This document compares various streaming analytics technologies including Apache Storm, Apache Trident, Apache Flink, and Spark Streaming. It discusses key features needed in streaming applications such as fault tolerance, message processing guarantees, back pressure, and resource utilization. It then provides an overview of each technology, describing their architectures, programming models, support for features like state management, and ability to run on shared clusters. The document concludes with suggestions on how to benchmark Spark Streaming applications.
Agenda:
• Brief overview of Spark provided spark-shell, spark-submit
• Overview of Spark ContextOverview of Zeppelin and Jupyter notebooks for Spark
• Introduction to IBM Spark Kernel
• Introduction to Cloudera Livy and Spark JobServer
Github Link:
Previous meetups:-
1) Introduction to Resilient Distributed Dataset and deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-and-resilient-distributed-dataset-basics-and-deep-dive
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/225159947/
Video: https://www.youtube.com/watch?v=MkeRWyF1y_0
Github: https://github.com/SatyaNarayan1/spark_meetup
2) Introduction to Spark DataFrames/SQL and Deep dive
Slides: http://www.slideshare.net/sachinparmarss/deep-dive-spark-data-frames-sql-and-catalyst-optimizer
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/226419828/
Video: https://www.youtube.com/watch?v=h71MNWRv99M
Github: https://github.com/parmarsachin/spark-dataframe-demo
3) Apache Spark - Introduction to Spark Streaming and Deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-to-spark-streaming-and-deep-dive-57671774
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/227008581/
Video:
Github: https://github.com/agsachin/spark-meetup
Looking forward to have a great interactive session. Do provide feedback.
As Hadoop becomes the defacto big data platform, enterprises deploy HDP across wide range of physical and virtual environments spanning private and public clouds. This session will cover key considerations for cloud deployment and showcase Cloudbreak for simple and consistent deployment across cloud providers of choice.
Continus sql with sql stream builder
Eventador Cloudera
Flink SQL Kafka Apache NiFi SMM Schema Registry
Avro Json Apache Calcite. Meetup Future of Data New York
Kenny Gorman Tim Spann John Kuchmek
Processing data from social media streams and sensors in real-time is becoming increasingly prevalent and there are plenty open source solutions to choose from. To help practitioners decide what to use when we compare three popular Apache projects allowing to do stream processing: Apache Storm, Apache Spark and Apache Samza.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the event streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and also used to be called Complex Event Processing (CEP). In the last 3 years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Apache Samza as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Event and Stream Processing and present what differences you might find between the more traditional CEP and the more modern Stream Processing solutions and show that a combination will bring the most value.
Apache Storm vs. Spark Streaming – two Stream Processing Platforms comparedGuido Schmutz
Storm as well as Spark Streaming are Open-Source Frameworks supporting distributed stream processing. Storm has been developed by Twitter and is a free and open source distributed real-time computation system that can be used with any programming language. It is written primarily in Clojure and supports Java by default. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. This presentation shows how you can implement stream processing solutions with the two frameworks, discusses how they compare and highlights the differences and similarities.
Agenda:
• Brief overview of Spark provided spark-shell, spark-submit
• Overview of Spark ContextOverview of Zeppelin and Jupyter notebooks for Spark
• Introduction to IBM Spark Kernel
• Introduction to Cloudera Livy and Spark JobServer
Github Link:
Previous meetups:-
1) Introduction to Resilient Distributed Dataset and deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-and-resilient-distributed-dataset-basics-and-deep-dive
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/225159947/
Video: https://www.youtube.com/watch?v=MkeRWyF1y_0
Github: https://github.com/SatyaNarayan1/spark_meetup
2) Introduction to Spark DataFrames/SQL and Deep dive
Slides: http://www.slideshare.net/sachinparmarss/deep-dive-spark-data-frames-sql-and-catalyst-optimizer
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/226419828/
Video: https://www.youtube.com/watch?v=h71MNWRv99M
Github: https://github.com/parmarsachin/spark-dataframe-demo
3) Apache Spark - Introduction to Spark Streaming and Deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-to-spark-streaming-and-deep-dive-57671774
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/227008581/
Video:
Github: https://github.com/agsachin/spark-meetup
Looking forward to have a great interactive session. Do provide feedback.
