Change Data Streaming Patterns for Microservices With Debezium confluent
(Gunnar Morling, RedHat) Kafka Summit SF 2018
Debezium (noun | de·be·zi·um | /dɪ:ˈbɪ:ziːəm/): secret sauce for change data capture (CDC) streaming changes from your datastore that enables you to solve multiple challenges: synchronizing data between microservices, gradually extracting microservices from existing monoliths, maintaining different read models in CQRS-style architectures, updating caches and full-text indexes and feeding operational data to your analytics tools
Join this session to learn what CDC is about, how it can be implemented using Debezium, an open source CDC solution based on Apache Kafka and how it can be utilized for your microservices. Find out how Debezium captures all the changes from datastores such as MySQL, PostgreSQL and MongoDB, how to react to the change events in near real time and how Debezium is designed to not compromise on data correctness and completeness also if things go wrong. In a live demo we’ll show how to set up a change data stream out of your application’s database without any code changes needed. You’ll see how to sink the change events into other databases and how to push data changes to your clients using WebSockets.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
Change Data Streaming Patterns for Microservices With Debezium confluent
(Gunnar Morling, RedHat) Kafka Summit SF 2018
Debezium (noun | de·be·zi·um | /dɪ:ˈbɪ:ziːəm/): secret sauce for change data capture (CDC) streaming changes from your datastore that enables you to solve multiple challenges: synchronizing data between microservices, gradually extracting microservices from existing monoliths, maintaining different read models in CQRS-style architectures, updating caches and full-text indexes and feeding operational data to your analytics tools
Join this session to learn what CDC is about, how it can be implemented using Debezium, an open source CDC solution based on Apache Kafka and how it can be utilized for your microservices. Find out how Debezium captures all the changes from datastores such as MySQL, PostgreSQL and MongoDB, how to react to the change events in near real time and how Debezium is designed to not compromise on data correctness and completeness also if things go wrong. In a live demo we’ll show how to set up a change data stream out of your application’s database without any code changes needed. You’ll see how to sink the change events into other databases and how to push data changes to your clients using WebSockets.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
How can you avoid inconsistencies between Kafka and the database? Enter change data capture (CDC) and Debezium. By capturing changes from the log files of the database, Debezium gives you both reliable and consistent inter-service messaging via Kafka and instant read-your-own-write semantics for services themselves.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scal...Databricks
The reality of most large scale data deployments includes storage decoupled from computation, pipelines operating directly on files and metadata services with no locking mechanisms or transaction tracking. For this reason attempts at achieving transactional behavior, snapshot isolation, safe schema evolution or performant support for CRUD operations has always been marred with tradeoffs.
This talk will focus on technical aspects, practical capabilities and the potential future of three table formats that have emerged in recent years as solutions to the issues mentioned above – ACID ORC (in Hive 3.x), Iceberg and Delta Lake. To provide a richer context, a comparison between traditional databases and big data tools as well as an overview of the reasons for the current state of affairs will be included.
After the talk, the audience is expected to have a clear understanding of the current development trends in large scale table formats, on the conceptual and practical level. This should allow the attendees to make better informed assessments about which approaches to data warehousing, metadata management and data pipelining they should adapt in their organizations.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
Debezium is a Kafka Connect plugin that performs Change Data Capture from your database into Kafka. This talk demonstrates how this can be leveraged to move your data from one database platform such as MySQL to PostgreSQL. A working example is available on GitHub (github.com/gh-mlfowler/debezium-demo).
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
DB proxy server test: run tests on tens of virtual machines with Jenkins, Vag...Timofey Turenko
The presentation describes CI environment for our product - Maxscale - database proxy server. To test such product we need a setup that consists of tens of machines: locally hosted virtual machines as well as machines from different clouds. All our Jenkins jobs are implemented in the form of Jenkins Job Builder code. Presentations also tells about our tool to manage virtual machines (wrapper over Vagrant)- MDBCI.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data.
That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads.
Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos.
Here’s what you’ll learn in this 2-hour session:
How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security
How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse
A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
How can you avoid inconsistencies between Kafka and the database? Enter change data capture (CDC) and Debezium. By capturing changes from the log files of the database, Debezium gives you both reliable and consistent inter-service messaging via Kafka and instant read-your-own-write semantics for services themselves.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scal...Databricks
The reality of most large scale data deployments includes storage decoupled from computation, pipelines operating directly on files and metadata services with no locking mechanisms or transaction tracking. For this reason attempts at achieving transactional behavior, snapshot isolation, safe schema evolution or performant support for CRUD operations has always been marred with tradeoffs.
This talk will focus on technical aspects, practical capabilities and the potential future of three table formats that have emerged in recent years as solutions to the issues mentioned above – ACID ORC (in Hive 3.x), Iceberg and Delta Lake. To provide a richer context, a comparison between traditional databases and big data tools as well as an overview of the reasons for the current state of affairs will be included.
