Apache Flink(tm) - A Next-Generation Stream ProcessorAljoscha Krettek
In diesem Vortrag wird es zunächst einen kurzen Überblick über den aktuellen Stand im Bereich der Streaming-Datenanalyse geben. Danach wird es mit einer kleinen Einführung in das Apache-Flink-System zur Echtzeit-Datenanalyse weitergehen, bevor wir tiefer in einige der interessanten Eigenschaften eintauchen werden, die Flink von den anderen Spielern in diesem Bereich unterscheidet. Dazu werden wir beispielhafte Anwendungsfälle betrachten, die entweder direkt von Nutzern stammen oder auf unserer Erfahrung mit Nutzern basieren. Spezielle Eigenschaften, die wir betrachten werden, sind beispielsweise die Unterstützung für die Zerlegung von Events in einzelnen Sessions basierend auf der Zeit, zu der ein Ereignis passierte (event-time), Bestimmung von Zeitpunkten zum jeweiligen Speichern des Zustands eines Streaming-Programms für spätere Neustarts, die effiziente Abwicklung bei sehr großen zustandsorientierten Streaming-Berechnungen und die Zugänglichkeit des Zustandes von außerhalb.
Overview of Apache Flink: Next-Gen Big Data Analytics FrameworkSlim Baltagi
These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing.
In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?
Apache Flink 101 - the rise of stream processing and beyondBowen Li
Apache Flink is the most popular and widely adopted streaming processing framework, powering real time stream event computations at extremely large scale in companies like Uber, Lyft, AWS, Alibaba, Pinterest, Splunk, Yelp, etc.
In this talk, we will go over use cases and basic (yet hard to achieve!) requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.
We will also take a look at how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning (especially online ML), and serverless computation, and how Flink fits with its distinct value.
SPEAKER: Bowen Li
SPEAKER BIO: Bowen is a committer of Apache Flink, senior engineer at Alibaba, and host of Seattle Flink Meetup.
This talk is an application-driven walkthrough to modern stream processing, exemplified by Apache Flink, and how this enables new applications and makes old applications easier and more efficient. In this talk, we will walk through several real-world stream processing application scenarios of Apache Flink, highlighting unique features in Flink that make these applications possible. In particular, we will see (1) how support for handling out of order streams enables real-time monitoring of cloud infrastructure, (2) how the ability handle high-volume data streams with low latency SLAs enables real-time alerts in network equipment, (3) how the combination of high throughput and the ability to handle batch as a special case of streaming enables an architecture where the same exact program is used for real-time and historical data processing, and (4) how stateful stream processing can enable an architecture that eliminates the need for an external database store, leading to more than 100x performance speedup, among many other benefits.
Introduction to Stream Processing with Apache Flink (2019-11-02 Bengaluru Mee...Timo Walther
Apache Flink is a distributed, stateful stream processor. It features exactly-once state consistency, sophisticated event-time support, high throughput and low latency processing, and APIs at different levels of abstraction (Java, Scala, SQL). In my talk, I'll give an introduction to Apache Flink, its features and discuss the use cases it solves. I'll explain why batch is just a special case of stream processing, how its community evolves Flink into a truly unified stream and batch processor and what this means for its users.
https://www.meetup.com/de-DE/Bangalore-Apache-Kafka-Group/events/265285812/
https://www.youtube.com/watch?v=Ych5bbmDIoA&list=PLvkUPePDi9sa27SG9eGNXH25cfUeo_WY9&index=2
Apache Flink(tm) - A Next-Generation Stream ProcessorAljoscha Krettek
In diesem Vortrag wird es zunächst einen kurzen Überblick über den aktuellen Stand im Bereich der Streaming-Datenanalyse geben. Danach wird es mit einer kleinen Einführung in das Apache-Flink-System zur Echtzeit-Datenanalyse weitergehen, bevor wir tiefer in einige der interessanten Eigenschaften eintauchen werden, die Flink von den anderen Spielern in diesem Bereich unterscheidet. Dazu werden wir beispielhafte Anwendungsfälle betrachten, die entweder direkt von Nutzern stammen oder auf unserer Erfahrung mit Nutzern basieren. Spezielle Eigenschaften, die wir betrachten werden, sind beispielsweise die Unterstützung für die Zerlegung von Events in einzelnen Sessions basierend auf der Zeit, zu der ein Ereignis passierte (event-time), Bestimmung von Zeitpunkten zum jeweiligen Speichern des Zustands eines Streaming-Programms für spätere Neustarts, die effiziente Abwicklung bei sehr großen zustandsorientierten Streaming-Berechnungen und die Zugänglichkeit des Zustandes von außerhalb.
