Introducing Change Data Capture with DebeziumChengKuan Gan
This document discusses change data capture (CDC) and how it can be used to stream change events from databases. It introduces Debezium, an open source CDC platform that captures change events from transaction logs. Debezium supports capturing changes from multiple databases and transmitting them as a stream of events. The summary discusses how CDC can be used for data replication between databases, auditing, and in microservices architectures. It also covers deployment of CDC on Kubernetes using OpenShift.
API management solutions help enterprises manage, secure, and mediate API traffic, ensure that developers and partners are productive, and grow their API programs to meet the increasing demands of a digital world. APIU management capabilities including Backend as a Service (BaaS) solutions, analytics engines, and monetization enable developers to develop and extend apps with modern features, provide deep insights into the APIs, and allow API providers to monetize their APIs and developers to share in the revenue.
Log Management
Log Monitoring
Log Analysis
Need for Log Analysis
Problem with Log Analysis
Some of Log Management Tool
What is ELK Stack
ELK Stack Working
Beats
Different Types of Server Logs
Example of Winlog beat, Packetbeat, Apache2 and Nginx Server log analysis
Mimikatz
Malicious File Detection using ELK
Practical Setup
Conclusion
The document provides an overview of a presentation on developing the OpenFabrics Interfaces libfabric. It discusses design guidelines for libfabric, including optimizing the software path to hardware, being scalable, implementation agnostic, and open source. It then covers libfabric architecture, including modes, capabilities, object model, endpoint types, address vectors, data transfer types, and API bootstrap. It provides examples of how Gasnet usage maps libfabric objects and initialization, address exchange, and using multi-receive buffers. The presentation aims to provide a lightning fast introduction to libfabric version 1.5.
The document discusses Microservices architecture and compares it to monolithic architecture. It covers topics like infrastructure for Microservices including API gateways, service discovery, event buses. It also discusses design principles like domain-driven design, event sourcing and CQRS. Microservices are presented as a better approach as they allow independent deployments, scale independently and use multiple programming languages compared to monolithic applications.
Splunk is like an iceberg, on the surface we see the major components: indexers, search heads, license master, cluster master but under the water line we have a huge number of forwarders collecting and aggregating data streams. These forwarders are the foundations of any installation and configuration issues translate into problems with alerts, search performance, cluster stability and scaling out. This talk shows you to various ways to measure the efficiency of data collection and how to improve it. Prepare for lots of complex searches to identify common problems and charts that show good and bad. The talk aims to revolutionise how you think about forwarders and data collection in Splunk and turbo charge your platform performance and improve stability.
Présentation de la suite ELK dans un contexte SIEM et zoom sur Wazuh (OSSEC) , IDS open source
Venez découvrir comment être proactif face aux problèmes de cyber sécurité en analysant les données fournies par vos équipements et applications critiques.
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.
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.
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...Flink Forward
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
The document discusses intra-cluster replication in Apache Kafka, including its architecture where partitions are replicated across brokers for high availability. Kafka uses a leader and in-sync replicas approach to strongly consistent replication while tolerating failures. Performance considerations in Kafka replication include latency and durability tradeoffs for producers and optimizing throughput for consumers.
This document discusses using Apache Kafka as a data hub to capture changes from various data sources using change data capture (CDC). It outlines several common CDC patterns like using modification dates, database triggers, or log files to identify changes. It then discusses using Kafka Connect to integrate various data sources like MongoDB, PostgreSQL and replicate changes. The document provides examples of open source CDC connectors and concludes with suggestions for getting involved in the Apache Kafka community.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
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/
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Apache Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics, in a fault-tolerant and scalable way. It is used for building real-time data pipelines and streaming apps. Producers write data to topics which are committed to disks across partitions and replicated for fault tolerance. Consumers read data from topics in a decoupled manner based on offsets. Kafka can process streaming data in real-time and at large volumes with low latency and high throughput.
The document discusses Kubernetes networking. It describes how Kubernetes networking allows pods to have routable IPs and communicate without NAT, unlike Docker networking which uses NAT. It covers how services provide stable virtual IPs to access pods, and how kube-proxy implements services by configuring iptables on nodes. It also discusses the DNS integration using SkyDNS and Ingress for layer 7 routing of HTTP traffic. Finally, it briefly mentions network plugins and how Kubernetes is designed to be open and customizable.