After the talk, the audience is expected to have a clear understanding of the current development trends in large scale table formats, on the conceptual and practical level. This should allow the attendees to make better informed assessments about which approaches to data warehousing, metadata management and data pipelining they should adapt in their organizations.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
Debezium is a Kafka Connect plugin that performs Change Data Capture from your database into Kafka. This talk demonstrates how this can be leveraged to move your data from one database platform such as MySQL to PostgreSQL. A working example is available on GitHub (github.com/gh-mlfowler/debezium-demo).
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
DB proxy server test: run tests on tens of virtual machines with Jenkins, Vag...Timofey Turenko
The presentation describes CI environment for our product - Maxscale - database proxy server. To test such product we need a setup that consists of tens of machines: locally hosted virtual machines as well as machines from different clouds. All our Jenkins jobs are implemented in the form of Jenkins Job Builder code. Presentations also tells about our tool to manage virtual machines (wrapper over Vagrant)- MDBCI.
Building Out Your Kafka Developer CDC Ecosystemconfluent
Building Out Your Kafka Developer CDC Ecosystem, Neil Buesing, VP of Streaming Technologies for Object Partners (OPI)
Meetup Link: https://www.meetup.com/TwinCities-Apache-Kafka/events/272944023/
DevOps Fest 2020. Сергій Калінець. Building Data Streaming Platform with Apac...DevOps_Fest
Apache Kafka зараз на хайпі. Все більше компаній починають використовувати її, як message bus. Проте Kafka може набагато більше, аніж бути просто транспортом. Її реальна міць і краса розкриваються, коли Kafka стає центральною нервовою системою вашої архітектури. Вона швидка, надійна і доволі гнучка для різних сценаріїв використання.
На цій доповіді Сергій поділитися досвідом побудови data streaming платформи. Ми поговоримо про те, як Kafka працює, як її потрібно конфігурувати і в які халепи можна потрапити, якщо Kafka використовується неоптимально.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
Apache Kafka - A modern Stream Processing PlatformGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Kafka Connect & Kafka Streams/KSQL - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Presentation @ Oracle Code Berlin.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can we make sure that all these events are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amounts of messages between a source and a target. This session will start with an introduction of Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table.
Kafka Connect & Streams - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Real-Time Log Analysis with Apache Mesos, Kafka and CassandraJoe Stein
Slides for our solution we developed for using Mesos, Docker, Kafka, Spark, Cassandra and Solr (DataStax Enterprise Edition) all developed in Go for doing realtime log analysis at scale. Many organizations either need or want log analysis in real time where you can see within a second what is happening within your entire infrastructure. Today, with the hardware available and software systems we have in place, you can develop, build and use as a service these solutions.
Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. 1 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Kafka Connect - Debezium
Stream MySQL events to Kafka
2. 2 | Kafka Connect /Debezium - Stream MySQL events to Kafka
About me
Kasun Don
Software Engineer - London
AWIN AG | Eichhornstraße 3 | 10785 Berlin
Telephone +49 (0)30 5096910 | info@awin.com | www.awin.com
• Automation & DevOps enthusiastic
• Hands on Big Data Engineering
• Open Source Contributor
3. 3 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Why Streaming MySQL events (CDC) ?
• Integrations with Legacy Applications
Avoid dual writes when Integrating with legacy systems.
• Smart Cache Invalidation
Automatically invalidate entries in a cache as soon as the record(s) for entries change or are removed.
• Monitoring Data Changes
Immediately react to data changes committed by application/user.
• Data Warehousing
Atomic operation synchronizations for ETL-type solutions.
• Event Sourcing (CQRS)
Totally ordered collection of events to asynchronously update the read-only views while writes can be recorded as normal.
4. 4 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Apache Kafka
Kafka is a distributed publish-subscribe messaging system that is designed to be fast, scalable,
and durable.
Producer
Consumer Consumer Consumer
Producer Producer
Kafka
5. 5 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Kafka Connect
Connectors – A logical process responsible for managing the copying of data between Kafka and
another system.
There are two types of connectors,
• Source Connectors import data from another system
• Sink Connectors export data from Kafka
Workers – Unit of work that schedules connectors and tasks in a
process.
There are two main type of workers: standalone and distributed
Tasks - Unit of process that handles assigned set of work load by connectors.
Connector configuration allows set to maximum number of tasks can be run by a
connector.
6. 6 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Kafka Connect - Overview
Data
Source
Data
Sink
KafkaConnect
KAFKA
KafkaConnect
7. 7 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Kafka Connect – Configuration
Common Connector Configuration
• name - Unique name for the connector. Attempting to register again with the same name will
fail.
• connector.class - The Java class for the connector
• tasks.max - The maximum number of tasks that should be created for this connector. The
connector may create fewer tasks if it cannot achieve this level of parallelism.
Please note that connector configuration might vary, see specific connector documentation for
more information.