Overview of Apache Flink: Next-Gen Big Data Analytics FrameworkSlim Baltagi
These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing.
In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?
Apache Flink 101 - the rise of stream processing and beyondBowen Li
Apache Flink is the most popular and widely adopted streaming processing framework, powering real time stream event computations at extremely large scale in companies like Uber, Lyft, AWS, Alibaba, Pinterest, Splunk, Yelp, etc.
In this talk, we will go over use cases and basic (yet hard to achieve!) requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.
We will also take a look at how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning (especially online ML), and serverless computation, and how Flink fits with its distinct value.
SPEAKER: Bowen Li
SPEAKER BIO: Bowen is a committer of Apache Flink, senior engineer at Alibaba, and host of Seattle Flink Meetup.
This talk is an application-driven walkthrough to modern stream processing, exemplified by Apache Flink, and how this enables new applications and makes old applications easier and more efficient. In this talk, we will walk through several real-world stream processing application scenarios of Apache Flink, highlighting unique features in Flink that make these applications possible. In particular, we will see (1) how support for handling out of order streams enables real-time monitoring of cloud infrastructure, (2) how the ability handle high-volume data streams with low latency SLAs enables real-time alerts in network equipment, (3) how the combination of high throughput and the ability to handle batch as a special case of streaming enables an architecture where the same exact program is used for real-time and historical data processing, and (4) how stateful stream processing can enable an architecture that eliminates the need for an external database store, leading to more than 100x performance speedup, among many other benefits.
Introduction to Stream Processing with Apache Flink (2019-11-02 Bengaluru Mee...Timo Walther
Apache Flink is a distributed, stateful stream processor. It features exactly-once state consistency, sophisticated event-time support, high throughput and low latency processing, and APIs at different levels of abstraction (Java, Scala, SQL). In my talk, I'll give an introduction to Apache Flink, its features and discuss the use cases it solves. I'll explain why batch is just a special case of stream processing, how its community evolves Flink into a truly unified stream and batch processor and what this means for its users.
https://www.meetup.com/de-DE/Bangalore-Apache-Kafka-Group/events/265285812/
https://www.youtube.com/watch?v=Ych5bbmDIoA&list=PLvkUPePDi9sa27SG9eGNXH25cfUeo_WY9&index=2
More complex streaming applications generally need to store some state of the running computations in a fault-tolerant manner. This talk discusses the concept of operator state and compares state management in current stream processing frameworks such as Apache Flink Streaming, Apache Spark Streaming, Apache Storm and Apache Samza.
We will go over the recent changes in Flink streaming that introduce a unique set of tools to manage state in a scalable, fault-tolerant way backed by a lightweight asynchronous checkpointing algorithm.
Talk presented in the Apache Flink Bay Area Meetup group on 08/26/15
These are the slides that supported the presentation on Apache Flink at the ApacheCon Budapest.
Apache Flink is a platform for efficient, distributed, general-purpose data processing.
Flink Streaming is the real-time data processing framework of Apache Flink. Flink streaming provides high level functional apis in Scala and Java backed by a high performance true-streaming runtime.
As many industries, banking is undergoing a fundamental change because of the software revolution. No longer are banks competing only on interest rates and having the best traders, these days customer experience and having the best engineers are the focus. In this changing world, banks compete with new start-ups, the so-called Fintechs, and with large platform organisations such as Google, Facebook and Apple. At ING, we believe that staying ahead of the game means changing how we interact with our customers, no longer a traditional model of waiting for the customers to come to the bank through our website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this. This presentation addresses how ING approaches the challenge, the role that Apache Flink plays, and the consequences regulations have on how we work with Open Source in general, and with Apache Flink (and data Artisans) in particular. This keynote takes place at Kino 3.
Beginning with MapReduce and its first popular open-source implementation in Apache Hadoop the data processing landscape has evolved quite a bit. Since then we have seen several paradigm shifts and open-source systems evolved to support new types of applications and to attract new audiences. We will follow developments using the example of the open-source stream processing system Apache Flink and in the end we will see how expressive APIs, support for event-driven applications, Flink SQL for seamless batch and stream processing, and a powerful runtime enable a wide range of applications.
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.
Javier Lopez_Mihail Vieru - Flink in Zalando's World of Microservices - Flink...Flink Forward
http://flink-forward.org/kb_sessions/flink-in-zalandos-world-of-microservices/
In this talk we present Zalando’s microservices architecture, introduce Saiki – our next generation data integration and distribution platform on AWS and show how we employ stream processing with Apache Flink for near-real time business intelligence.
