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.
Flink Forward Berlin 2017: Jörg Schad, Till Rohrmann - Apache Flink meets Apa...Flink Forward
Apache Mesos allows operators to run distributed applications across an entire datacenter and is attracting ever increasing interest. As much as distributed applications see increased use enabled by Mesos, Mesos also sees increasing use due to a growing ecosystem of well integrated applications. One of the latest additions to the Mesos family is Apache Flink. Flink is one of the most popular open source systems for real-time high scale data processing and allows users to deal with low-latency streaming analytical workloads on Mesos. In this talk we explain the challenges solved while integrating Flink with Mesos, including how Flink’s distributed architecture can be modeled as a Mesos framework, and how Flink was integrated with Fenzo. Next, we describe how Flink was packaged to easily run on DC/OS.
Apache Mesos allows operators to run distributed applications across an entire datacenter and is attracting ever increasing interest. As much as distributed applications see increased use enabled by Mesos, Mesos also sees increasing use due to a growing ecosystem of well integrated applications. One of the latest additions to the Mesos family is Apache Flink. Flink is one of the most popular open source systems for real-time high scale data processing and allows users to deal with low-latency streaming analytical workloads on Mesos. In this talk we explain the challenges solved while integrating Flink with Mesos, including how Flink’s distributed architecture can be modeled as a Mesos framework, and how Flink was integrated with Fenzo. Next, we describe how Flink was packaged to easily run on DC/OS.
With Flink 1.3 being released, the Flink community is already working towards the upcoming release 1.4. Given Flink's high development pace, which manifested in Flink 1.3 being one of the feature-wise biggest releases in its recent history, it becomes more and more difficult to keep track of all development threads. Moreover, it requires more effort to learn about newly added features and which value they provide for your application.
In this talk, I want to present and explain some of Flink's latest features, including incremental checkpointing, fine grained recovery, side outputs and many more. Furthermore, I want to put them in perspective with respect to Flink's future direction by giving some insights into ongoing development threads in the community. Thereby, I intend to give attendees a better picture about Flink's current and future capabilities.
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.
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...Flink Forward
Pravega is a stream storage system that we designed and built from the ground up for modern day stream processors such as Flink. Its storage layer is tiered and designed to provide low latency for writing and reading, while being able to store an unbounded amount of stream data that eventually becomes cold. We rely on a high-throughput component to store cold stream data, which is critical to enable applications to rely on Pravega alone for storing stream data. Pravega’s API enables applications to manipulate streams with a set of desirable features such as avoiding duplication and writing data transactionally. Both features are important for applications that require exactly-once semantics. This talk goes into the details of Pravega’s architecture and establishes the need for such a storage system.
Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloadsFlink Forward
http://flink-forward.org/kb_sessions/dynamic-scaling-how-apache-flink-adapts-to-changing-workloads/
Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.
Fabian Hueske - Stream Analytics with SQL on Apache FlinkVerverica
SQL is undoubtedly the most widely used language for data analytics. It is declarative, many database systems and query processors feature advanced query optimizers and highly efficient execution engines, and last but not least it is the standard that everybody knows and uses. With stream processing technology becoming mainstream a question arises: “Why isn’t SQL widely supported by open source stream processors?”. One answer is that SQL’s semantics and syntax have not been designed with the characteristics of streaming data in mind. Consequently, systems that want to provide support for SQL on data streams have to overcome a conceptual gap.
Apache Flink is a distributed stream processing system. Due to its support for event-time processing, exactly-once state semantics, and its high throughput capabilities, Flink is very well suited for streaming analytics. Since about a year, the Flink community is working on two relational APIs for unified stream and batch processing, the Table API and SQL. The Table API is a language-integrated relational API and the SQL interface is compliant with standard SQL. Both APIs are semantically compatible and share the same optimization and execution path based on Apache Calcite. A core principle of both APIs is to provide the same semantics for batch and streaming data sources, meaning that a query should compute the same result regardless whether it was executed on a static data set, such as a file, or on a data stream, like a Kafka topic.
In this talk we present the semantics of Apache Flink’s relational APIs for stream analytics. We discuss their conceptual model and showcase their usage. The central concept of these APIs are dynamic tables. We explain how streams are converted into dynamic tables and vice versa without losing information due to the stream-table duality. Relational queries on dynamic tables behave similar to materialized view definitions and produce new dynamic tables. We show how dynamic tables are converted back into changelog streams or are written as materialized views to external systems, such as Apache Kafka or Apache Cassandra, and are updated in place with low latency.
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4Flink Forward
With Flink 1.3 being released, the Flink community is already working towards the upcoming release 1.4. Given Flink's high development pace, which manifested in Flink 1.3 being one of the feature-wise biggest releases in its recent history, it becomes more and more difficult to keep track of all development threads. Moreover, it requires more effort to learn about newly added features and which value they provide for your application. In this talk, I want to present and explain some of Flink's latest features, including incremental checkpointing, fine grained recovery, side outputs and many more. Furthermore, I want to put them in perspective with respect to Flink's future direction by giving some insights into ongoing development threads in the community. Thereby, I intend to give attendees a better picture about Flink's current and future capabilities.
