Apache NiFi, Storm and Kafka augment each other in modern enterprise architectures. NiFi provides a coding free solution to get many different formats and protocols in and out of Kafka and compliments Kafka with full audit trails and interactive command and control. Storm compliments NiFi with the capability to handle complex event processing.
Join us to learn how Apache NiFi, Storm and Kafka can augment each other for creating a new dataplane connecting multiple systems within your enterprise with ease, speed and increased productivity.
https://www.brighttalk.com/webcast/9573/224063
Dataflow Management From Edge to Core with Apache NiFiDataWorks Summit
What is “dataflow?” — the process and tooling around gathering necessary information and getting it into a useful form to make insights available. Dataflow needs change rapidly — what was noise yesterday may be crucial data today, an API endpoint changes, or a service switches from producing CSV to JSON or Avro. In addition, developers may need to design a flow in a sandbox and deploy to QA or production — and those database passwords aren’t the same (hopefully). Learn about Apache NiFi — a robust and secure framework for dataflow development and monitoring.
Abstract: Identifying, collecting, securing, filtering, prioritizing, transforming, and transporting abstract data is a challenge faced by every organization. Apache NiFi and MiNiFi allow developers to create and refine dataflows with ease and ensure that their critical content is routed, transformed, validated, and delivered across global networks. Learn how the framework enables rapid development of flows, live monitoring and auditing, data protection and sharing. From IoT and machine interaction to log collection, NiFi can scale to meet the needs of your organization. Able to handle both small event messages and “big data” on the scale of terabytes per day, NiFi will provide a platform which lets both engineers and non-technical domain experts collaborate to solve the ingest and storage problems that have plagued enterprises.
Expected prior knowledge / intended audience: developers and data flow managers should be interested in learning about and improving their dataflow problems. The intended audience does not need experience in designing and modifying data flows.
Takeaways: Attendees will gain an understanding of dataflow concepts, data management processes, and flow management (including versioning, rollbacks, promotion between deployment environments, and various backing implementations).
Current uses: I am a committer and PMC member for the Apache NiFi, MiNiFi, and NiFi Registry projects and help numerous users deploy these tools to collect data from an incredibly diverse array of endpoints, aggregate, prioritize, filter, transform, and secure this data, and generate actionable insight from it. Current users of these platforms include many Fortune 100 companies, governments, startups, and individual users across fields like telecommunications, finance, healthcare, automotive, aerospace, and oil & gas, with use cases like fraud detection, logistics management, supply chain management, machine learning, IoT gateway, connected vehicles, smart grids, etc.
Running Apache NiFi with Apache Spark : Integration OptionsTimothy Spann
A walk-through of various options in integration Apache Spark and Apache NiFi in one smooth dataflow. There are now several options in interfacing between Apache NiFi and Apache Spark with Apache Kafka and Apache Livy.
Best Practices for ETL with Apache NiFi on Kubernetes - Albert Lewandowski, G...GetInData
Did you like it? Check out our E-book: Apache NiFi - A Complete Guide
https://ebook.getindata.com/apache-nifi-complete-guide
Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers.
Author: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
This presentation was created as an introduction to the Apache NiFi project; to be followed by “Lab 0” of the “Realtime Event Processing in Hadoop with NiFi, Kafka and Storm” tutorial hosted here: http://hortonworks.com/hadoop-tutorial/realtime-event-processing-nifi-kafka-storm/#section_1
Real-time Twitter Sentiment Analysis and Image Recognition with Apache NiFiTimothy Spann
A walk through of creating a dataflow for ingest of twitter data and analyzing the stream with NLTK Vader Python Sentiment Analysis and Inception v3 TensorFlow via Python in Apache NiFi. Storage in Hadoop HDFS.
Dataflow Management From Edge to Core with Apache NiFiDataWorks Summit
What is “dataflow?” — the process and tooling around gathering necessary information and getting it into a useful form to make insights available. Dataflow needs change rapidly — what was noise yesterday may be crucial data today, an API endpoint changes, or a service switches from producing CSV to JSON or Avro. In addition, developers may need to design a flow in a sandbox and deploy to QA or production — and those database passwords aren’t the same (hopefully). Learn about Apache NiFi — a robust and secure framework for dataflow development and monitoring.