Best Practices for ETL with Apache NiFi on Kubernetes - Albert Lewandowski, G...GetInData
Did you like it? Check out our E-book: Apache NiFi - A Complete Guide
https://ebook.getindata.com/apache-nifi-complete-guide
Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers.
Author: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Kafka is an open-source distributed commit log service that provides high-throughput messaging functionality. It is designed to handle large volumes of data and different use cases like online and offline processing more efficiently than alternatives like RabbitMQ. Kafka works by partitioning topics into segments spread across clusters of machines, and replicates across these partitions for fault tolerance. It can be used as a central data hub or pipeline for collecting, transforming, and streaming data between systems and applications.
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.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Apache Kafka is a high-throughput distributed messaging system that allows for both streaming and offline log processing. It uses Apache Zookeeper for coordination and supports activity stream processing and real-time pub/sub messaging. Kafka bridges the gaps between pure offline log processing and traditional messaging systems by providing features like batching, transactions, persistence, and support for multiple consumers.
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.
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.
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.
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...Flink Forward
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
The document discusses intra-cluster replication in Apache Kafka, including its architecture where partitions are replicated across brokers for high availability. Kafka uses a leader and in-sync replicas approach to strongly consistent replication while tolerating failures. Performance considerations in Kafka replication include latency and durability tradeoffs for producers and optimizing throughput for consumers.
This document discusses using Apache Kafka as a data hub to capture changes from various data sources using change data capture (CDC). It outlines several common CDC patterns like using modification dates, database triggers, or log files to identify changes. It then discusses using Kafka Connect to integrate various data sources like MongoDB, PostgreSQL and replicate changes. The document provides examples of open source CDC connectors and concludes with suggestions for getting involved in the Apache Kafka community.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
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/
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Apache Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics, in a fault-tolerant and scalable way. It is used for building real-time data pipelines and streaming apps. Producers write data to topics which are committed to disks across partitions and replicated for fault tolerance. Consumers read data from topics in a decoupled manner based on offsets. Kafka can process streaming data in real-time and at large volumes with low latency and high throughput.
The document discusses Kubernetes networking. It describes how Kubernetes networking allows pods to have routable IPs and communicate without NAT, unlike Docker networking which uses NAT. It covers how services provide stable virtual IPs to access pods, and how kube-proxy implements services by configuring iptables on nodes. It also discusses the DNS integration using SkyDNS and Ingress for layer 7 routing of HTTP traffic. Finally, it briefly mentions network plugins and how Kubernetes is designed to be open and customizable.
Best Practices for ETL with Apache NiFi on Kubernetes - Albert Lewandowski, G...GetInData
Did you like it? Check out our E-book: Apache NiFi - A Complete Guide
https://ebook.getindata.com/apache-nifi-complete-guide
Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers.
Author: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Kafka is an open-source distributed commit log service that provides high-throughput messaging functionality. It is designed to handle large volumes of data and different use cases like online and offline processing more efficiently than alternatives like RabbitMQ. Kafka works by partitioning topics into segments spread across clusters of machines, and replicates across these partitions for fault tolerance. It can be used as a central data hub or pipeline for collecting, transforming, and streaming data between systems and applications.
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.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Apache Kafka is a high-throughput distributed messaging system that allows for both streaming and offline log processing. It uses Apache Zookeeper for coordination and supports activity stream processing and real-time pub/sub messaging. Kafka bridges the gaps between pure offline log processing and traditional messaging systems by providing features like batching, transactions, persistence, and support for multiple consumers.
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
The document describes how to build a local Apache Kafka Connect CDC (change data capture) ecosystem using Docker and Docker Compose. It provides instructions for capturing changes from Oracle and MySQL databases and transforming and loading the data into Kafka. The setup leverages Confluent's Docker images and includes containers for Kafka, Schema Registry, Kafka Connect, and the databases. It also describes how to build custom Docker images, write single message transforms, and debug common issues.
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/
Elodina is a startup focused on supporting open source software like Mesos frameworks. They have experience implementing and assisting with Kafka, Mesos, Hadoop, Cassandra, and other big data systems. The document discusses containerizing data persistence on Mesos with various data systems like Kafka, MySQL, Cassandra and HDFS. It provides overviews of file systems, databases, and Mesos concepts like roles and resources. It also summarizes efforts to run these data systems as Mesos frameworks.
This document provides information about key concepts in Apache Kafka including producers, consumers, brokers, topics, partitions, replications, and Zookeeper. It also describes how to start the Kafka services, create and list topics, produce and consume messages, and configure producers and consumers. Finally, it briefly discusses Kafka Streams, Schema Registry, Kafka Connect, and KSQL.