As Hadoop becomes the defacto big data platform, enterprises deploy HDP across wide range of physical and virtual environments spanning private and public clouds. This session will cover key considerations for cloud deployment and showcase Cloudbreak for simple and consistent deployment across cloud providers of choice.
Continus sql with sql stream builder
Eventador Cloudera
Flink SQL Kafka Apache NiFi SMM Schema Registry
Avro Json Apache Calcite. Meetup Future of Data New York
Kenny Gorman Tim Spann John Kuchmek
Processing data from social media streams and sensors in real-time is becoming increasingly prevalent and there are plenty open source solutions to choose from. To help practitioners decide what to use when we compare three popular Apache projects allowing to do stream processing: Apache Storm, Apache Spark and Apache Samza.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the event streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and also used to be called Complex Event Processing (CEP). In the last 3 years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Apache Samza as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Event and Stream Processing and present what differences you might find between the more traditional CEP and the more modern Stream Processing solutions and show that a combination will bring the most value.
Apache Storm vs. Spark Streaming – two Stream Processing Platforms comparedGuido Schmutz
Storm as well as Spark Streaming are Open-Source Frameworks supporting distributed stream processing. Storm has been developed by Twitter and is a free and open source distributed real-time computation system that can be used with any programming language. It is written primarily in Clojure and supports Java by default. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. This presentation shows how you can implement stream processing solutions with the two frameworks, discusses how they compare and highlights the differences and similarities.
Apache Deep Learning 201 - Barcelona DWS March 2019Timothy Spann
Apache Deep Learning 201 - Barcelona DWS March 2019
The art of using Apache NiFi with Apache Tika, Apache OpenNLP, Apache Spark, Apache MXNet, Apache NiFi MiNiFi, Apache NiFi Registry, Apache Livy, Apache HBase, Apache Phoenix, Apache Hive and Apache YARN for deep learning workloads. Including Submarine.
Designing For Multicloud, CF Summit Frankfurt 2016Mark D'Cunha
Your carefully planned cloud strategy and technology architecture is useless, because multicloud changes everything. In this session, we will explore what multicloud means and why your business will force it upon you.
We provide examples of customers successfully using multicloud models, identify early patterns of usage and how to leverage them. You’ll learn about how Cloud Foundry provides unique capabilities to simplify and implement multicloud deployments. We’ll cover how you can use features like service brokers, service plans, asynchronous provisioning and arbitrary parameters to deploy muilticloud, while still maintaining a consistent experience for your application developers and IT operations staff.
Real-World Pulsar Architectural PatternsDevin Bost
This presentation covers Real-World Pulsar Architectural Patterns involving Distributed Caching and Distributed Tracing. We also cover the use of Apache Ignite, Jaeger, Apache Flink, and many other technologies, as well as industry best-practices.
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
Cloud Operations with Streaming Analytics using Apache NiFi and Apache FlinkDataWorks Summit
The amount of information coming from a Cloud deployment, that could be used to have a better situational awareness, and operate it efficiently is huge. Tools as the ones provided by Apache foundation can be used to build a solution to that challenge.
Nowadays Cloud deployments are pervasive in businesses, with scalability and multi tenancy as their core capabilities. This means that these deployments can grow easily beyond 1000 nodes and efficient operation of these huge clusters requires real time log analysis, metrics, events and configuration data. Performing correlation and finding patterns, not just to get to root causes but also to predict failures and reduce risk requires tools that go beyond current solutions.
In the prototype developed by Red Hat and KEEDIO (keedio.com), we managed to address the above challenges with the use of Big Data tools like Apache NiFi, Apache Kafka and Apache Flink, that enabled us to process the constant stream of syslog messages (RFC5424) produced by the Infrastructure as a Service, provided by OpenStack services, and also detect common failure patterns that could arise and generate alerts as needed.
This session is an (Intermediate) talk in our Apache Nifi and Data Science track. It focuses on Apache Flink, Apache Nifi, Apache Kafka and is geared towards Architect, Data Scientist, Data Analyst, Developer / Engineer audiences.
