the platform is multi-tenancy to isolate the workloads and able to track cost usage per tenant.
In this talk, Richa Singhal and Esha Shah will cover how to build a scalable Data Platform using Databricks and deploy your data pipelines effectively while managing the costs. The following topics will be covered:
Key tenets of a Data Platform
Setup multistage environment on Databricks
Build data pipelines locally and test on Databricks cluster
CI/CD for data pipelines with Databricks
Orchestrating pipelines using Apache Airflow – Change Data Capture using Databricks Delta
Leveraging Databricks Notebooks for Analytics and Data Science teams
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Meetup Streaming Data Pipeline DevelopmentTimothy Spann
Meetup Streaming Data Pipeline Development
28 June 2023 6pm EST
Milwaukee meetup
https://www.meetup.com/futureofdata-princeton/events/292976004/
Details
This will be a hybrid event with a Zoom. The in-person event will be in Milwaukee.
In this interactive session, Tim will lead participants through how to best build streaming data pipelines. He will cover how to build applications from some common use cases and highlight tips, tricks, best practices and patterns.
He will show how to build the easy way and then dive deep into the underlying open source technologies including Apache NiFi, Apache Flink, Apache Kafka and Apache Iceberg.
If you wish to follow along, please download open source projects beforehand. You can also download this helpful streaming platform: https://docs.cloudera.com/csp-ce/latest/installation/topics/csp-ce-installing-ce.html
All source code and slides will be shared for those interested in building their own FLaNK Apps. https://www.flankstack.dev/
https://www.thecapitalgrille.com/locations/wi/milwaukee/milwaukee/8027
The Capital Grille 310 W Wisconsin Ave, Milwaukee, WI 53203
limited seating, preference will be given to NLIT attendees
A peak at the menu (Not Pizza)
RISOTTO FRITTERS WITH FRESH MOZZARELLA AND PROSCIUTTO
SLICED SIRLOIN WITH ROQUEFORT AND BALSAMIC ONIONS
MINIATURE LOBSTER AND CRAB CAKES
WILD MUSHROOM AND HERBED CHEESE
You can join the meeting virtually here (no meat or cheese virtually):
Future of Data Milwaukee Meetup Streaming Data Pipeline Development 28 June 2023ssuser73434e
Future of Data Milwaukee Meetup Streaming Data Pipeline Development 28 June 2023
Future of Data: New Jersey - Princeton, Edison, Holmdel
This will be a hybrid event with a Zoom. The in-person event will be in Milwaukee.
In this interactive session, Tim will lead participants through how to best build streaming data pipelines. He will cover how to build applications from some common use cases and highlight tips, tricks, best practices and patterns.
He will show how to build the easy way and then dive deep into the underlying open source technologies including Apache NiFi, Apache Flink, Apache Kafka and Apache Iceberg.
If you wish to follow along, please download open source projects beforehand. You can also download this helpful streaming platform: https://docs.cloudera.com/csp-ce/latest/installation/topics/csp-ce-installing-ce.html
All source code and slides will be shared for those interested in building their own FLaNK Apps. https://www.flankstack.dev/
https://www.thecapitalgrille.com/locations/wi/milwaukee/milwaukee/8027
The Capital Grille 310 W Wisconsin Ave, Milwaukee, WI 53203
limited seating, preference will be given to NLIT attendees
A peak at the menu (Not Pizza)
RISOTTO FRITTERS WITH FRESH MOZZARELLA AND PROSCIUTTO
SLICED SIRLOIN WITH ROQUEFORT AND BALSAMIC ONIONS
MINIATURE LOBSTER AND CRAB CAKES
WILD MUSHROOM AND HERBED CHEESE
You can join the meeting virtually here (no meat or cheese virtually):
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
[DSC Europe 22] Overview of the Databricks Platform - Petar ZecevicDataScienceConferenc1
Databricks' founders caused a seismic shift in data analysis community when they created Apache Spark which has become a cornerstone of Big Data processing pipelines and tools in large and small companies all around the world. Now they've built a revolutionary, comprehensive and easy-to-use platform around Apache Spark and their other inventions, such as MLFlow and Koalas frameworks and most importantly the Data Lakehouse: a concept of fusing data warehouse and data lake architectures into a single versatile and fast platform. Technical foundation for Databricks Data Lakehouse is Delta Lake. More than 7000 organizations today rely on Databricks to enable massive-scale data engineering, collaborative data science, full-lifecycle machine learning and business analytics. Come to the talk and see the demo to find out why.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable.
Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.
This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents.
We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse.
Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scal...Databricks
The reality of most large scale data deployments includes storage decoupled from computation, pipelines operating directly on files and metadata services with no locking mechanisms or transaction tracking. For this reason attempts at achieving transactional behavior, snapshot isolation, safe schema evolution or performant support for CRUD operations has always been marred with tradeoffs.
This talk will focus on technical aspects, practical capabilities and the potential future of three table formats that have emerged in recent years as solutions to the issues mentioned above – ACID ORC (in Hive 3.x), Iceberg and Delta Lake. To provide a richer context, a comparison between traditional databases and big data tools as well as an overview of the reasons for the current state of affairs will be included.