Flink Forward Berlin 2017: Jörg Schad, Till Rohrmann - Apache Flink meets Apa...Flink Forward
Apache Mesos allows operators to run distributed applications across an entire datacenter and is attracting ever increasing interest. As much as distributed applications see increased use enabled by Mesos, Mesos also sees increasing use due to a growing ecosystem of well integrated applications. One of the latest additions to the Mesos family is Apache Flink. Flink is one of the most popular open source systems for real-time high scale data processing and allows users to deal with low-latency streaming analytical workloads on Mesos. In this talk we explain the challenges solved while integrating Flink with Mesos, including how Flink’s distributed architecture can be modeled as a Mesos framework, and how Flink was integrated with Fenzo. Next, we describe how Flink was packaged to easily run on DC/OS.
Apache Mesos allows operators to run distributed applications across an entire datacenter and is attracting ever increasing interest. As much as distributed applications see increased use enabled by Mesos, Mesos also sees increasing use due to a growing ecosystem of well integrated applications. One of the latest additions to the Mesos family is Apache Flink. Flink is one of the most popular open source systems for real-time high scale data processing and allows users to deal with low-latency streaming analytical workloads on Mesos. In this talk we explain the challenges solved while integrating Flink with Mesos, including how Flink’s distributed architecture can be modeled as a Mesos framework, and how Flink was integrated with Fenzo. Next, we describe how Flink was packaged to easily run on DC/OS.
With Flink 1.3 being released, the Flink community is already working towards the upcoming release 1.4. Given Flink's high development pace, which manifested in Flink 1.3 being one of the feature-wise biggest releases in its recent history, it becomes more and more difficult to keep track of all development threads. Moreover, it requires more effort to learn about newly added features and which value they provide for your application.
In this talk, I want to present and explain some of Flink's latest features, including incremental checkpointing, fine grained recovery, side outputs and many more. Furthermore, I want to put them in perspective with respect to Flink's future direction by giving some insights into ongoing development threads in the community. Thereby, I intend to give attendees a better picture about Flink's current and future capabilities.
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.
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...Flink Forward
Pravega is a stream storage system that we designed and built from the ground up for modern day stream processors such as Flink. Its storage layer is tiered and designed to provide low latency for writing and reading, while being able to store an unbounded amount of stream data that eventually becomes cold. We rely on a high-throughput component to store cold stream data, which is critical to enable applications to rely on Pravega alone for storing stream data. Pravega’s API enables applications to manipulate streams with a set of desirable features such as avoiding duplication and writing data transactionally. Both features are important for applications that require exactly-once semantics. This talk goes into the details of Pravega’s architecture and establishes the need for such a storage system.
Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloadsFlink Forward
http://flink-forward.org/kb_sessions/dynamic-scaling-how-apache-flink-adapts-to-changing-workloads/
Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.
Fabian Hueske - Stream Analytics with SQL on Apache FlinkVerverica
SQL is undoubtedly the most widely used language for data analytics. It is declarative, many database systems and query processors feature advanced query optimizers and highly efficient execution engines, and last but not least it is the standard that everybody knows and uses. With stream processing technology becoming mainstream a question arises: “Why isn’t SQL widely supported by open source stream processors?”. One answer is that SQL’s semantics and syntax have not been designed with the characteristics of streaming data in mind. Consequently, systems that want to provide support for SQL on data streams have to overcome a conceptual gap.
Apache Flink is a distributed stream processing system. Due to its support for event-time processing, exactly-once state semantics, and its high throughput capabilities, Flink is very well suited for streaming analytics. Since about a year, the Flink community is working on two relational APIs for unified stream and batch processing, the Table API and SQL. The Table API is a language-integrated relational API and the SQL interface is compliant with standard SQL. Both APIs are semantically compatible and share the same optimization and execution path based on Apache Calcite. A core principle of both APIs is to provide the same semantics for batch and streaming data sources, meaning that a query should compute the same result regardless whether it was executed on a static data set, such as a file, or on a data stream, like a Kafka topic.
In this talk we present the semantics of Apache Flink’s relational APIs for stream analytics. We discuss their conceptual model and showcase their usage. The central concept of these APIs are dynamic tables. We explain how streams are converted into dynamic tables and vice versa without losing information due to the stream-table duality. Relational queries on dynamic tables behave similar to materialized view definitions and produce new dynamic tables. We show how dynamic tables are converted back into changelog streams or are written as materialized views to external systems, such as Apache Kafka or Apache Cassandra, and are updated in place with low latency.
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4Flink Forward
With Flink 1.3 being released, the Flink community is already working towards the upcoming release 1.4. Given Flink's high development pace, which manifested in Flink 1.3 being one of the feature-wise biggest releases in its recent history, it becomes more and more difficult to keep track of all development threads. Moreover, it requires more effort to learn about newly added features and which value they provide for your application. In this talk, I want to present and explain some of Flink's latest features, including incremental checkpointing, fine grained recovery, side outputs and many more. Furthermore, I want to put them in perspective with respect to Flink's future direction by giving some insights into ongoing development threads in the community. Thereby, I intend to give attendees a better picture about Flink's current and future capabilities.