Zalando is one of the largest online fashion retailers in Europe. In order to secure our future growth and remain competitive in this dynamic market, we are transitioning from a monolithic to a microservices architecture and from a hierarchical to an agile organization.
We first have a look at how business intelligence processes have been working inside Zalando for the last years and present our current approach – Saiki. It is a scalable, cloud-based data integration and distribution infrastructure that makes data from our many microservices readily available for analytical teams.
We no longer live in a world of static data sets, but are instead confronted with endless streams of events that constantly inform us about relevant happenings from all over the enterprise. The processing of these event streams enables us to do near-real time business intelligence. In this context we have evaluated Apache Flink vs. Apache Spark in order to choose the right stream processing framework. Given our requirements, we decided to use Flink as part of our technology stack, alongside with Kafka and Elasticsearch.
With these technologies we are currently working on two use cases: a near real-time business process monitoring solution and streaming ETL.
Monitoring our business processes enables us to check if technically the Zalando platform works. It also helps us analyze data streams on the fly, e.g. order velocities, delivery velocities and to control service level agreements.
On the other hand, streaming ETL is used to relinquish resources from our relational data warehouse, as it struggles with increasingly high loads. In addition to that, it also reduces the latency and facilitates the platform scalability.
Finally, we have an outlook on our future use cases, e.g. near-real time sales and price monitoring. Another aspect to be addressed is to lower the entry barrier of stream processing for our colleagues coming from a relational database background.
Scaling stream data pipelines with Pravega and Apache FlinkTill Rohrmann
Extracting insights out of continuously generated data requires a stream processor with powerful data analytics features such as Apache Flink. A stream data pipeline with Flink typically includes a storage component to ingest and serve the data. Pravega is a stream store that ingests and stores stream data permanently, making the data available for tail, catch-up, and historical reads. One important challenge for such stream data pipelines is coping with the variations in the workload. Daily cycles and seasonal spikes might require the provisioning of the application to adapt accordingly. Pravega has a feature called stream scaling, which enables the capacity offered for the ingestion of events of a stream to grow and shrink over time according to workload. Such a feature is useful when the application downstream has the ability of accommodating such changes and also scale its provisioning accordingly. In this presentation, we introduce stream scaling in Pravega and how Flink jobs leverage this feature to rescale stateful jobs according to variations in the workload.
Data Policies for the Kafka-API with WebAssembly | Alexander Gallego, VectorizedHostedbyConfluent
Enforcing format, changing schema, introducing privacy filters have always been a challenge with the classical Kafka-API. In this talk we'll cover how to extend existing applications with webassembly, allowing developers to change the shape of data at runtime, per application without creating additional topics. By leveraging WebAssembly, we can extend the capabilities of the Kafka-API beyond what it was initially imagined. Come and learn about the future of the Kafka-API
More complex streaming applications generally need to store some state of the running computations in a fault-tolerant manner. This talk discusses the concept of operator state and compares state management in current stream processing frameworks such as Apache Flink Streaming, Apache Spark Streaming, Apache Storm and Apache Samza.
We will go over the recent changes in Flink streaming that introduce a unique set of tools to manage state in a scalable, fault-tolerant way backed by a lightweight asynchronous checkpointing algorithm.
Talk presented in the Apache Flink Bay Area Meetup group on 08/26/15
These are the slides that supported the presentation on Apache Flink at the ApacheCon Budapest.
Apache Flink is a platform for efficient, distributed, general-purpose data processing.
Flink Streaming is the real-time data processing framework of Apache Flink. Flink streaming provides high level functional apis in Scala and Java backed by a high performance true-streaming runtime.
As many industries, banking is undergoing a fundamental change because of the software revolution. No longer are banks competing only on interest rates and having the best traders, these days customer experience and having the best engineers are the focus. In this changing world, banks compete with new start-ups, the so-called Fintechs, and with large platform organisations such as Google, Facebook and Apple. At ING, we believe that staying ahead of the game means changing how we interact with our customers, no longer a traditional model of waiting for the customers to come to the bank through our website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this. This presentation addresses how ING approaches the challenge, the role that Apache Flink plays, and the consequences regulations have on how we work with Open Source in general, and with Apache Flink (and data Artisans) in particular. This keynote takes place at Kino 3.