After the talk, the audience is expected to have a clear understanding of the current development trends in large scale table formats, on the conceptual and practical level. This should allow the attendees to make better informed assessments about which approaches to data warehousing, metadata management and data pipelining they should adapt in their organizations.
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Scaling and Modernizing Data Platform with DatabricksDatabricks
Today a Data Platform is expected to process and analyze a multitude of sources spanning batch files, streaming sources, backend databases, REST APIs, and more. There is clearly a need for standardizing the platform that scales and be flexible letting data engineers and data scientists focus on the business problems rather than managing the infrastructure and backend services. Another key aspect of the platform is multi-tenancy to isolate the workloads and able to track cost usage per tenant.
In this talk, Richa Singhal and Esha Shah will cover how to build a scalable Data Platform using Databricks and deploy your data pipelines effectively while managing the costs. The following topics will be covered:
Key tenets of a Data Platform
Setup multistage environment on Databricks
Build data pipelines locally and test on Databricks cluster
CI/CD for data pipelines with Databricks
Orchestrating pipelines using Apache Airflow – Change Data Capture using Databricks Delta
Leveraging Databricks Notebooks for Analytics and Data Science teams
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Meetup Streaming Data Pipeline DevelopmentTimothy Spann
Meetup Streaming Data Pipeline Development
28 June 2023 6pm EST
Milwaukee meetup
https://www.meetup.com/futureofdata-princeton/events/292976004/
Details
This will be a hybrid event with a Zoom. The in-person event will be in Milwaukee.
In this interactive session, Tim will lead participants through how to best build streaming data pipelines. He will cover how to build applications from some common use cases and highlight tips, tricks, best practices and patterns.
He will show how to build the easy way and then dive deep into the underlying open source technologies including Apache NiFi, Apache Flink, Apache Kafka and Apache Iceberg.
If you wish to follow along, please download open source projects beforehand. You can also download this helpful streaming platform: https://docs.cloudera.com/csp-ce/latest/installation/topics/csp-ce-installing-ce.html
All source code and slides will be shared for those interested in building their own FLaNK Apps. https://www.flankstack.dev/
https://www.thecapitalgrille.com/locations/wi/milwaukee/milwaukee/8027
The Capital Grille 310 W Wisconsin Ave, Milwaukee, WI 53203
limited seating, preference will be given to NLIT attendees
A peak at the menu (Not Pizza)
RISOTTO FRITTERS WITH FRESH MOZZARELLA AND PROSCIUTTO
SLICED SIRLOIN WITH ROQUEFORT AND BALSAMIC ONIONS
MINIATURE LOBSTER AND CRAB CAKES
WILD MUSHROOM AND HERBED CHEESE
You can join the meeting virtually here (no meat or cheese virtually):
Future of Data Milwaukee Meetup Streaming Data Pipeline Development 28 June 2023ssuser73434e
Future of Data Milwaukee Meetup Streaming Data Pipeline Development 28 June 2023
Future of Data: New Jersey - Princeton, Edison, Holmdel
This will be a hybrid event with a Zoom. The in-person event will be in Milwaukee.
In this interactive session, Tim will lead participants through how to best build streaming data pipelines. He will cover how to build applications from some common use cases and highlight tips, tricks, best practices and patterns.
He will show how to build the easy way and then dive deep into the underlying open source technologies including Apache NiFi, Apache Flink, Apache Kafka and Apache Iceberg.
If you wish to follow along, please download open source projects beforehand. You can also download this helpful streaming platform: https://docs.cloudera.com/csp-ce/latest/installation/topics/csp-ce-installing-ce.html
All source code and slides will be shared for those interested in building their own FLaNK Apps. https://www.flankstack.dev/
https://www.thecapitalgrille.com/locations/wi/milwaukee/milwaukee/8027
The Capital Grille 310 W Wisconsin Ave, Milwaukee, WI 53203
limited seating, preference will be given to NLIT attendees
A peak at the menu (Not Pizza)
RISOTTO FRITTERS WITH FRESH MOZZARELLA AND PROSCIUTTO
SLICED SIRLOIN WITH ROQUEFORT AND BALSAMIC ONIONS
MINIATURE LOBSTER AND CRAB CAKES
WILD MUSHROOM AND HERBED CHEESE
You can join the meeting virtually here (no meat or cheese virtually):
DBA Fundamentals Group: Continuous SQL with Kafka and FlinkTimothy Spann
DBA Fundamentals Group: Continuous SQL with Kafka and Flink
20-Feb-2024
In this talk, I will walk through how someone can set up and run continuous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas, and publishing data.
We will then cover consuming Kafka data, joining Kafka topics, and inserting new events into Kafka topics as they arrive. This basic overview will show hands-on techniques, tips, and examples of how to do this.