Stephan Ewen - Experiences running Flink at Very Large ScaleVerverica
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
Flink Forward Berlin 2017: Fabian Hueske - Using Stream and Batch Processing ...Flink Forward
Apache Flink's DataStream API is very expressive and gives users precise control over time and state. However, many applications do not require this level of expressiveness and can be implemented more concisely and easily with a domain-specific API. SQL is undoubtedly the most widely used language for data processing but usually applied in the domain of batch processing. Apache Flink features two relational APIs for unified stream and batch processing, the Table API, a language-integrated relational query API for Scala and Java, and SQL. A Table API or SQL query computes the same result regardless whether it is evaluated on a static file or on a Kafka topic. While Flink evaluates queries on batch input like a conventional query engine, queries on streaming input are continuously processed and their results constantly updated and refined. In this talk we present Flink’s unified relational APIs, show how streaming SQL queries are processed, and discuss exciting new use-cases.
Flink Forward Berlin 2017: Steffen Hausmann - Build a Real-time Stream Proces...Flink Forward
The increasing number of available data sources in today's application stacks created a demand to continuously capture and process data from various sources to quickly turn high volume streams of raw data into actionable insights. Apache Flink addresses many of the challenges faced in this domain as it's specifically tailored to distributed computations over streams. While Flink provides all the necessary capabilities to process streaming data, provisioning and maintaining a Flink cluster still requires considerable effort and expertise. We will discuss how cloud services can remove most of the burden of running the clusters underlying your Flink jobs and explain how to build a real-time processing pipeline on top of AWS by integrating Flink with Amazon Kinesis and Amazon EMR. We will furthermore illustrate how to leverage the reliable, scalable, and elastic nature of the AWS cloud to effectively create and operate your real-time processing pipeline with little operational overhead.
Presenter: Robert Metzger
Video Link: https://www.youtube.com/watch?v=GWxyiTY-1uQ
Flink.tw Meetup Event (2016/07/19):
"Stream Processing with Apache Flink w/ Flink PMC Robert Metzger"
Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream p...Flink Forward
Stream Processing is a powerful paradigm, especially when backed by a system like Apache Flink. With each release and year, we see Flink being used for more challenging use case and applications.
But beyond the individual application (though it may be grand and challenging in itself), stream processing is a much broader building block: the foundational piece of a platform that brings together the different parts of a data architecture. A platform that integrates data analytics, data ingestion, SQL, Machine Learning, data provenance, databases, and other aspects of a data-driven infrastructure in a meaningful way.
In this keynote, we look at what goes into building a stream processing platform that is more than the sum of its parts.
Stateful stream processing with Apache FlinkKnoldus Inc.
Nowadays, many stream processing applications have sophisticated business logic, strict correctness guarantees, high performance, low latency, fault-tolerant, and maintain terabytes of state. There are many stream processing frameworks available in the market which helps businesses to write robust stateful stream processing applications.
In this session, we will talk about Apache Flink, a distributed stream processor with intuitive and expressive APIs to implement stateful stream processing applications. It can efficiently run such applications at a large scale in a fault-tolerant manner. In this session, we will see what is stateful stream processing in detail, and how Flink takes on stateful stream processing. We'll get to know how checkpointing mechanism works in Flink.
Announcing the next-generation dA Platform 2, which includes open source Apache Flink and the new Application Manager. dA Platform 2 makes it easier than ever to operationalize your Flink-powered stream processing applications in production.
Flink forward SF 2017: Elizabeth K. Joseph and Ravi Yadav - Flink meet DC/OS ...Flink Forward
Since the 1.2. release Apache Flink supports Apache Mesos and therefore also DC/OS as Deployment platform. In this talk, we show details of that integration and discuss tradeoffs between different deployment options. To show the ease of use, we will also live deploy small demo data pipeline using Apache Kafka and Apache Flink on top of DC/OS
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
Aljoscha Krettek offers a very short introduction to stream processing before diving into writing code and demonstrating the features in Apache Flink that make truly robust stream processing possible, with a focus on correctness and robustness in stream processing.
All of this will be done in the context of a real-time analytics application that we’ll be modifying on the fly based on the topics we’re working though, as Aljoscha exercises Flink’s unique features, demonstrates fault recovery, clearly explains why event time is such an important concept in robust, stateful stream processing, and covers the features you need in a stream processor to do robust, stateful stream processing in production.
We’ll also use a real-time analytics dashboard to visualize the results we’re computing in real time, allowing us to easily see the effects of the code we’re developing as we go along.
Topics include:
* Apache Flink
* Stateful stream processing
* Event time versus processing time
* Fault tolerance
* State management in the face of faults
* Savepoints
* Data reprocessing
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.
The streaming space is evolving at an ever increasing pace. This trend is also reflected in Apache Flink whose latest major release included again many new features. For streaming practitioners it is essential to learn about Flink's newest capabilities because often they enable completely new use cases and applications.