Speaker
Miguel Perez Colino, Senior Design Product Manager, Red Hat
Suneel Marthi, Senior Principal Engineer, Red Hat
In this webinar by Jonas Bonér, creator of Akka and CTO/Co-Founder of Lightbend, we take a look at Cloudstate, an OSS tool built on Akka, gRPC, Knative, GraalVM, and Kubernetes. Cloudstate lets you model, manage, and scale stateful services while preserving responsiveness by designing for resilience and elasticity.
A Journey to Reactive Function ProgrammingAhmed Soliman
A gentle introduction to functional reactive programming highlighting the reactive manifesto and ends with a demo in RxJS https://github.com/AhmedSoliman/rxjs-test-cat-scope
The Event Mesh: real-time, event-driven, responsive APIs and beyondSolace
Phil Scanlon, Head of Technology in Asia Pacific & Japan for Solace, describes "The Event Mesh" at API Days Melbourne in September 2018. Scanlon explains the complexities of the Event Mesh using the evolution to event-driven, the anatomy of an event, and real world examples.
AWS re:Invent 2016: Learn how IFTTT uses ElastiCache for Redis to predict eve...Amazon Web Services
IFTTT is a free service that empowers people to do more with the services they love, from automating simple tasks to transforming how someone interacts with and controls their home. IFTTT uses ElastiCache for Redis to store transaction run history and schedule predictions as well as indexes for log documents on S3. Join this session to learn how the scripting power of Lua and the data types of Redis allowed them to accomplish something they would not have been able to elsewhere.
Why Cloud-Native Kafka Matters: 4 Reasons to Stop Managing it YourselfDATAVERSITY
With your most talented teams bogged down managing a massive Kafka deployment, it can be challenging to move the dial on projects that drive real value for your business. For example, launching your next major feature, fueling more best-in-breed services like AI/ML on your cloud provider platform, or developing your first use cases for real-time data movement across clouds. By shifting to a fully managed, cloud-native service for Kafka you can unlock your teams to work on the projects that make the best use of your data in motion.
In this webinar you will learn about:
• The increasing value of data in motion to your business
• Challenges and costs of self-managing a large-scale Kafka deployment
• Benefits of managed cloud services for non-core activities like data storage, data warehousing, and messaging
• Optimizing time usage for value-generating activity like new product launches
• Potential cost savings for your business with a cloud-native service for Kafka
Ten reasons to choose Apache Pulsar over Apache Kafka for Event Sourcing_Robe...StreamNative
More and more developer want to build cloud-native distributed application or microservices by making use of high performing, cloud-agnostic messaging technology for maximum decoupling. The only thing we do not want is the hassle of managing the complex message infrasturcture needed for the job, or the risk of getting into a vendor lock-in. Generally developers know Apache Kafka, but for event sourcing or the CQRS pattern Kafka is not really suitable. In this talk I will give you at least ten reasons why to choose Pulsar over Kafka for event sourcing and data consensus.
AWS User Group Meetup Berlin - Kay Lerch on Apache NiFi (2016-04-19)Kay Lerch
My talk at yesterdays AWS Usergroup meetup in Berlin gave the audience an introduction to the concepts and features of Apache NiFi as well as to the capabilities of this product regarding integration of AWS IoT.
http://flink-forward.org/kb_sessions/flink-and-beam-current-state-roadmap/
It is no secret that the Dataflow model, which evolved from Google’s MapReduce, Flume, and MillWheel, has been a major influence to Apache Flink’s streaming API. The essentials of this model are captured in Apache Beam. Beam provides the Dataflow API with the option to deploy to various backends (e.g. Flink, Spark). In this talk we will examine the current state of the Flink Runner. Beam’s Runners manage the translation of the Beam API into the backend API. The Beam project itself has made an effort to summarize the capabilities of each Runner to provide an overview of the supported API concepts. From all open sources backends, Flink is currently the Runner which supports the most features. We will look at the supported Beam features and their counterpart in Flink. Further, we will look at potential improvements and upcoming features of the Flink Runner.