Abstract: Identifying, collecting, securing, filtering, prioritizing, transforming, and transporting abstract data is a challenge faced by every organization. Apache NiFi and MiNiFi allow developers to create and refine dataflows with ease and ensure that their critical content is routed, transformed, validated, and delivered across global networks. Learn how the framework enables rapid development of flows, live monitoring and auditing, data protection and sharing. From IoT and machine interaction to log collection, NiFi can scale to meet the needs of your organization. Able to handle both small event messages and “big data” on the scale of terabytes per day, NiFi will provide a platform which lets both engineers and non-technical domain experts collaborate to solve the ingest and storage problems that have plagued enterprises.
Expected prior knowledge / intended audience: developers and data flow managers should be interested in learning about and improving their dataflow problems. The intended audience does not need experience in designing and modifying data flows.
Takeaways: Attendees will gain an understanding of dataflow concepts, data management processes, and flow management (including versioning, rollbacks, promotion between deployment environments, and various backing implementations).
Current uses: I am a committer and PMC member for the Apache NiFi, MiNiFi, and NiFi Registry projects and help numerous users deploy these tools to collect data from an incredibly diverse array of endpoints, aggregate, prioritize, filter, transform, and secure this data, and generate actionable insight from it. Current users of these platforms include many Fortune 100 companies, governments, startups, and individual users across fields like telecommunications, finance, healthcare, automotive, aerospace, and oil & gas, with use cases like fraud detection, logistics management, supply chain management, machine learning, IoT gateway, connected vehicles, smart grids, etc.
Running Apache NiFi with Apache Spark : Integration OptionsTimothy Spann
A walk-through of various options in integration Apache Spark and Apache NiFi in one smooth dataflow. There are now several options in interfacing between Apache NiFi and Apache Spark with Apache Kafka and Apache Livy.
Best Practices for ETL with Apache NiFi on Kubernetes - Albert Lewandowski, G...GetInData
Did you like it? Check out our E-book: Apache NiFi - A Complete Guide
https://ebook.getindata.com/apache-nifi-complete-guide
Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers.
Author: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
This presentation was created as an introduction to the Apache NiFi project; to be followed by “Lab 0” of the “Realtime Event Processing in Hadoop with NiFi, Kafka and Storm” tutorial hosted here: http://hortonworks.com/hadoop-tutorial/realtime-event-processing-nifi-kafka-storm/#section_1
Real-time Twitter Sentiment Analysis and Image Recognition with Apache NiFiTimothy Spann
A walk through of creating a dataflow for ingest of twitter data and analyzing the stream with NLTK Vader Python Sentiment Analysis and Inception v3 TensorFlow via Python in Apache NiFi. Storage in Hadoop HDFS.
BYOP: Custom Processor Development with Apache NiFiDataWorks Summit
Apache NiFi, a robust, scalable, and secure tool for data flow management, ships with over 212 processors to ingest, route, manipulate, and exfil data from a variety of sources and consumers. But many users turn to NiFi to meet unusual requirements — from proprietary protocol parsing, to running inside connected cars, to offloading massive hardware metrics from oil rigs in the most remote environments. Rather than posting a community request for custom development or offloading unusual demands to unnecessary external systems, there’s an answer in NiFi. Learn how NiFi allows you to quickly prototype custom processors in the scripting language of your choice against live production data without affecting your existing flows. Easily translate prototypes to full-fledged processors to optimize performance and leverage the full provenance reporting infrastructure. Discover how the framework provides conventions to streamline your development and minimize common boilerplate code, and the robust testing framework to make testing easy, and dare we say, fun.
Expected prior knowledge / intended audience: developers and data flow managers should have passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The intended audience will have experience with programming in Groovy, Ruby, Jython, ECMAScript/Javascript, or Lua.
Takeaways: Attendees will gain an understanding in writing custom processors for Apache NiFi, including the component lifecycle, unit and integration testing, quick prototyping using a scripting language of their choice, and the artifact publishing and deployment process.
Introduction: This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
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.
Jeff Sposetti of Ambari discusses the Apache project Ambari used to help deploy and provision Hadoop Clusters
- Ambari Overview and the Community
- Ambari Architecture - Provisioning Clusters and Services -Standard Services: HDFS, YARN, MR2, Hive, new Services: Storm, Falcon
- Management and Monitoring Capabilities -Nagios and Ganglia Integration
- Key Innovation Features -Ambari Stacks providing dynamic service lifecycle -Ambari BluePrints powering Savannah OpenStack -Ambari Views enabling custom UI development
Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease.
Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos.
Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster.
Speaker
Jayush Luniya, Principal Software Engineer, Hortonworks
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
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/
This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
Pre-requisites: Registrants must bring a laptop that has the latest VirtualBox installed and an image for Hortonworks DataFlow (HDF) Sandbox will be provided.
Speaker: Andy LoPresto
Agenda:
1.Data Flow Challenges in an Enterprise
2.Introduction to Apache NiFi
3.Core Features
4.Architecture
5.Demo –Simple Lambda Architecture
6.Use Cases
7.Q & A
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
State of the Apache NiFi Ecosystem & CommunityAccumulo Summit
This talk will discuss the state of the Apache NiFi Ecosystem & Community.
Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems. It provides real-time control that makes it easy to manage the movement of data between any source and any destination. It is data source agnostic, supporting disparate and distributed sources of differing formats, schemas, protocols, speeds and sizes such as machines, geo location devices, click streams, files, social feeds, log files and videos and more. It is configurable plumbing for moving data around, similar to how Fedex, UPS or other courier delivery services move parcels around. And just like those services, Apache NiFi allows you to trace your data in real time, just like you could trace a delivery.
BYOP: Custom Processor Development with Apache NiFiDataWorks Summit
Apache NiFi, a robust, scalable, and secure tool for data flow management, ships with over 212 processors to ingest, route, manipulate, and exfil data from a variety of sources and consumers. But many users turn to NiFi to meet unusual requirements — from proprietary protocol parsing, to running inside connected cars, to offloading massive hardware metrics from oil rigs in the most remote environments. Rather than posting a community request for custom development or offloading unusual demands to unnecessary external systems, there’s an answer in NiFi. Learn how NiFi allows you to quickly prototype custom processors in the scripting language of your choice against live production data without affecting your existing flows. Easily translate prototypes to full-fledged processors to optimize performance and leverage the full provenance reporting infrastructure. Discover how the framework provides conventions to streamline your development and minimize common boilerplate code, and the robust testing framework to make testing easy, and dare we say, fun.
Expected prior knowledge / intended audience: developers and data flow managers should have passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The intended audience will have experience with programming in Groovy, Ruby, Jython, ECMAScript/Javascript, or Lua.
Takeaways: Attendees will gain an understanding in writing custom processors for Apache NiFi, including the component lifecycle, unit and integration testing, quick prototyping using a scripting language of their choice, and the artifact publishing and deployment process.
Introduction: This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
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.
Jeff Sposetti of Ambari discusses the Apache project Ambari used to help deploy and provision Hadoop Clusters
- Ambari Overview and the Community
- Ambari Architecture - Provisioning Clusters and Services -Standard Services: HDFS, YARN, MR2, Hive, new Services: Storm, Falcon
- Management and Monitoring Capabilities -Nagios and Ganglia Integration
- Key Innovation Features -Ambari Stacks providing dynamic service lifecycle -Ambari BluePrints powering Savannah OpenStack -Ambari Views enabling custom UI development
Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease.
Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos.
Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster.
Speaker
Jayush Luniya, Principal Software Engineer, Hortonworks
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
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/
This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
Pre-requisites: Registrants must bring a laptop that has the latest VirtualBox installed and an image for Hortonworks DataFlow (HDF) Sandbox will be provided.
Speaker: Andy LoPresto
Agenda:
1.Data Flow Challenges in an Enterprise
2.Introduction to Apache NiFi
3.Core Features
4.Architecture
5.Demo –Simple Lambda Architecture
6.Use Cases
7.Q & A
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
State of the Apache NiFi Ecosystem & CommunityAccumulo Summit
This talk will discuss the state of the Apache NiFi Ecosystem & Community.
Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems. It provides real-time control that makes it easy to manage the movement of data between any source and any destination. It is data source agnostic, supporting disparate and distributed sources of differing formats, schemas, protocols, speeds and sizes such as machines, geo location devices, click streams, files, social feeds, log files and videos and more. It is configurable plumbing for moving data around, similar to how Fedex, UPS or other courier delivery services move parcels around. And just like those services, Apache NiFi allows you to trace your data in real time, just like you could trace a delivery.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Curing the Kafka blindness—Streams Messaging ManagerDataWorks Summit
Companies who use Kafka today struggle with monitoring and managing Kafka clusters. Kafka is a key backbone of IoT streaming analytics applications. The challenge is understanding what is going on overall in the Kafka cluster including performance, issues and message flows. No open source tool caters to the needs of different users that work with Kafka: DevOps/developers, platform team, and security/governance teams. See how the new Hortonworks Streams Messaging Manager enables users to visualize their entire Kafka environment end-to-end and simplifies Kafka operations.