In this talk, I want to give a brief overview about Apache Flink and its latest feature additions, including the integration of CEP with streaming SQL, proper support for state evolution, temporal joins and many more. Furthermore, I want to put them in perspective with respect to Flink's future direction by giving some insights into ongoing development threads in the community. Thereby, I intend to give attendees a better picture about Flink's current and future capabilities.
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.
Реактивные микросервисы с Apache Kafka / Денис Иванов (2ГИС)Ontico
HighLoad++ 2017
Зал «Дели + Калькутта», 7 ноября, 14:00
Тезисы:
http://www.highload.ru/2017/abstracts/3031.html
Apache Kafka - довольно популярная опенсорс-платформа для обработки потоков сообщений. Абстракция распределенного лога, лежащая в основе Kafka, дает возможность использовать ее в качестве системы очередей, но при этом дает некоторые очень полезные преимущества, недоступные даже решениям ESB-уровня.
...
Stephan Ewen - Experiences running Flink at Very Large ScaleVerverica
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
Flink Forward Berlin 2017: Fabian Hueske - Using Stream and Batch Processing ...Flink Forward
Apache Flink's DataStream API is very expressive and gives users precise control over time and state. However, many applications do not require this level of expressiveness and can be implemented more concisely and easily with a domain-specific API. SQL is undoubtedly the most widely used language for data processing but usually applied in the domain of batch processing. Apache Flink features two relational APIs for unified stream and batch processing, the Table API, a language-integrated relational query API for Scala and Java, and SQL. A Table API or SQL query computes the same result regardless whether it is evaluated on a static file or on a Kafka topic. While Flink evaluates queries on batch input like a conventional query engine, queries on streaming input are continuously processed and their results constantly updated and refined. In this talk we present Flink’s unified relational APIs, show how streaming SQL queries are processed, and discuss exciting new use-cases.
Flink Forward Berlin 2017: Steffen Hausmann - Build a Real-time Stream Proces...Flink Forward
The increasing number of available data sources in today's application stacks created a demand to continuously capture and process data from various sources to quickly turn high volume streams of raw data into actionable insights. Apache Flink addresses many of the challenges faced in this domain as it's specifically tailored to distributed computations over streams. While Flink provides all the necessary capabilities to process streaming data, provisioning and maintaining a Flink cluster still requires considerable effort and expertise. We will discuss how cloud services can remove most of the burden of running the clusters underlying your Flink jobs and explain how to build a real-time processing pipeline on top of AWS by integrating Flink with Amazon Kinesis and Amazon EMR. We will furthermore illustrate how to leverage the reliable, scalable, and elastic nature of the AWS cloud to effectively create and operate your real-time processing pipeline with little operational overhead.
Presenter: Robert Metzger
Video Link: https://www.youtube.com/watch?v=GWxyiTY-1uQ
Flink.tw Meetup Event (2016/07/19):
"Stream Processing with Apache Flink w/ Flink PMC Robert Metzger"
Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream p...Flink Forward
Stream Processing is a powerful paradigm, especially when backed by a system like Apache Flink. With each release and year, we see Flink being used for more challenging use case and applications.
But beyond the individual application (though it may be grand and challenging in itself), stream processing is a much broader building block: the foundational piece of a platform that brings together the different parts of a data architecture. A platform that integrates data analytics, data ingestion, SQL, Machine Learning, data provenance, databases, and other aspects of a data-driven infrastructure in a meaningful way.
In this keynote, we look at what goes into building a stream processing platform that is more than the sum of its parts.
Stateful stream processing with Apache FlinkKnoldus Inc.
Nowadays, many stream processing applications have sophisticated business logic, strict correctness guarantees, high performance, low latency, fault-tolerant, and maintain terabytes of state. There are many stream processing frameworks available in the market which helps businesses to write robust stateful stream processing applications.
In this session, we will talk about Apache Flink, a distributed stream processor with intuitive and expressive APIs to implement stateful stream processing applications. It can efficiently run such applications at a large scale in a fault-tolerant manner. In this session, we will see what is stateful stream processing in detail, and how Flink takes on stateful stream processing. We'll get to know how checkpointing mechanism works in Flink.
Announcing the next-generation dA Platform 2, which includes open source Apache Flink and the new Application Manager. dA Platform 2 makes it easier than ever to operationalize your Flink-powered stream processing applications in production.
Flink forward SF 2017: Elizabeth K. Joseph and Ravi Yadav - Flink meet DC/OS ...Flink Forward
Since the 1.2. release Apache Flink supports Apache Mesos and therefore also DC/OS as Deployment platform. In this talk, we show details of that integration and discuss tradeoffs between different deployment options. To show the ease of use, we will also live deploy small demo data pipeline using Apache Kafka and Apache Flink on top of DC/OS
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
Aljoscha Krettek offers a very short introduction to stream processing before diving into writing code and demonstrating the features in Apache Flink that make truly robust stream processing possible, with a focus on correctness and robustness in stream processing.
All of this will be done in the context of a real-time analytics application that we’ll be modifying on the fly based on the topics we’re working though, as Aljoscha exercises Flink’s unique features, demonstrates fault recovery, clearly explains why event time is such an important concept in robust, stateful stream processing, and covers the features you need in a stream processor to do robust, stateful stream processing in production.