Tim Spann
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
ITPC Building Modern Data Streaming AppsTimothy Spann
ITPC Building Modern Data Streaming Apps
https://princetonacm.acm.org/tcfpro/
17th Annual IEEE IT Professional Conference (ITPC)
Armstrong Hall at The College of New Jersey
Friday, March 17th, 2023 at 8:30 AM to 5:00 PM
TCF Photo
In continuous operation since 1976, the Trenton Computer Festival (TCF) is the nation's longest running personal computer. For the seventeenth year, the TCF is extending its program to provide Information Technology and computer professionals with an additional day of conference. It is intended, in an economical way, to provide attendees with insight and information pertinent to their jobs, and to keep them informed of emerging technologies that could impact their work.
The IT Professional Conference is co-sponsored by the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Chapter of Princeton / Central Jersey.
11:00am Building Modern Data Streaming Apps
presented by
Timothy Spann
Building Modern Data Streaming Apps
In this session, I will show you some best practices I have discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there we build streaming ETL with Spark, enhance events with Pulsar Functions for ML and enrichment. We build continuous queries against our topics with Flink SQL.
Timothy Spann
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark.
Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...Timothy Spann
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipelines
https://www.meetup.com/futureofdata-newyork/events/298660453/
Unlocking Financial Data with Real-Time Pipelines
(Flink Analytics on Stocks with SQL )
By Timothy Spann
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. I will be utilizing NiFi 2.0 with Python and Vector Databases.
Timothy Spann
Principal Developer Advocate, Cloudera
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
https://twitter.com/PaaSDev
https://www.linkedin.com/in/timothyspann/
https://medium.com/@tspann
https://github.com/tspannhw/FLiPStackWeekly/
BigDataFest_ Building Modern Data Streaming Appsssuser73434e
https://sessionize.com/big-data-fest-by-softserve/
The Big Data Fest 2023 is a two-day online event that brings together experts, enthusiasts, and members of the community to discuss the latest developments, trending technologies, and tools, and make an impact on the future of Big Data and Data Engineering.
Attendees will have the opportunity to hear from keynote speakers, attend panel discussions and live Q&As, and participate in hands-on workshops.
The event will also feature a charity component aimed at raising money for Open Eyes Fund to buy ambulances for the hottest spots in Ukraine. We invite everyone to support this event and help make a difference in saving lives.
Participation in the event is free, but we encourage attendees to make donations to support this important initiative.
The conference will include a variety of activities divided into cloud streams, such as:
Keynote speeches from leading experts in the field of Big Data
Live Q&As
Panel discussions on the future of Data Engineering
Hands-on workshops on data management and analytics
Networking opportunities with top professionals and leading experts in the field.
Our main goal is to influence the future shape of Data Engineering and promote the use of Big Data for the greater good.
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar and/or Apache Kafka. From there we build streaming ETL with Apache Spark and enhance events with serverless functions for ML and enrichment. We build continuous queries against our topics with Flink SQL. We will stream data into Iceberg and other data stores.
We use the best streaming tools for the current applications with FLiPN and FLaNK. https://www.datainmotion.dev/
https://www.youtube.com/watch?v=qW9CP8Xngk4&ab_channel=SoftServeCareer
Apache NiFi
Apache Flink
Apache Kafka
Apache iceberg
Streams Messaging Manager
SQL Stream Builder
Cloudera DataFlow Designer
NiFi Registry
Cloudera Schema Registry
big data fest building modern data streaming appsTimothy Spann
big data fest building modern data streaming apps
25 May 2023
softtserver
flank stack
apache nifi
apache flink
apache kafka
minifi
java
apache iceberg
cloudera
tim spann
JConWorld_ Continuous SQL with Kafka and FlinkTimothy Spann
JConWorld: Continuous SQL with Kafka and Flink
In this talk, I will walk through how someone can setup and run continous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas and publishing data.
We will then cover consuming Kafka data, joining Kafka topics and inserting new events into Kafka topics as they arrive. This basic over view will show hands-on techniques, tips and examples of how to do this.
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science. https://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html
https://www.youtube.com/channel/UCDIDMDfje6jAvNE8DGkJ3_w?view_as=subscriber
Building Real-time Pipelines with FLaNK_ A Case Study with Transit DataTimothy Spann
Building Real-time Pipelines with FLaNK_ A Case Study with Transit Data
Building Real-time Pipelines with FLaNK: A Case Study with Transit Data
In this session, we will explore the powerful combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines. We will present a case study using the FLaNK-MTA project, which leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). By integrating Flink, NiFi, and Kafka, FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making.
Takeaways:
Understanding the integration of Apache Flink, Apache NiFi, and Apache Kafka for real-time data processing
Insights into building scalable and fault-tolerant data processing pipelines
Best practices for data collection, transformation, and analytics with FLaNK-MTA as a reference
Knowledge of use cases and potential business impact of real-time data processing pipelines
https://github.com/tspannhw/FLaNK-MTA/tree/main
https://medium.com/@tspann/finding-the-best-way-around-7491c76ca4cb
apache nifi
apache kafka
apache flink
apache iceberg
apache parquet
real-time streaming
tim spann
principal developer advocate
cloudera
datainmotion.dev
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...Timothy Spann
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and Kafka
Apache NiFi, Apache Flink, Apache Kafka
Timothy Spann
Principal Developer Advocate
Cloudera
Data in Motion
https://budapestdata.hu/2023/en/speakers/timothy-spann/
Timothy Spann
Principal Developer Advocate
Cloudera (US)
LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
GSJUG: Mastering Data Streaming Pipelines 09May2023Timothy Spann
GSJUG: Mastering Data Streaming Pipelines 09May2023
https://www.meetup.com/futureofdata-princeton/events/293233881/
This is a repost from the Garden State Java Users Group Event.