Event streaming: A paradigm shift in enterprise software architectureSina Sojoodi
This talk helps developers and architects understand the benefits, opportunities and challenges in moving from traditional point-to-point integration in application architecture to one with event streaming. Apache Kafka and Spring provide a solid foundation for enterprise and large organizations to implement event streaming solutions. Examples and common patterns are covered
towards the end.
Many thanks to James Watters and all the original content authors, editors and aggregators referenced in the slides.
IoT Sensor Analytics with Kafka, ksqlDB and TensorFlowKai Wähner
Use cases and architectures for IoT projects leveraging Apache Kafka, ksqlDB, machine Learning / deep Learning frameworks like TensorFlow, and cloud infrastructure.
Large numbers of IoT devices lead to big data and the need for further processing and analysis. Apache Kafka is a highly scalable and distributed open source streaming platform, which can connect to MQTT and other IoT standards. Kafka ingests, stores, processes and forwards high volumes of data from thousands of IoT devices.
The rapidly expanding world of stream processing can be daunting, with new concepts such as various types of time semantics, windowed aggregates, changelogs, and programming frameworks to master. KSQL is the streaming SQL engine on top of Apache Kafka which simplifies all this and make stream processing available to everyone without the need to write source code.
This talk shows how to leverage Kafka and KSQL in an IoT sensor analytics scenario for predictive maintenance and integration with real time monitoring systems. A live demo shows how to embed and deploy Machine Learning models - built with frameworks like TensorFlow, DeepLearning4J or H2O - into mission-critical and scalable real time applications.
Lessons from Building Large-Scale, Multi-Cloud, SaaS Software at DatabricksDatabricks
The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software
Real-Time Event & Stream Processing on MS AzureKhalid Salama
These slides discuss the main concepts of event & stream processing, as well as the related technologies on Microsoft Azure. We start by giving and overview of what Event & Stream Processing is. Then we describe the canonical architecture of a Stream Processing solution. We will delve into Message Queuing part of the solution. After that, we Introduce Apache Storm on HDInsight, as well as Azure Stream Analytics. We compare Apache Storm to Azure Stream Analytics, and finally conclude with useful resources
Apache Deep Learning 201 - Barcelona DWS March 2019Timothy Spann
Apache Deep Learning 201 - Barcelona DWS March 2019
The art of using Apache NiFi with Apache Tika, Apache OpenNLP, Apache Spark, Apache MXNet, Apache NiFi MiNiFi, Apache NiFi Registry, Apache Livy, Apache HBase, Apache Phoenix, Apache Hive and Apache YARN for deep learning workloads. Including Submarine.
Designing For Multicloud, CF Summit Frankfurt 2016Mark D'Cunha
Your carefully planned cloud strategy and technology architecture is useless, because multicloud changes everything. In this session, we will explore what multicloud means and why your business will force it upon you.
We provide examples of customers successfully using multicloud models, identify early patterns of usage and how to leverage them. You’ll learn about how Cloud Foundry provides unique capabilities to simplify and implement multicloud deployments. We’ll cover how you can use features like service brokers, service plans, asynchronous provisioning and arbitrary parameters to deploy muilticloud, while still maintaining a consistent experience for your application developers and IT operations staff.
Real-World Pulsar Architectural PatternsDevin Bost
This presentation covers Real-World Pulsar Architectural Patterns involving Distributed Caching and Distributed Tracing. We also cover the use of Apache Ignite, Jaeger, Apache Flink, and many other technologies, as well as industry best-practices.
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
Cloud Operations with Streaming Analytics using Apache NiFi and Apache FlinkDataWorks Summit
The amount of information coming from a Cloud deployment, that could be used to have a better situational awareness, and operate it efficiently is huge. Tools as the ones provided by Apache foundation can be used to build a solution to that challenge.
Nowadays Cloud deployments are pervasive in businesses, with scalability and multi tenancy as their core capabilities. This means that these deployments can grow easily beyond 1000 nodes and efficient operation of these huge clusters requires real time log analysis, metrics, events and configuration data. Performing correlation and finding patterns, not just to get to root causes but also to predict failures and reduce risk requires tools that go beyond current solutions.