We’ll also use a real-time analytics dashboard to visualize the results we’re computing in real time, allowing us to easily see the effects of the code we’re developing as we go along.
Topics include:
* Apache Flink
* Stateful stream processing
* Event time versus processing time
* Fault tolerance
* State management in the face of faults
* Savepoints
* Data reprocessing
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.
The streaming space is evolving at an ever increasing pace. This trend is also reflected in Apache Flink whose latest major release included again many new features. For streaming practitioners it is essential to learn about Flink's newest capabilities because often they enable completely new use cases and applications.
In this talk, I want to give a brief overview about Apache Flink and its latest feature additions, including the integration of CEP with streaming SQL, proper support for state evolution, temporal joins and many more. Furthermore, I want to put them in perspective with respect to Flink's future direction by giving some insights into ongoing development threads in the community. Thereby, I intend to give attendees a better picture about Flink's current and future capabilities.
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.
Реактивные микросервисы с Apache Kafka / Денис Иванов (2ГИС)Ontico
HighLoad++ 2017
Зал «Дели + Калькутта», 7 ноября, 14:00
Тезисы:
http://www.highload.ru/2017/abstracts/3031.html
Apache Kafka - довольно популярная опенсорс-платформа для обработки потоков сообщений. Абстракция распределенного лога, лежащая в основе Kafka, дает возможность использовать ее в качестве системы очередей, но при этом дает некоторые очень полезные преимущества, недоступные даже решениям ESB-уровня.
...
Metrics are Not Enough: Monitoring Apache Kafka / Gwen Shapira (Confluent)Ontico
HighLoad++ 2017
Зал «Дели + Калькутта», 8 ноября, 17:00
Тезисы:
http://www.highload.ru/2017/abstracts/2978.html
When you are running systems in production, clearly you want to make sure they are up and running at all times. But in a distributed system such as Apache Kafka… what does “up and running” even mean?
...
Enabling Real-Time Business with Change Data CaptureMapR Technologies
Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.
Denodo DataFest 2017: Integrating Big Data and Streaming Data with Enterprise...Denodo
Watch live presentation here: https://goo.gl/UcZEHU
Big data projects are becoming mature and consistent. However, they remain siloed compared to the enterprise data. In addition, now new streaming data needs to integrated as well.
Watch this Denodo DataFest 2017 session to discover:
• How big data projects can be combined with other enterprise data.
• How to integrate streaming data into the mix.
• Benefits of aggregating the data without having to move them into a centralized repository.
Data Stream Processing - Concepts and FrameworksMatthias Niehoff
An overview on various concepts used in data stream processing. Most of them are used for solving problems in the field of time, focussing on processing time compared to event time. The techniques shown include the Dataflow API as it was introduced by Google and the concepts of stream and table duality. But I will also come up with other problems like data lookup and deployment of streaming applications and various strategies on solving these problems.
In the end I will give a brief outline on the implementation status of those strategies in the popular streaming frameworks Apache Spark Streaming, Apache Flink and Kafka Streams.
Build your First IoT Application with IBM Watson IoTJanakiram MSV
Watch this webinar to learn how to build your first connected application. I will walk you through the key steps involved in building your first IoT application in the cloud with IBM Watson IoT. At the end of the session, you will gain an understanding of registering devices and sending messages to the cloud via MQTT.
Denodo DataFest 2017: Outpace Your Competition with Real-Time ResponsesDenodo
Watch the presentation on-demand now: https://goo.gl/kceFTe
Today’s digital economy demands a new way of running business. Flexible access to information and responses in real time are essential for outpacing competition.
Watch this Denodo DataFest 2017 session to discover:
• Data access challenges faced by organizations today.
• How data virtualization facilitates real-time analytics.
• Key use cases and customer success stories.
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.
Reintroducing the Stream Processor: A universal tool for continuous data anal...Paris Carbone
The talk motivates the use of data stream processing technology for different aspects of continuous data computation, beyond "real-time" analysis, to incorporate historical data computation, reliable application logic and interactive analysis.
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.
Leveraging Mainframe Data for Modern Analyticsconfluent
“The mainframe is going away” is as true now as it was 10, 20 and 30 years ago. Mainframes are still crucial in handling critical business transactions, they were however built for an era where batch data movement was the norm and can be difficult to integrate into today’s data-driven, real-time, analytics-focused business processes as well as the environments that support them. Until now.
Join experts from Confluent, Attunity, and Capgemini for a one-hour online talk session where you’ll learn how to:
Unlock your mainframe data with unique change data capture (CDC) functionality without incurring the complexity and expense that come with sending ongoing queries into the mainframe database
How using CDC benefits advanced analytics approaches such as deep machine learning and predictive analytics
Deliver ongoing streams of data in real-time to the most demanding analytics environments
Ensure that your analytics environment includes the broadest possible range of data sources and destinations while ensuring true enterprise-grade functionality
Identify use cases that can help you get started delivering value to the business moving from POC to Pilot to Production
Stream Processing is emerging as a popular paradigm for data processing architectures, because it handles the continuous nature of most data and computation and gets rid of artificial boundaries and delays. In this talk, we are going to look at some of the most common misconceptions about stream processing and debunk them.