Join me at
https://www.meetup.com/garden-state-java-user-group/events/293229660/
See: https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.1787151623.1682868226-741104479.1678110925
Please note that registration via EventBrite is required to attend either in-person or online.
We are happy to announce that Tim Spann will be our special guest for the May 9, 2023 meeting!
Abstract:
In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more.
In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL.
We will show where Java fits in as sources, enrichments, NiFi processors and sinks.
We hope to see you on May 9!
Speaker
Timothy Spann
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more.
In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there, we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL.
We will show where Java fits in as sources, enrichments, NiFi processors, and sinks.
https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.178
Unlocking Financial Data with Real-Time Pipelines
tspannOSSFinance_UnlockingFinancialDatawithReal-TimePipelines.pdf
Tim Spann
Twitter: @PaasDev // Blog: datainmotion.dev
Principal Developer Advocate.
Princeton Future of Data Meetup.
ex-Pivotal, ex-Hortonworks, ex-StreamNative, ex-PwC
https://medium.com/@tspann
https://github.com/tspannhw
https://events.linuxfoundation.org/open-source-finance-forum-new-york/
Open Source in Finance Forum NYC
November 1, 2023
Building Real-time Travel Alerts
In this session, we will walk through how to build a complete streaming application to send alerts based on travel advisories from public data. We will also join in other data sources of relevance and push out alerts.
We will show you how to build this streaming application with Apache NiFi, Apache Kafka, and Apache Flink and show you when/why/how, and what to build to maximize performance, productivity, and ease of development.
Let's get streaming.
Apache Flink
Apache Kafka
Apache NiFi
FLaNK Stack
Tim Spann
Big Data Conference Europe 2023
26Oct2023_Adding Generative AI to Real-Time Streaming Pipelines_ NYC MeetupTimothy Spann
26Oct2023_ Adding Generative AI to Real-Time Streaming Pipelines_ NYC Meetup.pdf
## Details
**Important**
Please complete your registration in this short form.
For on-site we have limited room, so please confirm if you are attending in-person in Manhattan, NYC.
--------------------------------------------------------------------------------------------
We're at StarTree, excited to join forces with our friends at Cloudera, for a meetup that is all about The Latest in Real-Time Analytics: Generative AI and LLM, featuring Apache Pinot and Apache NiFi.
Join us for an insightful discussion about cutting-edge analytics, meet the community in person, and catch up over drinks and snacks.
What's the plan ?
05:30-06:00 Pizza and Networking
06:00-06:35 Adding Generative AI to Real-Time Streaming Pipelines | Tim Spann, Principal Developer Advocate, Cloudera
06:35-07:10 Apache Pinot and Kafka an excellent pairing for refined palates | Tim Veil, VP of Solutions Engineering and Enablement, StarTree
07:10-07:20 QNA
07:20- 07:30 More Snacks and Networking ;)
**Important**
Seats are limited
Please complete your registration in this short form.
Adding Generative AI to Real-Time Streaming Pipelines | Timothy Spann
In this talk, Tim will discuss the basics of real-time streaming, walk through the tools used including Apache NiFi, Apache Kafka and Apache Flink and show how to build a real-time streaming pipeline that sends prompts to LLMs hosted by the likes of Hugging Face, IBM and Cloudera. He will also discuss where real-time data stores like Apache Pinot come into play.
He will show a detailed demonstration of a few use cases involving different sources of data including Kafka, Medium Articles and interactive Question and Response in Slack. He will then show you how you can build your own and where areas of growth exist.
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more.
https://github.com/tspannhw/SpeakerProfile
Apache Pinot and Kafka an excellent pairing for refined palates | Tim Veil
The other Tim, Tim Veil, will dive into the history and architect
OSACon 2023_ Unlocking Financial Data with Real-Time PipelinesTimothy Spann
OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
Unlocking Financial Data with Real-Time Pipelines
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data.
Key Points to be Covered:
Introduction to Real-Time Data Pipelines: a. The limitations of traditional batch processing in the financial domain. b. Understanding the need for real-time data processing.
Apache Flink: Powering Real-Time Stream Processing: a. Overview of Apache Flink and its role in real-time stream processing. b. Use cases for Apache Flink in the financial industry. c. How Flink enables fast, scalable, and fault-tolerant processing of streaming financial data.
Apache Kafka: Building Resilient Event Streaming Platforms: a. Introduction to Apache Kafka and its role as a distributed streaming platform. b. Kafka's capabilities in handling high-throughput, fault-tolerant, and real-time data streaming. c. Integration of Kafka with financial data sources and consumers.