In the prototype developed by Red Hat and KEEDIO (keedio.com), we managed to address the above challenges with the use of Big Data tools like Apache NiFi, Apache Kafka and Apache Flink, that enabled us to process the constant stream of syslog messages (RFC5424) produced by the Infrastructure as a Service, provided by OpenStack services, and also detect common failure patterns that could arise and generate alerts as needed.
This session is an (Intermediate) talk in our Apache Nifi and Data Science track. It focuses on Apache Flink, Apache Nifi, Apache Kafka and is geared towards Architect, Data Scientist, Data Analyst, Developer / Engineer audiences.
Speaker
Miguel Perez Colino, Senior Design Product Manager, Red Hat
Suneel Marthi, Senior Principal Engineer, Red Hat
In this webinar by Jonas Bonér, creator of Akka and CTO/Co-Founder of Lightbend, we take a look at Cloudstate, an OSS tool built on Akka, gRPC, Knative, GraalVM, and Kubernetes. Cloudstate lets you model, manage, and scale stateful services while preserving responsiveness by designing for resilience and elasticity.
A Journey to Reactive Function ProgrammingAhmed Soliman
A gentle introduction to functional reactive programming highlighting the reactive manifesto and ends with a demo in RxJS https://github.com/AhmedSoliman/rxjs-test-cat-scope
The Event Mesh: real-time, event-driven, responsive APIs and beyondSolace
Phil Scanlon, Head of Technology in Asia Pacific & Japan for Solace, describes "The Event Mesh" at API Days Melbourne in September 2018. Scanlon explains the complexities of the Event Mesh using the evolution to event-driven, the anatomy of an event, and real world examples.
AWS re:Invent 2016: Learn how IFTTT uses ElastiCache for Redis to predict eve...Amazon Web Services
IFTTT is a free service that empowers people to do more with the services they love, from automating simple tasks to transforming how someone interacts with and controls their home. IFTTT uses ElastiCache for Redis to store transaction run history and schedule predictions as well as indexes for log documents on S3. Join this session to learn how the scripting power of Lua and the data types of Redis allowed them to accomplish something they would not have been able to elsewhere.
Why Cloud-Native Kafka Matters: 4 Reasons to Stop Managing it YourselfDATAVERSITY
With your most talented teams bogged down managing a massive Kafka deployment, it can be challenging to move the dial on projects that drive real value for your business. For example, launching your next major feature, fueling more best-in-breed services like AI/ML on your cloud provider platform, or developing your first use cases for real-time data movement across clouds. By shifting to a fully managed, cloud-native service for Kafka you can unlock your teams to work on the projects that make the best use of your data in motion.
In this webinar you will learn about:
• The increasing value of data in motion to your business
• Challenges and costs of self-managing a large-scale Kafka deployment
• Benefits of managed cloud services for non-core activities like data storage, data warehousing, and messaging
• Optimizing time usage for value-generating activity like new product launches
• Potential cost savings for your business with a cloud-native service for Kafka
Ten reasons to choose Apache Pulsar over Apache Kafka for Event Sourcing_Robe...StreamNative
More and more developer want to build cloud-native distributed application or microservices by making use of high performing, cloud-agnostic messaging technology for maximum decoupling. The only thing we do not want is the hassle of managing the complex message infrasturcture needed for the job, or the risk of getting into a vendor lock-in. Generally developers know Apache Kafka, but for event sourcing or the CQRS pattern Kafka is not really suitable. In this talk I will give you at least ten reasons why to choose Pulsar over Kafka for event sourcing and data consensus.
AWS User Group Meetup Berlin - Kay Lerch on Apache NiFi (2016-04-19)Kay Lerch
My talk at yesterdays AWS Usergroup meetup in Berlin gave the audience an introduction to the concepts and features of Apache NiFi as well as to the capabilities of this product regarding integration of AWS IoT.
http://flink-forward.org/kb_sessions/flink-and-beam-current-state-roadmap/
It is no secret that the Dataflow model, which evolved from Google’s MapReduce, Flume, and MillWheel, has been a major influence to Apache Flink’s streaming API. The essentials of this model are captured in Apache Beam. Beam provides the Dataflow API with the option to deploy to various backends (e.g. Flink, Spark). In this talk we will examine the current state of the Flink Runner. Beam’s Runners manage the translation of the Beam API into the backend API. The Beam project itself has made an effort to summarize the capabilities of each Runner to provide an overview of the supported API concepts. From all open sources backends, Flink is currently the Runner which supports the most features. We will look at the supported Beam features and their counterpart in Flink. Further, we will look at potential improvements and upcoming features of the Flink Runner.