- Myth 1: Streaming is approximate and exactly-once is not possible.
- Myth 2: Streaming is for real-time only.
- Myth 4: Streaming is harder to learn than Batch Processing.
- Myth 3: You need to choose between latency and throughput.
We will look at these and other myths and debunk them at the example of Apache Flink. We will discuss Apache Flink's approach to high performance stream processing with state, strong consistency, low latency, and sophisticated handling of time. With such building blocks, Apache Flink can handle classes of problems previously considered out of reach for stream processing. We also take a sneak preview at the next steps for Flink.
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
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - ...Dataconomy Media
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder of DataTorrent presented "Streaming Analytics with Apache Apex" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
Apache Flink offers a fast, distributed, and failure-tolerant data-processing engine along with APIs for many different use cases, chief among them stateful stream processing. We give a quick overview of the capabilities of Flink before discussing the current state of Flink, the upcoming new release, and future developments.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache Apex
Introduction to Apache Apex - The next generation native Hadoop platform. This talk will cover details about how Apache Apex can be used as a powerful and versatile platform for big data processing. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch alerts, real-time actions, threat detection, etc.
Bio:
Pramod Immaneni is Apache Apex PMC member and senior architect at DataTorrent, where he works on Apache Apex and specializes in big data platform and applications. Prior to DataTorrent, he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs.
Keeping Analytics Data Fresh in a Streaming Architecture | John Neal, QlikHostedbyConfluent
Qlik is an industry leader across its solution stack, both on the Data Integration side of things with Qlik Replicate (real-time CDC) and Qlik Compose (data warehouse and data lake automation), and on the Analytics side with Qlik Sense. These two “sides” of Qlik are coming together more frequently these days as the need for “always fresh” data increases across organizations.
When real-time streaming applications are the topic du jour, those companies are looking to Apache Kafka to provide the architectural backbone those applications require. Those same companies turn to Qlik Replicate to put the data from their enterprise database systems into motion at scale, whether that data resides in “legacy” mainframe databases; traditional relational databases such as Oracle, MySQL, or SQL Server; or applications such as SAP and SalesForce.
In this session we will look in depth at how Qlik Replicate can be used to continuously stream changes from a source database into Apache Kafka. From there, we will explore how a purpose-built consumer can be used to provide the bridge between Apache Kafka and an analytics application such as Qlik Sense.
Similar to Modern Stream Processing With Apache Flink @ GOTO Berlin 2017 (20)
Future of Apache Flink Deployments: Containers, Kubernetes and More - Flink F...Till Rohrmann
Container technology experiences an ever increasing adoption throughout many industries. Not only does this technology make your applications portable across different machines and operating systems, it also allows to scale applications in a matter of seconds. Moreover, it significantly simplifies and speeds up deployments which decreases development and operation costs. Consequently, more and more Flink deployments run in containerized environments which poses new challenges for Flink.
In this talk, we will take a look at Flink's current and future container support which will make it a first class citizen of the container world. First of all, we will explain how the new reactive execution mode will solve the problem of seamless application scaling and how it blends in with any environment. Complementary to the reactive mode, the active execution mode demonstrates its strengths when it comes to changing workloads such as batch jobs. Last but not least, we will take a look beyond Flink's own nose and investigate how Flink can be used together with Kubernetes operators or data Artisans' Application Manager. We will conclude the talk with a short demo of Flink's native Kubernetes support and giving an outlook on future developments in the container realm.
Elastic Streams at Scale @ Flink Forward 2018 BerlinTill Rohrmann
One of the big operational challenges when running streaming applications is to cope with varying workloads. Variations, e.g. daily cycles, seasonal spikes or sudden events, require that allocated resources are constantly adapted. Otherwise, service quality deteriorates or money is wasted. Apache Flink 1.5 includes a lot of enhancements to support full resource elasticity on cluster management frameworks such as Apache Mesos. With the latest version, it is now possible to build elastic applications which can be programmatically scaled up or down in order to react to changing workloads. In this talk, we will discuss recent improvements to Flink's deployment model which also enables full resource elasticity. In particular, we will discuss how Flink leverages cluster management frameworks, e.g. Mesos, and already-introduced features like scalable state to support elastic streaming applications. We will conclude the presentation with a short demo showing how a stateful Flink application can be rescaled on top of Mesos.
Apache Flink® Meets Apache Mesos® and DC/OSTill Rohrmann
Apache Mesos allows operators to run distributed applications across an entire datacenter and is attracting ever increasing interest. As much as distributed applications see increased use enabled by Mesos, Mesos also sees increasing use due to a growing ecosystem of well integrated applications. One of the latest additions to the Mesos family is Apache Flink. Flink is one of the most popular open source systems for real-time high scale data processing and allows users to deal with low-latency streaming analytical workloads on Mesos.
In this talk we explain the challenges solved while integrating Flink with Mesos, including how Flink’s distributed architecture can be modeled as a Mesos framework, and how Flink was integrated with Fenzo. Next, we describe how Flink was packaged to easily run on DC/OS.