Distributed Mode - Worker Configuration
bootstrap.servers - A list of host/port pairs to use for establishing the initial connection to the Kafka cluster.
group.id - A unique string that identifies the Connect cluster group this worker belongs to.
config.storage.topic - The topic to store connector and task configuration data in. This must be the same for all
workers with the same group.id.
offset.storage.topic - The topic to store offset data for connectors in. This must be the same for all workers with the
same group.id
status.storage.topic - The name of the topic where connector and task configuration status updates are stored.
For more distributed mode worker configuration : http://docs.confluent.io/current/connect/userguide.html#configuring-workers
8. 8 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Kafka Connect – Running A Instance
It is recommended to run Kafka Connect on containerized environments such as Kubernetes, Mesos, Docker Swarm, or
YARN.
Kafka Connect distributed mode exposes port 8083 by default to serve management REST interface.
Kafka Connect does not automatically handle restarting or scaling workers which means your existing clustering solutions can continue to be used transparently. –
Confluent.io
$ docker run -d
> --name=kafka-connect
> --net=host
> -e CONNECT_BOOTSTRAP_SERVERS="kafka-broker:9092"
> -e CONNECT_GROUP_ID="group_1"
> -e CONNECT_CONFIG_STORAGE_TOPIC="kafka-connect-config"
> -e CONNECT_OFFSET_STORAGE_TOPIC="kafka-connect-offset"
> -e CONNECT_STATUS_STORAGE_TOPIC="kafka-connect-status"
> -e CONNECT_KEY_CONVERTER="org.apache.kafka.connect.json.JsonConverter"
> -e CONNECT_VALUE_CONVERTER="org.apache.kafka.connect.json.JsonConverter"
> -e CONNECT_INTERNAL_KEY_CONVERTER="org.apache.kafka.connect.json.JsonConverter"
> -e CONNECT_INTERNAL_VALUE_CONVERTER="org.apache.kafka.connect.json.JsonConverter"
> -e CONNECT_LOG4J_LOGGERS="io.debezium.connector.mysql=INFO"
> -v /opt/kafka-connect/jars:/etc/kafka-connect/jars
> --restart always
> confluentinc/cp-kafka-connect:3.3.0
9. 9 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Debezium Connector
What is Debezium ?
Debezium is an open source distributed platform for change data capture using MySQL row-level binary
logs. Debezium built on top of Kafka Connect API Framework to support fault tolerance and high availability
using Apache Kafka eco system. Debezium records in a transaction log all row-level changes committed to
each database table.
Supported Databases
Debezium currently able to support following list of database software.
• MySQL
• MongoDB
• PostgreSQL
For more Information : http://debezium.io/docs/connectors/
10. 10 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Debezium Connector – MySQL Configuration
Enable binary logs
server-id = 1000001
log_bin = mysql-bin
binlog_format = row
binlog_row_image = full
expire_logs_days = 5
or
Enable GTIDs
gtid_mode = on
enforce_gtid_consistency = on
MySQL user with sufficient privileges
GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION
CLIENT ON *.* TO 'debezium' IDENTIFIED BY password';
Supported MySQL topologies
• MySQL standalone
• MySQL master and slave
• Highly Available MySQL clusters
• Multi-Master MySQL
• Hosted MySQL eg: Amazon RDS and Amazon Aurora
11. 11 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Debezium Connector – MySQL Connector
Configuration
Example Configuration
{
"name": "example-connector",
"config": {
"connector.class": "io.debezium.connector.mysql.MySqlConnector",
"tasks.max": "1",
"database.hostname": "127.0.0.1",
"database.port": "3306",
"database.user": "debezium",
"database.password": "dbz",
"database.server.id": "184054",
"database.server.name": "mysql-example",
"database.whitelist": "db1",
"database.history.kafka.bootstrap.servers": "kafka:9092",
"database.history.kafka.topic": "dbhistory.mysql-example"
}
}
For more configuration : http://debezium.io/docs/connectors/mysql/
12. 12 | Kafka Connect /Debezium - Stream MySQL events to Kafka
Debezium Connector – Add Connector to Kafka
Connect
For more configuration : http://debezium.io/docs/connectors/mysql/
More REST Endpoints : https://docs.confluent.io/current/connect/managing.html#using-the-rest-interface
List Available Connector plugins
$ curl -s http://kafka-connect:8083/connector-plugins
[
{
"class": "io.confluent.connect.jdbc.JdbcSinkConnector"
},
{
"class": "io.confluent.connect.jdbc.JdbcSourceConnector"
},
{
"class": "io.debezium.connector.mysql.MySqlConnector"
},
{
"class": "org.apache.kafka.connect.file.FileStreamSinkConnector"
},
{
"class": "org.apache.kafka.connect.file.FileStreamSourceConnector"
}
]
Add connector
$ curl -s -X POST -H "Content-Type: application/json" --data @connector-config.json http://kafka-connect:8083/conn
Remove connector
$ curl -X DELETE -H "Content-Type: application/json” http://kafka-connect:8083/connectors