Apache NiFi: Data Ingestion and Flow Management: a. Overview of Apache NiFi and its role in data ingestion and flow management. b. Data integration and transformation capabilities of NiFi for financial data. c. Utilizing NiFi to collect and process financial data from diverse sources.
Iceberg: Efficient Data Lake Management: a. Understanding Iceberg and its role in managing large-scale data lakes. b. Iceberg's schema evolution and table-level metadata capabilities. c. How Iceberg simplifies data lake management in financial institutions.
Real-World Use Cases: a. Real-time fraud detection using Flink, Kafka, and NiFi. b. Portfolio risk analysis with Iceberg and Flink. c. Streamlined regulatory reporting leveraging all four technologies.
Best Practices and Considerations: a. Architectural considerations when building real-time financial data pipelines. b. Ensuring data integrity, security, and compliance in real-time pipelines. c. Scalability an
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
https://www.youtube.com/watch?v=Yeua8NlzQ3Y
https://www.conf42.com/Large_Language_Models_LLMs_2024_Tim_Spann_generative_ai_streaming
Adding Generative AI to Real-Time Streaming Pipelines
Abstract
Let’s build streaming pipelines that convert streaming events into prompts, call LLMs, and process the results.
Summary
Tim Spann: My talk is adding generative AI to real time streaming pipelines. I'm going to discuss a couple of different open source technologies. We'll touch on Kafka, Nifi, Flink, Python, Iceberg. All the slides, all the code and GitHub are out there.
Llm, if you didn't know, is rapidly evolving. There's a lot of different ways to interact with models. That enrichment, transformation, processing really needs tools. The amount of models and projects and software that are available is massive.
Nifi supports hundreds of different inputs and can convert them on the fly. Great way to distribute your data quickly to whoever needs it without duplication, without tight coupling. Fun to find new things to integrate into.
So what we can do is, well, I want to get a meetup chat going. I have a processor here that just listens for events as they come from slack. And then I'm going to clean it up, add a couple fields and push that out to slack. Every model is a little bit of different tweaking.
Nifi acts as a whole website. And as you see here, it can be get, post, put, whatever you want. We send that response back to flink and it shows up here. Thank you for attending this talk. I'm going to be speaking at some other events very shortly.
Transcript
This transcript was autogenerated. To make changes, submit a PR.
Hi, Tim Spann here. My talk is adding generative AI to real time streaming pipelines, and we're here for the large language model conference at Comp 42, which is always a nice one, great place to be. I'm going to discuss a couple of different open source technologies that work together to enable you to build real time pipelines using large language models. So we'll touch on Kafka, Nifi, Flink, Python, Iceberg, and I'll show you a little bit of each one in the demos. I've been working with data machine learning, streaming IoT, some other things for a number of years, and you could contact me at any of these places, whether Twitter or whatever it's called, some different blogs, or in person at my meetups and at different conferences around the world. I do a weekly newsletter, cover streaming ML, a lot of LLM, open source, Python, Java, all kinds of fun stuff, as I mentioned, do a bunch of different meetups. They are not just in the east coast of the US, they are available virtually live, and I also put them on YouTube, and if you need them somewhere else, let me know. We publish all the slides, all the code and GitHub. Everything you need is out there. Let's get into the talk. Llm, if you didn't know, is rapidly evolving. While you're typing down the things that you use, it
AIDEVDAY_ Data-in-Motion to Supercharge AITimothy Spann
AIDEVDAY_ Data-in-Motion to Supercharge AI
https://www.meetup.com/futureofdata-newyork/events/295376737/
Lightning Talk 2: Data-in-Motion to Supercharge AI
Speaker: Timothy Spann @Cloudera
Abstract: A quick look at the current state of real-time streaming for powering both data ingest and transformation to provide training and enhancement data to models. Also how to use streaming to feed a pipeline of data against your models or models hosted a HuggingFace or elsewhere.
https://huggingface.co/bigscience/bloom
https://www.aicamp.ai/event/eventdetails/W2023082314
Apache NiFi, Apache Kafka, Apache Flink, HuggingFace, WatsonX.AI, REST API, Cloudera Machine Learning (CML), Bloom, Deep Learning, AI
OSSNA Building Modern Data Streaming AppsTimothy Spann
OSSNA
Building Modern Data Streaming Apps
https://ossna2023.sched.com/event/1Jt05/virtual-building-modern-data-streaming-apps-with-open-source-timothy-spann-streamnative
Timothy Spann
Cloudera
Principal Developer Advocate
Data in Motion
In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize all the best features. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there, we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We make continuous queries against our topics with Flink SQL. We will stream data into various open-source data stores, including Apache Iceberg, Apache Pinot, and others. We use the best streaming tools for the current applications with the open source stack - FLiPN. https://www.flipn.app/ Updates: This will be in-person with live coding based on feedback from the crowd. This will also include new data stores, new sources, and data relevant to and from the Vancouver area. This will also include updates to the platforms and inclusion of Apache Iceberg, Apache Pinot and some other new tech.