In this session learn how SMM visualizes the intricate details of how Apache Kafka functions in real time while simultaneously surfacing every nuance of tuning, optimizing, and measuring input and output. SMM will assist users to quickly understand and operate Kafka while providing the much-needed transparency that sophisticated and experienced users need to avoid all the pitfalls of running a Kafka cluster.
Future of Data New Jersey - HDF 3.0 Deep DiveAldrin Piri
Presentation on new features of HDF 3.0 presented on August 8, 2017 to the Future of Data: New Jersey Meetup group. This event was hosted by Honeywell in Morris Plains, NJ.
https://www.meetup.com/futureofdata-princeton/events/240972326/
Originally created for Hadoop Summit 2016: Melbourne.
http://www.hadoopsummit.org/melbourne/
Apache NiFi is becoming a defacto tool for handling orchestration, routing and mediation of data in the highly complex and heterogeneous world of Big Data, connecting many components (in-motion and at-rest) of its ecosystem into one homogenous and secure data flow. And while features such as security, provenance, dynamic prioritization and extensibility have long captured the attention of the enterprises, the innovation in NiFi land continues. This hands-on talk consisting of live demos and code will concentrate on what’s new an exciting in the world of NiFi. It will cover the newest and most advanced features of NiFi as well as demonstrate some of the "work in progress" essentially giving you a preview into the future.
Using Spark Streaming and NiFi for the Next Generation of ETL in the EnterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Connecting the Drops with Apache NiFi & Apache MiNiFiDataWorks Summit
Demand for increased capture of information to drive analytic insights into an organizations' assets and infrastructure is growing at unprecedented rates. However, as data volume growth soars, the ability to provide seamless ingestion pipelines becomes operationally complex as the magnitude of data sources and types expands.
This talk will focus on the efforts of the Apache NiFi community including subproject, MiNiFi; an agent based architecture and its relation to the core Apache NiFi project. MiNiFi is focused on providing a platform that meets and adapts to where data is born while providing the core tenets of NiFi in provenance, security, and command and control. These capabilities provide versatile avenues for the bi-directional exchange of information across data and control planes while dealing with the constraints of operation at opposite ends of the scale spectrum tackling the first and last miles of dataflow management.
We will highlight ongoing and new efforts in the community to provide greater flexibility with deployment and configuration management of flows. Versioned flows provide greater operational flexibility and serve as a powerful foundation to orchestrate the collection and transmission from the point of data's inception through to its transmission to consumers and processing systems.
The First Mile - Edge and IoT Data Collection With Apache Nifi and MiniFiDataWorks Summit
Apache NiFi MiNiFi enables data collection in a brand new environment - small sensor footprint, intermittent or limited bandwidth distributed system, and disposable or short-lived hardware. You can prioritize this data or perform initial analysis on the edge, as well as immediately encrypt and protect it.
Concept: Apache NiFi offers a revolutionary data flow management system and extensive integration of existing data production, consumption and analysis ecosystems, all of which are robust data delivery and a (data) logging infrastructure It is protected by. Learn about the additional project Apache MiNiFi, which extends the scope of NiFi's power to the maximum. MiNiFi is a lightweight application that can be placed on hardware that is one order of magnitude smaller than the existing standard data collection platform and is less powerful. As a JVM-enabled native agent MiNiFi enables data gathering in a brand new environment - small sensor footprint, intermittent or limited bandwidth distributed system, and disposable or short-lived hardware. You can prioritize this data or perform initial analysis on the edge, as well as immediately encrypt and protect it. Regional governance and regulatory policies are applied to geopolitical boundaries and comply with legal requirements. And all of this configuration can be done from the existing NiFi and central control using the stable data UI that the data flow administrator has already liked and trusted.
Required prior knowledge / targeted participants: Developers and data flow administrators need some knowledge of Apache NiFi as a platform for routing, conversion, and data delivery through the system (a brief overview is provided ). In this talk we will focus on extending data collection, routing, data history, and NiFi control functions, through IoT / edge integration via MiNiFi.