Beginning with MapReduce and its first popular open-source implementation in Apache Hadoop the data processing landscape has evolved quite a bit. Since then we have seen several paradigm shifts and open-source systems evolved to support new types of applications and to attract new audiences. We will follow developments using the example of the open-source stream processing system Apache Flink and in the end we will see how expressive APIs, support for event-driven applications, Flink SQL for seamless batch and stream processing, and a powerful runtime enable a wide range of applications.
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.
Javier Lopez_Mihail Vieru - Flink in Zalando's World of Microservices - Flink...Flink Forward
http://flink-forward.org/kb_sessions/flink-in-zalandos-world-of-microservices/
In this talk we present Zalando’s microservices architecture, introduce Saiki – our next generation data integration and distribution platform on AWS and show how we employ stream processing with Apache Flink for near-real time business intelligence.
Zalando is one of the largest online fashion retailers in Europe. In order to secure our future growth and remain competitive in this dynamic market, we are transitioning from a monolithic to a microservices architecture and from a hierarchical to an agile organization.
We first have a look at how business intelligence processes have been working inside Zalando for the last years and present our current approach – Saiki. It is a scalable, cloud-based data integration and distribution infrastructure that makes data from our many microservices readily available for analytical teams.
We no longer live in a world of static data sets, but are instead confronted with endless streams of events that constantly inform us about relevant happenings from all over the enterprise. The processing of these event streams enables us to do near-real time business intelligence. In this context we have evaluated Apache Flink vs. Apache Spark in order to choose the right stream processing framework. Given our requirements, we decided to use Flink as part of our technology stack, alongside with Kafka and Elasticsearch.
With these technologies we are currently working on two use cases: a near real-time business process monitoring solution and streaming ETL.
Monitoring our business processes enables us to check if technically the Zalando platform works. It also helps us analyze data streams on the fly, e.g. order velocities, delivery velocities and to control service level agreements.
On the other hand, streaming ETL is used to relinquish resources from our relational data warehouse, as it struggles with increasingly high loads. In addition to that, it also reduces the latency and facilitates the platform scalability.
Finally, we have an outlook on our future use cases, e.g. near-real time sales and price monitoring. Another aspect to be addressed is to lower the entry barrier of stream processing for our colleagues coming from a relational database background.
Scaling stream data pipelines with Pravega and Apache FlinkTill Rohrmann
Extracting insights out of continuously generated data requires a stream processor with powerful data analytics features such as Apache Flink. A stream data pipeline with Flink typically includes a storage component to ingest and serve the data. Pravega is a stream store that ingests and stores stream data permanently, making the data available for tail, catch-up, and historical reads. One important challenge for such stream data pipelines is coping with the variations in the workload. Daily cycles and seasonal spikes might require the provisioning of the application to adapt accordingly. Pravega has a feature called stream scaling, which enables the capacity offered for the ingestion of events of a stream to grow and shrink over time according to workload. Such a feature is useful when the application downstream has the ability of accommodating such changes and also scale its provisioning accordingly. In this presentation, we introduce stream scaling in Pravega and how Flink jobs leverage this feature to rescale stateful jobs according to variations in the workload.
Data Policies for the Kafka-API with WebAssembly | Alexander Gallego, VectorizedHostedbyConfluent
Enforcing format, changing schema, introducing privacy filters have always been a challenge with the classical Kafka-API. In this talk we'll cover how to extend existing applications with webassembly, allowing developers to change the shape of data at runtime, per application without creating additional topics. By leveraging WebAssembly, we can extend the capabilities of the Kafka-API beyond what it was initially imagined. Come and learn about the future of the Kafka-API
Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.
Streaming Analytics & CEP - Two sides of the same coin?Till Rohrmann
Talk I gave together with Fabian Hueske at the Berlin Buzzwords 2016 conference.
The talk demonstrates how we can combine streaming analytics and complex event processing (CEP) on the same execution engine, namely Apache Flink. This combination allows to open up a new field of applications where we can easily combine aggregations with temporal pattern detection.
Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Ser...Kai Wähner
Streaming Analytics Comparison of Open Source Frameworks, Products and Cloud Services. Includes Apache Storm, Flink, Spark, TIBCO, IBM, AWS Kinesis, Striim, Zoomdata, ...
This session discusses the technical concepts of stream processing / streaming analytics and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The focus of the session lies on comparing
- different open source frameworks such as Apache Apex, Apache Flink or Apache Spark Streaming
- engines from software vendors such as IBM InfoSphere Streams, TIBCO StreamBase
- cloud offerings such as AWS Kinesis.
- real time streaming UIs such as Striim, Zoomdata or TIBCO Live Datamart.
Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Apache Hadoop frameworks (such as MapReduce, Hive, Pig or Impala) and machine learning (such as R, Spark ML or H2O.ai).
Streaming Analytics - Comparison of Open Source Frameworks and ProductsKai Wähner
Stream Processing is a concept used to create a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Benefits, amongst others, are faster processing and reaction to real-time complex event streams and the flexibility to quickly adapt to changing business and analytic needs. Big data, cloud, mobile and internet of things are the major drivers for stream processing and streaming analytics.
This session discusses the technical concepts of stream processing and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The audience will understand when to use open source frameworks such as Apache Storm, Apache Spark or Esper, and powerful engines from software vendors such as IBM InfoSphere Streams or TIBCO StreamBase. Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Hadoop and statistical analysis with software such as SAS, Apache Spark’s MLlib or R language.
Apache Fink 1.0: A New Era for Real-World Streaming AnalyticsSlim Baltagi
These are the slides of my talk at the Chicago Apache Flink Meetup on April 19, 2016. This talk explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation, marks a new era of Real-Time and Real-World streaming analytics. The talk will map Flink's capabilities to streaming analytics use cases.
Apache Flink Crash Course by Slim Baltagi and Srini PalthepuSlim Baltagi
In this hands-on Apache Flink presentation, you will learn in a step-by-step tutorial style about:
• How to setup and configure your Apache Flink environment: Local/VM image (on a single machine), cluster (standalone), YARN, cloud (Google Compute Engine, Amazon EMR, ... )?
• How to get familiar with Flink tools (Command-Line Interface, Web Client, JobManager Web Interface, Interactive Scala Shell, Zeppelin notebook)?
• How to run some Apache Flink example programs?
• How to get familiar with Flink's APIs and libraries?
• How to write your Apache Flink code in the IDE (IntelliJ IDEA or Eclipse)?
• How to test and debug your Apache Flink code?
• How to deploy your Apache Flink code in local, in a cluster or in the cloud?
• How to tune your Apache Flink application (CPU, Memory, I/O)?
This talk given at the Hadoop Summit in San Jose on June 28, 2016, analyzes a few major trends in Big Data analytics.
These are a few takeaways from this talk:
- Adopt Apache Beam for easier development and portability between Big Data Execution Engines.
- Adopt stream analytics for faster time to insight, competitive advantages and operational efficiency.
- Accelerate your Big Data applications with In-Memory open source tools.
- Adopt Rapid Application Development of Big Data applications: APIs, Notebooks, GUIs, Microservices…
- Have Machine Learning part of your strategy or passively watch your industry completely transformed!
- How to advance your strategy for hybrid integration between cloud and on-premise deployments?
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications.
In this talk, you learn more about:
1. What is Apache Flink Stack?
2. Batch vs. Streaming Analytics
3. Key Differentiators of Apache Flink for Streaming Analytics
4. Real-World Use Cases with Flink for Streaming Analytics
5. Who is using Flink?
6. Where do you go from here?
Big Data pipeline with Scala by Rohit Rai, Tuplejump - presented at Pune Scal...Thoughtworks
At Tuplejump we have a built a big data platform powered by Scala everywhere. Using Akka for message/event processing, spark for streaming and batch processing, Shark for adhoc querying and Play to power our web based IDE. This talk will walk through what various components of the platform and how Scala, the concepts like Reactive programming, event driven architecture and ecosystem components like Akka actors framework, Spark RDDs and Play Web framework guided and inspired our decisions and also provided the kickstart to attempt this huge challenge of building a complete integrated end-to-end Big Data Application Framework.
Continuous Intelligence - Intersecting Event-Based Business Logic and MLParis Carbone
Modern data-driven business infrastructure is not as effective as it should be when it comes to critical decision making. Eng-to-End Data pipelines are composed out of fundamentally diverse pieces of tech, each focusing on a specific frontend (e.g., DataFrames, Tensors, Streams) and running in total isolation, thus, being highly unoptimised and complex to integrate with event-based business logic. Our research group has been looking into ways we can use advanced systems theory to compile, optimise and execute distributed functions in unison across the whole spectrum of data-driven programming, leading to a unified way to combine analytics and services all the way down to hardware execution and make continuous intelligence a reality.
Key Takeaways
Introducing the concept of Continuous Intelligence and why we are not there yet.
Pinpointing weaknesses in the current way we structure data-driven pipelines today
Explaining the potential of an Intermediate Representation (IR) and Shared Hardware Execution support to solve the problem.
Presenting our vision on how this new tech can be used to radically change the way we declare and distill knowledge from data in a fast-changing world.