https://github.com/tspannhw/SpeakerProfile Tim Spann is a Principal Developer Advocate for Cloudera. He works with Apache Kafka, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
Timothy J Spann
Cloudera
Principal Developer Advocate
Hightstown, NJ
Websitehttps://datainmotion.dev/
BigDataFest Building Modern Data Streaming Appsssuser73434e
BigDataFest: Building Modern Data Streaming Apps
2023
https://app.softserveinc.com/apply/big_data_fest/
CONFERENCE FOR
•DATA ENGINEERS•DATA SCIENTISTS•DATA ARCHITECTS
•DATA AND BUSINESS ANALYSTS•SOFTWARE DEVELOPERS
•ANYONE INTERESTED IN LEARNING MORE ABOUT DATA
Description
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar and/or Apache Kafka. From there we build streaming ETL with Apache Spark and enhance events with serverless functions for ML and enrichment. We build continuous queries against our topics with Flink SQL. We will stream data into Iceberg and other data stores.
We use the best streaming tools for the current applications with FLiPN and FLaNK. https://www.datainmotion.dev/
Tim Spann is a Principal Developer Advocate at Cloudera where he works with Apache Pulsar, Apache Flink, Apache NiFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
https://www.datainmotion.dev/p/about-me.html
https://dzone.com/users/297029/bunkertor.html
https://conferences.oreilly.com/strata/strata-ny-2018/public/schedule/speaker/185963
Similar to Meetup: Streaming Data Pipeline Development (20)
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNKTimothy Spann
Building Real-Time Pipelines With FLaNK
Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp
The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making.
Apache NiFi
Apache Kafka
Apache Flink
Apache Iceberg
LLM
Generative AI
Slack
Postgresql
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
Gen AI on Enterprise Cloud
Apache NiFi
Milvus
Apache Kafka
Apache Flink
Cloudera Machine Learning
Cloudera DataFlow
https://medium.com/@tspann/building-a-milvus-connector-for-nifi-34372cb3c7fa
https://www.meetup.com/futureofdata-princeton/events/300737266/
https://lu.ma/q7pcfyjn?source=post_page-----34372cb3c7fa--------------------------------&tk=TTyakY
If you're interested in working with Generative AI on the cloud, this virtual workshop is for you.
Tim Spann from Cloudera and Yujian Tang from Zilliz will cover how you can implement your own GenAI workflows on the cloud at enterprise scale.
9:00 - 9:05: Intro
9:05 - 9:15: What is Milvus
9:15 - 9:25: Cloudera Development Platform
9:25 - 10:00: Demo
Location
https://www.youtube.com/watch?v=IfWIzKsoHnA
https://github.com/tspannhw/SpeakerProfile
https://www.linkedin.com/in/yujiantang/
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...Timothy Spann
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Transit Data
https://xtremej.dev/2023/schedule/
Building Real-time Pipelines with FLaNK: A Case Study with Transit Data
Overview of the problem, the application (code walkthru and running), overview of FLaNK, introduction to NiFi, introduction to Kafka, and introduction to Flink.
28March2024-Codeless-Generative-AI-Pipelines
https://www.meetup.com/futureofdata-princeton/events/299440871/
https://www.meetup.com/real-time-analytics-meetup-ny/events/299290822/
******Note*****
The event is seat-limited, therefore please complete your registration here. Only people completing the form will be able to attend.
-----------------------
We're excited to invite you to join us in-person, for a Real-Time Analytics exploration!
Join us for an evening of insights, networking as we delve into the OSS technologies shaping the field!
Agenda:
05:30-06:00: Pizza and friends
06:00- 06:40: Codeless GenAI Pipelines with Flink, Kafka, NiFi
06:40- 07:20 Real-Time Analytics in the Corporate World: How Apache Pinot® Powers Industry Leaders
07:20-07:30 QNA
Codeless GenAI Pipelines with Flink, Kafka, NiFi | Tim Spann, Cloudera
Explore the power of real-time streaming with GenAI using Apache NiFi. Learn how NiFi simplifies data engineering workflows, allowing you to focus on creativity over technical complexities. I'll guide you through practical examples, showcasing NiFi's automation impact from ingestion to delivery. Whether you're a seasoned data engineer or new to GenAI, this talk offers valuable insights into optimizing workflows. Join us to unlock the potential of real-time streaming and witness how NiFi makes data engineering a breeze for GenAI applications!
Real-Time Analytics in the Corporate World: How Apache Pinot® Powers Industry Leaders | Viktor Gamov, StarTree
Explore how industry leaders like LinkedIn, Uber Eats, and Stripe are mastering real-time data with Viktor as your guide. Discover how Apache Pinot transforms data into actionable insights instantly. Viktor will showcase Pinot's features, including the Star-Tree Index, and explain why it's a game-changer in data strategy. This session is for everyone, from data geeks to business gurus, eager to uncover the future of tech. Join us and be wowed by the power of real-time analytics with Apache Pinot!
-------
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera.
He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more.