Event streaming: A paradigm shift in enterprise software architectureSina Sojoodi
This talk helps developers and architects understand the benefits, opportunities and challenges in moving from traditional point-to-point integration in application architecture to one with event streaming. Apache Kafka and Spring provide a solid foundation for enterprise and large organizations to implement event streaming solutions. Examples and common patterns are covered
towards the end.
Many thanks to James Watters and all the original content authors, editors and aggregators referenced in the slides.
IoT Sensor Analytics with Kafka, ksqlDB and TensorFlowKai Wähner
Use cases and architectures for IoT projects leveraging Apache Kafka, ksqlDB, machine Learning / deep Learning frameworks like TensorFlow, and cloud infrastructure.
Large numbers of IoT devices lead to big data and the need for further processing and analysis. Apache Kafka is a highly scalable and distributed open source streaming platform, which can connect to MQTT and other IoT standards. Kafka ingests, stores, processes and forwards high volumes of data from thousands of IoT devices.
The rapidly expanding world of stream processing can be daunting, with new concepts such as various types of time semantics, windowed aggregates, changelogs, and programming frameworks to master. KSQL is the streaming SQL engine on top of Apache Kafka which simplifies all this and make stream processing available to everyone without the need to write source code.
This talk shows how to leverage Kafka and KSQL in an IoT sensor analytics scenario for predictive maintenance and integration with real time monitoring systems. A live demo shows how to embed and deploy Machine Learning models - built with frameworks like TensorFlow, DeepLearning4J or H2O - into mission-critical and scalable real time applications.
Lessons from Building Large-Scale, Multi-Cloud, SaaS Software at DatabricksDatabricks
The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software
Real-Time Event & Stream Processing on MS AzureKhalid Salama
These slides discuss the main concepts of event & stream processing, as well as the related technologies on Microsoft Azure. We start by giving and overview of what Event & Stream Processing is. Then we describe the canonical architecture of a Stream Processing solution. We will delve into Message Queuing part of the solution. After that, we Introduce Apache Storm on HDInsight, as well as Azure Stream Analytics. We compare Apache Storm to Azure Stream Analytics, and finally conclude with useful resources
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...Data Con LA
While the last few years have seen great advancements in computing paradigms for big data stores, there remains one critical bottleneck in this architecture - the ingestion process. Instead of immediate insights into the data, a poor ingestion process can cause headaches and problems to no end. On the other hand, a well-designed ingestion infrastructure should give you real-time visibility into how your systems are functioning at any given time. This can significantly increase the overall effectiveness of your ad-campaigns, fraud-detection systems, preventive-maintenance systems, or other critical applications underpinning your business.
In this session we will explore various modes of ingest including pipelining, pub-sub, and micro-batching, and identify the use-cases where these can be applied. We will present this in the context of open source frameworks such as Apache Flume, Kafka, among others that can be used to build related solutions. We will also present when and how to use multiple modes and frameworks together to form hybrid solutions that can address non-trivial ingest requirements with little or no extra overhead. Through this discussion we will drill-down into details of configuration and sizing for these frameworks to ensure optimal operations and utilization for long-running deployments.
Developing Connected Applications with AWS IoT - Technical 301Amazon Web Services
AWS IoT is a managed cloud platform that can support billions of devices and trillions of messages, and can process and route those messages to AWS endpoints and to other devices reliably and securely.
In this session we look at patterns and architectures for developing connected applications using AWS IoT. We dive into demo applications that tie together physical IoT devices, web browsers, identity providers, and mobile devices to create smart, connected applications using Amazon Web Services.