Uses the example of correct, high-througput, grouping and counting of streaming events as a backdrop for exploring the state-of-the art features of Apache Flink
Data Stream Analytics - Why they are importantParis Carbone
Streaming is cool and it can help us do quick analytics and make profit but what about tsunamis? This is a motivation talk presented at the SeRC Big Data Workshop in Sweden during spring 2016. It motivates the streaming paradigm and provides examples on Apache Flink.
Data Stream Processing with Apache FlinkFabian Hueske
This talk is an introduction into Stream Processing with Apache Flink. I gave this talk at the Madrid Apache Flink Meetup at February 25th, 2016.
The talk discusses Flink's features, shows it's DataStream API and explains the benefits of Event-time stream processing. It gives an outlook on some features that will be added after the 1.0 release.
This presentation held in at Inovex GmbH in Munich in November 2015 was about a general introduction of the streaming space, an overview of Flink and use cases of production users as presented at Flink Forward.
Flink Forward Berlin 2017: Stephan Ewen - The State of Flink and how to adopt...Flink Forward
Data stream processing has redefined how many of us build data pipelines. Apache Flink is one of the systems at the forefront of that development: With its versatile APIs (event-time streaming, Stream SQL, events/state) and powerful execution model, Flink has been part of re-defining what stream processing can do. By now, Apache Flink powers some of the largest data stream processing pipelines in open source data stream processing. In this keynote, we will look at the evolution of Stream Processing and Apache Flink during the last year, and what we believe will be the next wave of stream processing applications. We show how the Flink community and users evolved, what use cases are coming up, and how new and upcoming features in Flink are making new types of applications possible. We will also discuss common challenges that companies are facing when adopting stream processing, and how we can help companies to rapidly adopt and roll out stream processing company-wide.
Flink Forward SF 2017: Stephan Ewen - Convergence of real-time analytics and ...Flink Forward
Witnessing the rise of stream processing from the driving seat, we see Apache Flink® and associated technologies used for a wide variety of business applications, from routing data through systems, serving as a backbone for real-time analytics on live data using SQL, detecting credit card fraud, to implementing complete end-to-end social networks. Such applications enable modern data-driven businesses where decisions and actions happen in real-time, and transform traditional businesses to become more data-driven. Observing the variety of these applications implemented using Flink, it becomes apparent that the traditional dividing line between analytics and operational applications is becoming more and more blurry. Historically, operational applications were built using transactional databases, and analytics were done offline. In contrast, Flink’s, state, checkpoints, and time management are the core building blocks for both operational applications with strong data consistency needs, and for real-time analytics with correctness guarantees. With these shared building blocks, developers start building what is arguably a new class of data-driven applications: applications that are operational in that they serve live systems and at the same time analytical in that they perform complex data analysis. Following application architectures like CQRS and using new features like Flink’s queryable state, streaming analytics and online applications move even closer to each other. In this talk, guided by real-world use cases, we present how the unique core concepts behind Flink simplify the development, deployment, and management of data-driven applications, and we conclude with a vision for the future for Flink and stream processing.
Apache Kafka® + Machine Learning for Supply Chain confluent
Watch this talk here: https://www.confluent.io/online-talks/apache-kafka-machine-learning-for-supply-chain
Automating multifaceted, complex workflows requires hybrid solutions like streaming analytics of IoT data, batch analytics like machine learning solutions, and real-time visualizations. Leaders in organizations who are responsible for global supply chain planning are responsible for working with and integrating with data from disparate sources around the world. Many of these data sources output information in real-time, which assists planners in operationalizing plans and interacting with manufacturing output. IoT sensors on manufacturing equipment and inventory control systems feed real-time processing pipelines to match actual production figures against planned schedules to calculate yield efficiency.
Using information from both real-time systems and batch optimization, supply chain managers are able to economize operations and automate tedious inventory and manufacturing accounting processes. Sitting on top of all these systems is a supply chain visualization tool, enabling users' visibility over the global supply chain. If you are responsible for key data integration initiatives, join for a detailed walk through of a customer's use of this system built using Confluent and Expero tools.
WHAT YOU'LL LEARN:
• See different use cases in automation industry and Industrial IoT (IIoT) where an event streaming platform adds business value.
• Understand different architecture options to leverage Apache Kafka and Confluent.
• How to leverage different analytics tools and machine learning frameworks in a flexible and scalable way.
• How real-time visualization ties together streaming and batch analytics for business users, interpreters, and analysts.
• Understand how streaming and batch analytics optimize the supply chain planning workflow.