TCFPro24 Building Real-Time Generative AI PipelinesTimothy Spann
https://princetonacm.acm.org/tcfpro/
18th Annual IEEE IT Professional Conference (ITPC)
Armstrong Hall at The College of New Jersey
Friday, March 15th, 2024 | 10:00 AM to 5:00 PM
IT Professional Conference at Trenton Computer Festival
IEEE Information Technology Professional Conference on Friday, March 15th, 2024
TCFPro24 Building Real-Time Generative AI Pipelines
Building Real-Time Generative AI Pipelines
In this talk, Tim will delve into the exciting realm of building real-time generative AI pipelines with streaming capabilities. The discussion will revolve around the integration of cutting-edge technologies to create dynamic and responsive systems that harness the power of generative algorithms.
From leveraging streaming data sources to implementing advanced machine learning models, the presentation will explore the key components necessary for constructing a robust real-time generative AI pipeline. Practical insights, use cases, and best practices will be shared, offering a comprehensive guide for developers and data scientists aspiring to design and implement dynamic AI systems in a streaming environment.
Tim will show a live demo showing we can use Apache NiFi to provide a live chat between a person in Slack and several LLM models all orchestrated with Apache NiFi, Apache Kafka and Python. We will use RAG against Chroma and Pinecone vector data stores, Hugging Face and WatsonX.AI LLM, and add additional context with NiFi lookups of stocks, weather and other data streams in real-time.
Timothy Spann
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark.
Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
Conf42-Python-Building Apache NiFi 2.0 Python Processors
https://www.conf42.com/Python_2024_Tim_Spann_apache_nifi_2_processors
Building Apache NiFi 2.0 Python Processors
Abstract
Let’s enhance real-time streaming pipelines with smart Python code. Adding code for vector databases and LLM.
Summary
Tim Spann: I'm going to be talking today, be building Apache 9520 Python processors. One of the main purposes of supporting Python in the streaming tool Apache Nifi is to interface with new machine learning and AI and Gen AI. He says Python is a real game changer for Cloudera.
You're just going to add some metadata around it. It's a great way to pass a file along without changing it too substantially. We really need you to have Python 310 and again JDK 21 on your machine. You got to be smart about how you use these models.
There are a ton of python processors available. You can use them in multiple ways. We're still in the early world of Python processors, so now's the time to start putting yours out there. Love to see a lot of people write their own.
When we are parsing documents here, again, this is the Python one I'm picking PDF. Lots of different things you could do. If you're interested on writing your own python code for Apache Nifi, definitely reach out and thank.
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg with Stock Data and LLM
Abstract
In this talk, we’ll discuss how to use Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg to process and analyze stock data. We demonstrated the ingestion, processing, and analysis of stock data. Additionally, we illustrated how to use an LLM to generate predictions from the analyzed data.
Karin Wolok
Developer Relations, Dev Marketing, and Community Programming @ Project Elevate
Karin Wolok's LinkedIn account Karin Wolok's twitter account
Tim Spann
Principal Developer Advocate @ Cloudera
Tim Spann's LinkedIn account Tim Spann's twitter account
https://www.conf42.com/Python_2024_Karin_Wolok_Tim_Spann_nifi__kafka_risingwave_iceberg_llm
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI PipelinesTimothy Spann
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
https://www.aicamp.ai/event/eventdetails/W2024022214
apache nifi
llm
generative ai
gen ai
ml
dl
machine learning
apache kafka
apache flink
postgresql
python
AI Meetup (NYC): GenAI, LLMs, ML and Data
Feb 22, 05:30 PM EST
Welcome to the monthly in-person AI meetup in New York City, in collaboration with Microsoft. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers
Agenda:
* 5:30pm~6:00pm: Checkin, Food/drink and networking
* 6:00pm~6:10pm: Welcome/community update
* 6:10pm~8:30pm: Tech talks
* 8:30pm: Q&A, Open discussion
Tech Talk: Searching and Reasoning Over Multimedia Data with Vector Databases and LMMs
Speaker: Zain Hasan (Weaviate LinkedIn)
Abstract: In this talk, Zain Hasan will discuss how we can use open-source multimodal embedding models in conjunction with large generative multimodal models that can that can see, hear, read, and feel data(!), to perform cross-modal search(searching audio with images, videos with text etc.) and multimodal retrieval augmented generation (MM-RAG) at the billion-object scale with the help of open source vector databases. I will also demonstrate, with live code demos, how being able to perform this cross-modal retrieval in real-time can enables users to use LLMs that can reason over their enterprise multimodal data. This talk will revolve around how we can scale the usage of multimodal embedding and generative models in production.
Tech Talk: Codeless Generative AI Pipelines
Speaker: Timothy Spann (Cloudera LinkedIn)
Abstract: Join us for an insightful talk on leveraging the power of real-time streaming tools, specifically Apache NiFi, to revolutionize GenAI data engineering. In this session, we’ll explore how the integration of Apache NiFi can automate the entire process of prompt building, making it a seamless and efficient task.
Speakers/Topics:
Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics
Sponsors:
We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 20,000+ local or 300K+ developers worldwide.