Apache Flink and More @ MesosCon Asia 2017Till Rohrmann
Apache Mesos allows operators to run distributed applications across an entire datacenter and is attracting ever increasing interest. As much as distributed applications see increased use enabled by Mesos, Mesos also sees increasing use due to a growing ecosystem of well-integrated applications. One of the latest additions to the Mesos family is Apache Flink.
Flink is one of the most popular open source systems for real-time high scale data processing and allows users to deal with low-latency streaming analytical workloads on Mesos.
In this talk, we explain the challenges solved while integrating Flink with Mesos, including how Flink’s distributed architecture can be modeled as a Mesos framework, and how Flink was integrated with Fenzo. Next, we describe how Flink was packaged to easily run on DC/OS.
As stream processing engines become more and more popular and are used in different environments, the demand to support different deployment scenarios increases. Depending on the user's infrastructure, a stream processor might be run on a bare metal cluster in standalone mode, deployed via Apache Yarn and Mesos, or run in a containerized environment. In order to fulfill the requirements of different deployment options and to provide enough flexibility for the future, the Flink community has recently started to redesign Flink's distributed architecture.
This talk will explain the limitations of the old architecture and how they are solved with the new design. We will present the new building blocks of a Flink cluster and demonstrate, using the example of Flink's Mesos and Docker support, how they can be combined to run Flink nearly everywhere.
Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Sys...Till Rohrmann
In recent years, the generated and collected data is increasing at an almost exponential rate. At the same time, the data’s value has been identified in terms of insights that can be provided. However, retrieving the value requires powerful analysis tools, since valuable insights are buried deep in large amounts of noise. Unfortunately, analytic capacities did not scale well with the growing data. Many existing tools run only on a single computer and are limited in terms of data size by its memory. A very promising solution to deal with large-scale data is scaling systems and exploiting parallelism.
In this presentation, we propose Gilbert, a distributed sparse linear algebra system, to decrease the imminent lack of analytic capacities. Gilbert offers a MATLAB-like programming language for linear algebra programs, which are automatically executed in parallel. Transparent parallelization is achieved by compiling the linear algebra operations first into an intermediate representation. This language-independent form enables high-level algebraic optimizations. Different optimization strategies are evaluated and the best one is chosen by a cost-based optimizer. The optimized result is then transformed into a suitable format for parallel execution. Gilbert generates execution plans for Apache Spark and Apache Flink, two massively parallel dataflow systems. Distributed matrices are represented by square blocks to guarantee a well-balanced trade-off between data parallelism and data granularity.
An exhaustive evaluation indicates that Gilbert is able to process varying amounts of data exceeding the memory of a single computer on clusters of different sizes. Two well known machine learning (ML) algorithms, namely PageRank and Gaussian non-negative matrix factorization (GNMF), are implemented with Gilbert. The performance of these algorithms is compared to optimized implementations based on Spark and Flink. Even though Gilbert is not as fast as the optimized algorithms, it simplifies the development process significantly due to its high-level programming abstraction.
Dynamic Scaling: How Apache Flink Adapts to Changing Workloads (at FlinkForwa...Till Rohrmann
Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.
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.
Apache Flink: Streaming Done Right @ FOSDEM 2016Till Rohrmann
The talk I gave at the FOSDEM 2016 on the 31st of January.
The talk explains how we can do stateful stream processing with Apache Flink at the example of counting tweet impressions. It covers Flink's windowing semantics, stateful operators, fault tolerance and performance numbers. The talks ends with giving an outlook on what's is going to happen in the next couple of months.
Streaming Data Flow with Apache Flink @ Paris Flink Meetup 2015Till Rohrmann
Talk which I gave at the first Apache Flink Meetup in Paris on the 29th of October.
It gives an introduction into Apache Flink's streaming and batch API. Furthermore, it is explained how Flink jobs are deployed. Flink's checkpointing mechanism is presented which gives exactly-once processing guarantees.
Fault Tolerance and Job Recovery in Apache Flink @ FlinkForward 2015Till Rohrmann
The talk explains how Apache Flink checkpoints stateful jobs using the asynchronous barrier snapshotting algorithm to give exactly once semantics in streaming. Furthermore, Flink's approach to master high availability (HA) is described which solves the problem of the JobManager being the single point of failure. Job checkpointing in combination with HA is the basis for Flink's fault tolerance mechanism to recover from occurring failures.
Interactive Data Analysis with Apache Flink @ Flink Meetup in BerlinTill Rohrmann
This talk shows how we can use Apache Flink and Apache Zeppelin to do interactive data analysis. The examples show the usage of FlinkML to solve a linear regression and classification problem.
Computing recommendations at extreme scale with Apache Flink @Buzzwords 2015Till Rohrmann
How to scale recommendations to extremely large scale using Apache Flink. We use matrix factorization to calculate a latent factor model which can be used for collaborative filtering. The implemented alternating least squares algorithm is able to deal with data sizes on the scale of Netflix.
Machine Learning with Apache Flink at Stockholm Machine Learning GroupTill Rohrmann
This presentation presents Apache Flink's approach to scalable machine learning: Composable machine learning pipelines, consisting of transformers and learners, and distributed linear algebra.