• Conceptualize the intersection between resource utilization and manufacturing assets with long term planning and supply chain optimization.
IIoT with Kafka and Machine Learning for Supply Chain Optimization In Real Ti...Kai Wähner
I did a webinar with Confluent's partner Expero about "Apache Kafka and Machine Learning for Real Time Supply Chain Optimization". This is a great example for anybody in automation industry / Industrial IoT (IIoT) like automotive, manufacturing, logistics, etc.
We explain how a real time event streaming platform can integrate in real time with the legacy world and proprietary IIoT protocols (like Siemens S7, Modbus, Beckhoff ADS, OPC-UA, et al). You can process the data at scale and then ingest it into a modern database (like AWS S3, Snowflake or MongoDB) or analytic / machine learning framework (like TensorFlow, PyTorch or Azure Machine Learning Service).
Now You See Me, Now You Compute: Building Event-Driven Architectures with Apa...Michael Noll
Talk URL: https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/77360
Abstract: Would you cross the street with traffic information that’s a minute old? Certainly not. Modern businesses have the same needs nowadays, whether it’s due to competitive pressure or because their customers have much higher expectations of how they want to interact with a product or service. At the heart of this movement are events: in today’s digital age, events are everywhere. Every digital action—across online purchases to ride-sharing requests to bank deposits—creates a set of events around transaction amount, transaction time, user location, account balance, and much more. The technology that allows businesses to read, write, store, and compute and process these events in real-time are event-streaming platforms, and tens of thousands of companies like Netflix, Audi, PayPal, Airbnb, Uber, and Pinterest have picked Apache Kafka as the de facto choice to implement event-driven architectures and reshape their industries.
Michael Noll explores why and how you can use Apache Kafka and its growing ecosystem to build event-driven architectures that are elastic, scalable, robust, and fault tolerant, whether it’s on-premises, in the cloud, on bare metal machines, or in Kubernetes with Docker containers. Specifically, you’ll look at Kafka as the storage and publish and subscribe layer; Kafka’s Connect framework for integrating external data systems such as MySQL, Elastic, or S3 with Kafka; and Kafka’s Streams API and KSQL as the compute layer to implement event-driven applications and microservices in Java and Scala and streaming SQL, respectively, that process the events flowing through Kafka in real time. Michael provides an overview of the most relevant functionality, both current and upcoming, and shares best practices and typical use cases so you can tie it all together for your own needs.
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...HostedbyConfluent
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenzhong XU | Current 2022
If you are a data scientist or a platform engineer, you probably can relate to the pains of working with the current explosive growth of Data/ML technologies and toolings. With many overlapping options and steep learning curves for each, it’s increasingly challenging for data science teams. Many platform teams started thinking about building an abstracted ML platform layer to support generalized ML use cases. But there are many complexities involved, especially as the underlying real-time data is shifting into the mainstream.
In this talk, we’ll discuss why ML platforms can benefit from a simple and ""invisible"" abstraction. We’ll offer some evidence on why you should consider leveraging streaming technologies even if your use cases are not real-time yet. We’ll share learnings (combining both ML and Infra perspectives) about some of the hard complexities involved in building such simple abstractions, the design principles behind them, and some counterintuitive decisions you may come across along the way.
By the end of the talk, I hope data scientists can walk away with some tips on how to evaluate ML platforms, and platform engineers learned a few architectural and design tricks.
Processing Real-Time Data at Scale: A streaming platform as a central nervous...confluent
(Marcus Urbatschek, Confluent)
Presentation during Confluent’s streaming event in Munich. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Modern Stream Processing With Apache Flink @ GOTO Berlin 2017Till Rohrmann
In our fast moving world it becomes more and more important for companies to gain near real-time insights from their data to make faster decisions. These insights do not only provide a competitve edge over ones rivals but also enable a company to create completely new services and products. Amongst others, predictive user interfaces and online recommendation can be implemented when being able to process large amounts of data in real-time.
Apache Flink, one of the most advanced open source distributed stream processing platforms, allows you to extract business intelligence from your data in near real-time. With Apache Flink it is possible to process billions of messages with milliseconds latency. Moreover, its expressive APIs allow you to quickly solve your problems, ranging from classical analytical workloads to distributed event-driven applications.
In this talk, I will introduce Apache Flink and explain how it enables users to develop distributed applications and process analytical workloads alike. Starting with Flink’s basic concepts of fault-tolerance, statefulness and event-time aware processing, we will take a look at the different APIs and what they allow us to do. The talk will be concluded by demonstrating how we can use Flink’s higher level abstractions such as FlinkCEP and StreamSQL to do declarative stream processing.