Speaker: Adam Larter, Solutions Architect, Amazon Web Services
Featured Customer - Tekt Industries
Dean Wampler, O’Reilly author and Big Data Strategist in the office of the CTO at Lightbend discusses practical tips for architecting stream-processing applications and explains how you can tame some of the complexity in moving from data at rest to data in motion.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Getting started with Azure Event Hubs and Stream Analytics servicesVladimir Bychkov
The total amount of data in the world almost doubles every 2 years. Storing data for offline processing is no longer a viable business model. In the past few years, new technologies for real-time data processing emerged. Microsoft Azure offers a comprehensive set of tools to ingest and process data in motion. In this presentation we will go over and learn how to collect data from devices, how to process data in real time using Azure Stream Analytic jobs, and how to produce and handle actionable insights.
Landoop presenting how to simplify your ETL process using Kafka Connect for (E) and (L). Introducing KCQL - the Kafka Connect Query Language & how it can simplify fast-data (ingress & egress) pipelines. How KCQL can be used to set up Kafka Connectors for popular in-memory and analytical systems and live demos with HazelCast, Redis and InfluxDB. How to get started with a fast-data docker kafka development environment. Enhance your existing Cloudera (Hadoop) clusters with fast-data capabilities.
UX, ethnography and possibilities: for Libraries, Museums and ArchivesNed Potter
These slides are adapted from a talk I gave at the Welsh Government's Marketing Awards for the LAM sector, in 2017.
It offers a primer on UX - User Experience - and how ethnography and design might be used in the library, archive and museum worlds to better understand our users. All good marketing starts with audience insight.
The presentation covers the following:
1) An introduction to UX
2) Ethnography, with definitions and examples of 7 ethnographic techniques
3) User-centred design and Design Thinking
4) Examples of UX-led changes made at institutions in the UK and Scandinavia
5) Next Steps - if you'd like to try out UX at your own organisation
Apache Kafka is a distributed streaming platform that forms a key part of the infrastructure at many companies including Uber, Netflix and LinkedIn. In this talk, Matt gave a technical overview of Apache Kafka, discussed practical use cases of Kafka for IoT data and demonstrated how to ingest data from an MQTT server using Kafka Connect.
The technologies and people we are designing experiences for are constantly changing, in most cases they are changing at a rate that is difficult keep up with. When we think about how our teams are structured and the design processes we use in light of this challenge, a new design problem (or problem space) emerges, one that requires us to focus inward. How do we structure our teams and processes to be resilient? What would happen if we looked at our teams and design process as IA’s, Designers, Researchers? What strategies would we put in place to help them be successful? This talk will look at challenges we face leading, supporting, or simply being a part of design teams creating experiences for user groups with changing technological needs.
Explore IoT in Big Data while brewing beer. All verticals are instrumenting devices to learn more about their process to help cut costs or improve efficiency.
Five Cool Use Cases for the Spring Component of the SOA Suite 11gGuido Schmutz
Both Oracle SOA Suite and Oracle Unified Business Process Management Suite make it possible to embed Java code as a Service Component Architecture (SCA) first-class citizen through the Spring component implementation type. Thereby the coarse-grained components of Oracle SOA Suite are extended by the much-finer-grained Spring beans wrapped inside the Spring component. This session presents five cool use cases for the Spring component. It shows how and why you would want to use the Spring component and will hopefully inspire attendees to use it for their own projects.
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...Prolifics
Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.
Just like you can't defeat the laws of physics there are natural laws that ultimately decide software performance. Even the latest technology beta is still bound by Newton's laws, and you can't change the speed of light, even in the cloud!
Five cool ways the JVM can run Apache Spark fasterTim Ellison
The IBM JVM runs Apache Spark fast! This talk explains some of the findings and optimizations from our experience of running Spark workloads.
The talk was originally presented at the SparkEU Summit 2015 in Amsterdam.
Presentation from OpenStack Summit Tokyo
Also check the video from
https://www.openstack.org/summit/tokyo-2015/videos/presentation/ntts-journey-with-openstack
Azure + DataStax Enterprise (DSE) Powers Office365 Per User StoreDataStax Academy
We will present our Office 365 use case scenarios, why we chose Cassandra + Spark, and walk through the architecture we chose for running DSE on Azure.