Venue:
Microsoft NYC - Times Square, 11 Times Square, New York, NY 10036
Room Name: Central Park West 6501
Community on Slack/Discord
- Event chat: chat and connect with speakers and attendees
- Sharing blogs, events, job openings, projects collaborations
Join Slack (search and join the #newyork channel) | Join Discord
[EN]DSS23_tspann_Integrating LLM with Streaming Data PipelinesTimothy Spann
[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines
https://dssconf.pl/en/#agenda-section
Integrating LLM with Streaming Data Pipelines
Timothy Spann, Principal Developer Advocate, Cloudera
APACHE NIFI, APACHE FLINK, APACHE KAFKA, LLM, HUGGINGFACE, REST, STREAMING
LESS
In this talk and demo I will walk through how to add LLMs to your streaming pipelines by integration through Apache NiFi.
https://github.com/tspannhw/FLaNK-watsonx.ai
Cloudera
streaming
llm
generative ai
slack to slack
AIDevWorld 23 Apache NiFi 101 Introduction and Best Practices
https://sched.co/1RoAO
Timothy Spann, Cloudera, Principal Developer Advocate
In this talk, we will walk step by step through Apache NiFi from the first load to first application. I will include slides, articles and examples to take away as a Quick Start to utilizing Apache NiFi in your real-time dataflows. I will help you get up and running locally on your laptop, Docker or in CDP Public Cloud.
Wednesday November 1, 2023 12:00pm - 12:25pm PDT
VIRTUAL AI DevWorld -- Main Stage https://app.hopin.com/events/api-world-2023-ai-devworld/stages
Retail & E-Commerce AI (Industry AI Conference)
Session Type OPEN TALK
Track or Conference Retail & E-Commerce AI (Industry AI Conference), Industry AI Conference, VIRTUAL, Tensorflow & PyTorch & Open Source Frameworks (AI/ML Engineering Conference), AI/ML Engineering Conference, AI DevWorld
In-Person/Virtual Virtual, Virtual Exclusive
apache nifi
Timothy Spann
Cloudera
Principal Developer Advocate for Data in Motion
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
cloudera dataflow
CoC23_ Looking at the New Features of Apache NiFiTimothy Spann
CoC23_ Looking at the New Features of Apache NiFi
Apache NiFi has a lot of new features, processors and best practices that have arrived in the last year or so.
I will walk through building flows using the latest tips, techniques and processor.
I will and change a number of data flows utilizing the latest NiFi version and point out gotchas and some never dos. The deck will act as a take-away with notes, tips and guides to what we covered.
===> Any NiFi 1.23+ and 2.0 in progress features people want to see?
CoC23_ Let’s Monitor The Conditions at the ConferenceTimothy Spann
CoC23_ Let’s Monitor The Conditions at the Conference
At home, I monitor the temperature, humidity, gas levels, ozone, air quality, and other features around my desk.Let’s bring this to the different spots around the conference including lunch tables, vendor booths, hotel rooms, and more. I need to know about these readings now, not when I get back home from the conference. We need to get these sensor readings immediately in case we need to turn on a fan or move to another area. We will also see if my talk produces a lot of hot air!?!??My setup is pretty simple, a raspberry pi, a breakout garden sensor mount, and as many sensors as I can carry. All code and parameters will be available ASF-licensed in github.The software stack is Python and Java, MQTT, HTML, JQuery, and Apache NiFi, Apache Kafka and Apache Flink.
CoC23_Utilizing Real-Time Transit Data for Travel OptimizationTimothy Spann
CoC23_Utilizing Real-Time Transit Data for Travel Optimization
@PaasDev www.datainmotion.dev github.com/tspannhw medium.com/@tspann
Principal Developer Advocate
Princeton Future of Data Meetup
ex-Pivotal, ex-Hortonworks, ex-StreamNative, ex-PwC, ex-EY, ex-HPE.
Apache NiFi x Apache Kafka x Apache Flink
There are a lot of factors involved in determining how you can find our way around and avoid delays, bad weather,dangers and expenses. In this talk I will focus on public transport in the largest transit system in the United States, the MTA,
which is focused around New York City. Utilizing public and semi-public data feeds, this can be extended to most city and metropolitan areas around the world. As a personal example, I live in New Jersey and this is an extremely useful use of open source and public
data.
Once I am notified that I need to travel to Manhattan, I need to start my data streams flowing. Most of the data sources are REST feeds that are ingested by Apache NiFi to transform, convert, enrich and finalize it for usage in streaming tables with Flink SQL, but also keep that same contract with Kafka consumers, Iceberg tables and other users of this data. I do not need to many user interfaces to interopt with the system as I want my final decision sent in a Slack message to me and then I’ll get moving. Along the way data will be visible in NiFi lineage, Kafka topic views, Flink SQL output, REST output and Iceberg tables.
Apache NiFi, Apache Kafka, Apache OpenNLP, Apache Tika, Apache Flink, Apache Avro, Apache Parquet, Apache Iceberg.
https://github.com/tspannhw/FLaNK-MTA/tree/main
https://medium.com/@tspann/finding-the-best-way-around-7491c76ca4cb
https://medium.com/@tspann/open-source-streaming-talks-in-progress-3e75af8848b0
https://medium.com/@tspann/watching-airport-traffic-in-real-time-32c522a6e386
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.