The Power of Distributed Snapshots in Apache FlinkC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2FcTsIA.
Stephan Ewen talks about how Apache Flink handles stateful stream processing and how to manage distributed stream processing and data driven applications efficiently with Flink's checkpoints and savepoints. Filmed at qconsf.com.
Stephan Ewen is a PMC member and one of the original creators of Apache Flink, and co-founder and CTO of data Artisans.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. Apache Flink Research
A look into the future
Paris Carbone - PhD Candidate
KTH Royal Institute of Technology
<parisc@kth.se, senorcarbone@apache.org>
1
3. Research in Flink
• Many ideas behind Flink were research products
• Job plan optimiser
• Efficient joins
• Memory management
• Execution engine always was a streaming engine
3
4. Our Focus
• Contributions already in the current release
• Streaming Semantics - Expressive Windowing
• State Management (representation, handling)
• Graph Semantics - Gelly
• Exactly-once-processing (checkpointing)
4
5. Ongoing Research
• Advanced State Management & Fault Tolerance
• Pre-aggregate sharing for sliding windows
• Streaming ML Pipelines
• Streaming Graphs
• Experiment Reproducibility
5
7. Lessons Learned from
Batch
7
batch-1batch-2
• If a batch computation fails, simply repeat computation
as a transaction (if we have repeatable sources)
• Transaction rate is constant
• Can we apply these principles to a true streaming
execution?
12. ML Pipelines
12
training set test set
Flink ML
ETL
Transformers
Learners
Evaluators
training
stream
test
stream
Flink
Streaming ML
stream ETL
concept drift detection
anomaly detection
online learning
online classification
13. ML on Unbounded Data
13
• We are often interested in:
• Low latency approximations on a single pass
• Instant classification on stream ingestion with higher
error bounds
• Continous aggregates on unbounded data synopses
(e.g. stream sampling)
14. Streaming ML
14
Streaming APIBatch API
Table
ML
Gelly
ML
Gelly
bounded data
multi-pass algorithms
bulk classification
unbounded data
single-pass algorithms
instant classification
15. ML Use Cases
15
Batch ML Streaming ML
SVM Anomaly Detection
Clustering Concept Drift Detection
Col. Filtering (matrix
factorisation)
Incremental Clustering
Rank Estimation Dec. Tree and Rule Mining
Similarity Matching
Approximations (freq itemsets,
distinct items, samples etc.)
16. Stream ML Abstractions
16
• Reusing the same abstractions from the batch ML library
(e.g. Transformer, Learner, Evaluator)
• plus some more abstractions (e.g. Drift Detector)
https://github.com/senorcarbone/flink/tree/incremental-ml
17. Example: Vertical Hoeffding
Trees
• Building a decision tree on-the-fly
• Parallelizing attribute metric computation (vertical
parallelization)
17
20. Or even more complex
pipelines
20
Transformer Learner Evaluator
change
error
Batch ML
Pipeline
correct
schedule
Integrating Batch and Streaming ML
21. Unbounded Graph Analysis
21
• Graphs are often created by a snapshot of a stream of
events: user interactions, product purchases, clicks, etc.
• Can we process the graph as a stream, immediately when
it arrives in the system?
• We can leverage existing research on one-pass streaming
algorithms and Flink’s streaming engine
22. Streaming Graphs?
22
Streaming APIBatch API
Table
ML
Gelly
ML
Gelly
bounded graph data
multi-pass algorithms (BSP)
exact computations
unbounded graph data
single-pass algorithms
incremental computations
26. Experiments -
Reproducibility
• Defining, Deploying, orchestrating and collecting results
for experiments is a big hustle!
• A single experiment will need
• devops hours to allocate VMs, fetch the right versions
and install system dependencies in the correct order
• dev hours to write scripts for data processing/collection
• Repeating a benchmark/experiment is impossible without
all the low level configuration details
26
27. Introducing Karamel
27
standalone web app
karamel
file
karamelized
cookbooks
• Simplifying system dependencies to a
bare minimum
• Simple integration for existing cookbooks
(chef) by adding a Karamel file
• Compositional cluster definitions
• Tight integration with Github
yaml
28. Introducing Karamel
27
standalone web app
karamel
file
karamelized
cookbooks
• Simplifying system dependencies to a
bare minimum
• Simple integration for existing cookbooks
(chef) by adding a Karamel file
• Compositional cluster definitions
• Tight integration with Github
yaml