Key Points: Participants will learn about the opportunity to collect and capture data flows close to the source of data, "edge", such as IoT devices, vehicles, machines, etc. Participants prioritize, filter, protect, and manipulate this data in the initial data lifecycle and understand the potential for data visibility and performance improvement.
The First Mile -- Edge and IoT Data Collection with Apache NiFi and MiNiFiDataWorks Summit
Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
Big Data Day LA 2016/ Big Data Track - Building scalable enterprise data flow...Data Con LA
Connecting enterprise systems has always been a tough task. Modern IoT applications have exacerbated the issue by the need to integrate legacy systems with novel high velocity data streams. Various patterns like messaging, REST, etc. have been proposed, but they necessitate rearchitecting the integration layer which is extremely arduous. In this talk we will show you how to use Apache NiFi to solve your data integration, movement and ingestion problems. Next, we will examine how Apache NiFi can be used to construct durable, scalable and responsive IoT apps in conjunction with other stream processing and messaging frameworks.
Data Analytics is often described as one of the biggest challenges associated with big data, but even before that step can happen, data must be ingested and made available to enterprise users. That’s where Apache Kafka comes in.
Hortonworks Technical Workshop: Real Time Monitoring with Apache HadoopHortonworks
Real Time Monitoring requires a high scalable infrastructure of message bus, database, distributed event processing and scalable analytics engine. By bringing together leading open source projects of Apache Kafka, Apache HBase, Apache Storm and Apache Hive, the Hortonworks Data Platform offers a comprehensive Real Time Analysis platform. In this session, we will provide an in-depth overview all the key technology components and demonstrate a working solution for monitoring a fleet of trucks.
Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=0278dc8aa49a9991e1ce436c71f53d30
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
Forrester forecasts* that direct spending on the Internet of Things (IoT) will exceed $400 Billion by 2023. From manufacturing and utilities, to oil & gas and transportation, IoT improves visibility, reduces downtime, and creates opportunities for entirely new business models.
But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
https://hortonworks.com/webinar/iot-predictions-2019-beyond-data-heart-iot-strategy/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
Johns Hopkins - Using Hadoop to Secure Access Log EventsHortonworks
In this webinar, we talk with experts from Johns Hopkins as they share techniques and lessons learned in real-world Apache Hadoop implementation.
https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
Cybersecurity today is a big data problem. There’s a ton of data landing on you faster than you can load, let alone search it. In order to make sense of it, we need to act on data-in-motion, use both machine learning, and the most advanced pattern recognition system on the planet: your SOC analysts. Advanced visualization makes your analysts more efficient, helps them find the hidden gems, or bombs in masses of logs and packets.
https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Curing Kafka Blindness with Hortonworks Streams Messaging ManagerHortonworks
With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance.
Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
TIME SERIES: APPLYING ADVANCED ANALYTICS TO INDUSTRIAL PROCESS DATAHortonworks
Thanks to sensors and the Internet of Things, industrial processes now generate a sea of data. But are you plumbing its depths to find the insight it contains, or are you just drowning in it? Now, Hortonworks and Seeq team to bring advanced analytics and machine learning to time-series data from manufacturing and industrial processes.
Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
Trimble Transportation Enterprise is a leading provider of enterprise software to over 2,000 transportation and logistics companies. They have designed an architecture that leverages Hortonworks Big Data solutions and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships.
https://hortonworks.com/webinar/blockchain-with-machine-learning-powered-by-big-data-trimble-transportation-enterprise/
Delivering Real-Time Streaming Data for Healthcare Customers: ClearsenseHortonworks
For years, the healthcare industry has had problems of data scarcity and latency. Clearsense solved the problem by building an open-source Hortonworks Data Platform (HDP) solution while providing decades worth of clinical expertise. Clearsense is delivering smart, real-time streaming data, to its healthcare customers enabling mission-critical data to feed clinical decisions.
https://hortonworks.com/webinar/delivering-smart-real-time-streaming-data-healthcare-customers-clearsense/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It provides an end-to-end platform that can collect, curate, analyze, and act on data in real-time, on-premises, or in the cloud with a drag-and-drop visual interface. It’s being used across industries on large amounts of data that had stored in isolation which made collaboration and analysis difficult.
Join industry experts from Hortonworks and Attunity as they explain how Apache NiFi and streaming CDC technology provides a distributed, resilient platform for unlocking the value of data in new ways.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.