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.
Cassandra - A decentralized storage systemArunit Gupta
Cassandra uses consistent hashing to partition and distribute data across nodes in the cluster. Each node is assigned a random position on a ring based on the hash value of the partition key. This allows data to be evenly distributed when nodes join or leave. Cassandra replicates data across multiple nodes for fault tolerance and high availability. It supports different replication policies like rack-aware and datacenter-aware replication to ensure replicas are not co-located. Membership and failure detection in Cassandra uses a gossip protocol and scuttlebutt reconciliation to efficiently discover nodes and detect failures in the distributed system.
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.
Multitenancy: Kafka clusters for everyone at LINEkawamuray
Yuto Kawamura from LINE Corporation presented on their use of Apache Kafka clusters to provide multitenancy for different internal teams. They face challenges in ensuring isolation between client workloads and preventing abusive clients. Their solutions include request quotas to limit client resource usage, slow logs to identify slow requests, and changes to the broker code to pre-warm caches and minimize the impact of disk reads during message fetching. With these approaches, they are able to reliably operate shared Kafka clusters with high throughput and multiple tenants.
Building big data pipelines with Kafka and KubernetesVenu Ryali
This document discusses setting up a streaming platform using Apache Kafka and Kubernetes. It describes containerizing Kafka and Kafka Streams applications and deploying them on Kubernetes for scalability, fault tolerance, and easy upgrades. It also covers performance tuning of the platform, including optimizations for RocksDB, state stores, and network traffic. Troubleshooting performance issues within containers is discussed, such as installing profiling tools in separate containers. The goal is to provide a modern, scalable platform for data pipelines and microservices.
This document provides instructions for setting up Apache Kafka and Spark Streaming to process streaming data from Kafka with Spark. It describes how to install Zookeeper and Kafka, create a Kafka topic, produce and consume messages, and run the KafkaWordCount Spark Streaming example application to perform word count on the streaming data from Kafka. It also explains the different processing semantics supported by Spark Streaming for Kafka integration.
This document provides an introduction and overview of Apache Mesos. It begins by describing Mesos' origins at companies like Google and how it enables fine-grained resource sharing in data centers. It then discusses concepts like schedulers, executors, frameworks and how Mesos allows building distributed applications and data center infrastructure. The document also covers Mesos concepts such as resources, attributes, roles, constraints and how tools like Marathon interact with Mesos. Finally, it provides examples of Mesos frameworks for technologies like Kafka and Cassandra.
Single Node Apache Kafka 0.10 Setup and Configuration on Ubuntu 14.04 documents the steps to setup a single node Apache Kafka cluster on Ubuntu 14.04. The document outlines downloading and extracting Apache Kafka 0.10, configuring Zookeeper and Kafka server properties, creating a Kafka topic, producing and consuming messages from the topic, and describing and deleting topics. Key steps include starting Zookeeper, starting the Kafka server, creating a topic, producing messages to the topic using a console producer, and consuming messages from the topic using a console consumer.
Just-in-time: Repetitive production system in which processing and movement of materials and goods occur just as they are needed, usually in small batches
JIT is characteristic of lean production systems
JIT operates with very little “fat”
By James Francis, CEO of Paradigm Asset Management
In the landscape of urban safety innovation, Mt. Vernon is emerging as a compelling case study for neighboring Westchester County cities. The municipality’s recently launched Public Safety Camera Program not only represents a significant advancement in community protection but also offers valuable insights for New Rochelle and White Plains as they consider their own safety infrastructure enhancements.
AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify
AI competitor analysis helps businesses watch and understand what their competitors are doing. Using smart competitor intelligence tools, you can track their moves, learn from their strategies, and find ways to do better. Stay smart, act fast, and grow your business with the power of AI insights.
For more information please visit here https://www.contify.com/
How iCode cybertech Helped Me Recover My Lost Fundsireneschmid345
I was devastated when I realized that I had fallen victim to an online fraud, losing a significant amount of money in the process. After countless hours of searching for a solution, I came across iCode cybertech. From the moment I reached out to their team, I felt a sense of hope that I can recommend iCode Cybertech enough for anyone who has faced similar challenges. Their commitment to helping clients and their exceptional service truly set them apart. Thank you, iCode cybertech, for turning my situation around!
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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 | [email protected] | 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