The presentation was held at the Machine Learning Stockholm group on the 23rd of March 2015.
Introduction to Apache Flink - Fast and reliable big data processingTill Rohrmann
This presentation introduces Apache Flink, a massively parallel data processing engine which currently undergoes the incubation process at the Apache Software Foundation. Flink's programming primitives are presented and it is shown how easily a distributed PageRank algorithm can be implemented with Flink. Intriguing features such as dedicated memory management, Hadoop compatibility, streaming and automatic optimisation make it an unique system in the world of Big Data processing.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
3. What changes faster? Data or Query?
3
Data changes slowly
compared to fast
changing queries
ad-hoc queries, data
exploration, ML training and
(hyper) parameter tuning
Batch Processing
Use Case
Data changes fast
application logic
is long-lived
continuous applications,
data pipelines, standing queries,
anomaly detection, ML evaluation,
…
Stream Processing
Use Case
7. Apache Flink in a Nutshell
7
Queries
Applications
Devices
etc.
Database
Stream
File / Object
Storage
Stateful computations over streams
real-time and historic
fast, scalable, fault tolerant, in-memory,
event time, large state, exactly-once
Historic
Data
Streams
Application
8. 8
Event Streams State (Event) Time Snapshots
The Core Building Blocks
real-time and
hindsight
complex
business logic
consistency with
out-of-order data
and late data
forking /
versioning /
time-travel
9. Powerful Abstractions
9
Process Function (events, state, time)
DataStream API (streams, windows)
Stream SQL / Tables (dynamic tables)
Stream- & Batch
Data Processing
High-level
Analytics API
Stateful Event-
Driven Applications
val stats = stream
.keyBy("sensor")
.timeWindow(Time.seconds(5))
.sum((a, b) -> a.add(b))
def processElement(event: MyEvent, ctx: Context, out: Collector[Result]) = {
// work with event and state
(event, state.value) match { … }
out.collect(…) // emit events
state.update(…) // modify state
// schedule a timer callback
ctx.timerService.registerEventTimeTimer(event.timestamp + 500)
}
Layered abstractions to
navigate simple to complex use cases
10. Hardened at scale
10
Athena X Streaming SQL
Platform Service
Streaming Platform as a Service
Fraud detection
Streaming Analytics Platform
100s jobs, 1000s nodes, TBs state
metrics, analytics, real time ML
Streaming SQL as a platform
15. Modern distributed app. architecture
15
Application
Sensor
APIs
Application
Application
Application
The limit to what you can do is how sophisticated
can you compute over the stream!
Boils down to: How well do you handle state and time
24. Scaling a Service
24
separately provision additional
database capacity
provision compute
and state together
Classic tiered architecture Streaming architecture
provision
compute
25. Rolling out a new Service
25
provision a new database
(or add capacity to an existing one)
simply occupies some
additional backup space
Classic tiered
architecture
Streaming architecture
provision compute
and state together
26. What users built on checkpoints…
§ Upgrades and Rollbacks
§ Cross Datacenter Failover
§ State Archiving
§ Application Migration
§ Spot Instance Region Chasing
§ A/B testing
§ …
26
27. 27
What is the next wave of
stream processing
applications?
28. What changes faster? Data or Query?
28
Data changes slowly
compared to fast
changing queries
ad-hoc queries, data
exploration, ML training and
tuning
Batch Processing
Use Case
Data changes fast
application logic
is long-lived
continuous applications,
data pipelines, standing queries,
anomaly detection, ML evaluation,
…
Stream Processing
Use Case
29. Analytics & Business Logic
29
Stream
Source analytics metrics
Business
Logic
Stream
Processor
events
ApplicationDatabase
32. 32
Can one build an entire sophisticated
web application (say a social network)
on a stream processor?
(Yes, we can!™)
33. 33
Social network implemented using event sourcing
and CQRS (Command Query Responsibility
Segregation) on Kafka/Flink/Elasticsearch/Redis
More: https://data-artisans.com/blog/drivetribe-cqrs-apache-flink
@
34. 34
The next wave of stream
processing applications…
… is all types of stateful
applications that react to
data and time!
36. The dA Platform Architecture
36
dA Platform 2
Apache Flink
Stateful stream processing
Kubernetes
Container platform
Logging
Streams from
Kafka, Kinesis,
S3, HDFS,
Databases, ...
dA
Application
Manager
Application lifecycle
management
Metrics
CI/CD
Real-time
Analytics
Anomaly- &
Fraud Detection
Real-time
Data Integration
Reactive
Microservices
(and more)
37. Versioned Applications, not Jobs/Jars
37
Stream
Processing
Application
Version 3
Version 2
Version 1
Code and Application
Snapshot
upgrade
upgrade
New Application
Version 3a
Version 2a
fork /
duplicate
39. Hooks for CI/CD pipelines
39
Kubernetes Cluster
Application
Version 1
CI Service
dA
Application
Manager
push
update
trigger
CI
upgrade
API
stateful
upgrade
Application
Version 2