The presentation will feature demos on how you too can build similar applications.
Real World Problem Solving Using Application Performance Management 10CA Technologies
CA Application Performance Management 10 dramatically reduces the time needed to find and solve app problems. In this session you will learn about common problem-solving techniques used by experts to solve real-world app problems. You will get a chance to put these techniques to the test in a hands-on lab that mimics an interesting application performance problem.
For more information, please visit http://cainc.to/Nv2VOe
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Apache Storm vs. Spark Streaming - two stream processing platforms comparedGuido Schmutz
Storm as well as Spark Streaming are Open-Source Frameworks supporting distributed stream processing. Storm has been developed by Twitter and is a free and open source distributed real-time computation system that can be used with any programming language. It is written primarily in Clojure and supports Java by default. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. This presentation shows how you can implement stream processing solutions with the two frameworks, discusses how they compare and highlights the differences and similarities.
How to Improve Performance Testing Using InfluxDB and Apache JMeterInfluxData
Apache JMeter is a useful way to run performance tests across different servers. In order to monitor these results, SAP chose to integrate JMeter with InfluxDB, their time series database, to collect and store the temporary transactions. They use Grafana to visualize real-time performance metrics. What happens if your database goes down – for any reason? It could be because of too many JMeter threads trying to access the database or because Grafana is trying to access too many cores of transactions during a performance test. Discover how SAP improves their performance monitoring team’s productivity.
In this webinar, Subhodeep Ganguly will cover:
SAP’s approach to recovering transactions due to database failure
How JMeter execution threads will store the data in a temporary flat/CSV file compatible with InfluxDB
Their ability to reduce recovery times and to improve automatic performance testing
Usage of influx-replay tool as a plugin or compact jar file during the execution of an end-to-end performance test
Continuent Tungsten - Scalable Saa S Data Managementguest2e11e8
The key needs of SaaS vendors include:
i) managing multi-tenant architectures with shared DBMS, ii) maintaining customer SLAs for uptime and performance and iii) optimized, efficient operations.
The key benefits Continuent Tungsten offers SaaS vendors are:
i) high availability and protection from data loss, ii) simple, efficient cluster management and iii) enable complex database topologies.
Tungsten offers high-availability, database cluster management and management of complex topologies for multi-tenant architectures.
Tungsten high availability and data protection features include maintaining live copies with data consistency checking and tightly coupled backup/restore integration with cluster management tools.
Tungsten cluster management allows SaaS vendors to migrate customers and perform system upgrades without downtime, thus enabling these maintenance operations during normal business hours.
Tungsten also enables complex replication topologies, including data filtering and data archiving strategies, maintaining extra data copies for data-marts, routing different customers to different DBMS copies, and providing cross-site multi-master replication.
Flink Forward San Francisco 2019: Towards Flink 2.0: Rethinking the stack and...Flink Forward
Flink currently features different APIs for bounded/batch (DataSet) and streaming (DataStream) programs. And while the DataStream API can handle batch use cases, it is much less efficient in that compared to the DataSet API. The Table API was built as a unified API on top of both, to cover batch and streaming with the same API, and under the hood delegate to either DataSet or DataStream.
In this talk, we present the latest on the Flink community's efforts to rework the APIs and the stack for better unified batch & streaming experience. We will discuss:
- The future roles and interplay of DataSet, DataStream, and Table API
- The new Flink stack and the abstractions on which these APIs will build
- The new unified batch/streaming sources
- How batch and streaming optimizations differ in the runtime, and what the future interplay of batch and streaming execution could look like
Similar to Comparison of various streaming technologies (20)
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Flink uses effectively distributed blocking queues with bounded capacity
The output side never puts too much data on the wire by a simple watermark mechanism. If enough data is in-flight, we wait before we copy more data to the wire until it is below a threshold. This guarantees that there is never too much data in-flight. If new data is not consumed on the receiving side (because there is no buffer available), this slows down the sender.
http://data-artisans.com/how-flink-handles-backpressure/