Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems.
However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes.
We describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector.
eventbrite_kafka_summit_event_logo_v3-035858-edited.png
It covers a brief introduction to Apache Kafka Connect, giving insights about its benefits,use cases, motivation behind building Kafka Connect.And also a short discussion on its architecture.
Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
Watch this talk here: https://www.confluent.io/online-talks/how-apache-kafka-works-on-demand
Pick up best practices for developing applications that use Apache Kafka, beginning with a high level code overview for a basic producer and consumer. From there we’ll cover strategies for building powerful stream processing applications, including high availability through replication, data retention policies, producer design and producer guarantees.
We’ll delve into the details of delivery guarantees, including exactly-once semantics, partition strategies and consumer group rebalances. The talk will finish with a discussion of compacted topics, troubleshooting strategies and a security overview.
This session is part 3 of 4 in our Fundamentals for Apache Kafka series.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Kafka Streams: What it is, and how to use it?confluent
Kafka Streams is a client library for building distributed applications that process streaming data stored in Apache Kafka. It provides a high-level streams DSL that allows developers to express streaming applications as set of processing steps. Alternatively, developers can use the lower-level processor API to implement custom business logic. Kafka Streams handles tasks like fault-tolerance, scalability and state management. It represents data as streams for unbounded data or tables for bounded state. Common operations include transformations, aggregations, joins and table operations.
Introduction to Apache Kafka and Confluent... and why they matterconfluent
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
It covers a brief introduction to Apache Kafka Connect, giving insights about its benefits,use cases, motivation behind building Kafka Connect.And also a short discussion on its architecture.
Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
Watch this talk here: https://www.confluent.io/online-talks/how-apache-kafka-works-on-demand
Pick up best practices for developing applications that use Apache Kafka, beginning with a high level code overview for a basic producer and consumer. From there we’ll cover strategies for building powerful stream processing applications, including high availability through replication, data retention policies, producer design and producer guarantees.
We’ll delve into the details of delivery guarantees, including exactly-once semantics, partition strategies and consumer group rebalances. The talk will finish with a discussion of compacted topics, troubleshooting strategies and a security overview.
This session is part 3 of 4 in our Fundamentals for Apache Kafka series.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Kafka Streams: What it is, and how to use it?confluent
Kafka Streams is a client library for building distributed applications that process streaming data stored in Apache Kafka. It provides a high-level streams DSL that allows developers to express streaming applications as set of processing steps. Alternatively, developers can use the lower-level processor API to implement custom business logic. Kafka Streams handles tasks like fault-tolerance, scalability and state management. It represents data as streams for unbounded data or tables for bounded state. Common operations include transformations, aggregations, joins and table operations.
Introduction to Apache Kafka and Confluent... and why they matterconfluent
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
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.
Kafka Tutorial - basics of the Kafka streaming platformJean-Paul Azar
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have started to expand on the Java examples to correlate with the design discussion of Kafka. We have also expanded on the Kafka design section and added references.
Kafka Summit NYC 2017 - Singe Message Transforms are not the Transformations ...confluent
Single message transformations allow lightweight modifications to individual messages as they are ingested or emitted by Kafka Connect connectors. Some key uses of single message transformations include data masking, event routing, event enhancement, and partitioning. They involve simple, message-at-a-time transformations configured through properties rather than writing complex code. Kafka Streams is better suited for more complex transformations like aggregations, joins, and windowing where the transformed data is stored back in Kafka.
Kafka Streams vs. KSQL for Stream Processing on top of Apache KafkaKai Wähner
Spoilt for Choice – Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka:
Apache Kafka is a de facto standard streaming data processing platform. It is widely deployed as event streaming platform. Part of Kafka is its stream processing API “Kafka Streams”. In addition, the Kafka ecosystem now offers KSQL, a declarative, SQL-like stream processing language that lets you define powerful stream-processing applications easily. What once took some moderately sophisticated Java code can now be done at the command line with a familiar and eminently approachable syntax.
This session discusses and demos the pros and cons of Kafka Streams and KSQL to understand when to use which stream processing alternative for continuous stream processing natively on Apache Kafka infrastructures. The end of the session compares the trade-offs of Kafka Streams and KSQL to separate stream processing frameworks such as Apache Flink or Spark Streaming.
Kafka Connect and Streams (Concepts, Architecture, Features)Kai Wähner
High level introduction to Kafka Connect and Kafka Streams, two components of the Apache Kafka open source framework. See the concepts, architecture and features.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
Apache Kafka can act as both an enemy and a friend to traditional middleware like message queues, ETL tools, and enterprise service buses. As an enemy, Kafka replaces many of the individual components and provides a single scalable platform for messaging, storage, and processing. However, Kafka can also integrate with traditional middleware as a friend through connectors and client APIs, allowing certain use cases to still leverage existing tools. In complex environments with both new and legacy systems, Kafka acts as a "frenemy" - replacing some functions but integrating with other existing technologies to provide a bridge to new architectures.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Apache Kafka® is the technology behind event streaming which is fast becoming the central nervous system of flexible, scalable, modern data architectures. Customers want to connect their databases, data warehouses, applications, microservices and more, to power the event streaming platform. To connect to Apache Kafka, you need a connector!
This online talk dives into the new Verified Integrations Program and the integration requirements, the Connect API and sources and sinks that use Kafka Connect. We cover the verification steps and provide code samples created by popular application and database companies. We will discuss the resources available to support you through the connector development process.
This is Part 2 of 2 in Building Kafka Connectors - The Why and How
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
Netflix changed its data pipeline architecture recently to use Kafka as the gateway for data collection for all applications which processes hundreds of billions of messages daily. This session will discuss the motivation of moving to Kafka, the architecture and improvements we have added to make Kafka work in AWS. We will also share the lessons learned and future plans.
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.
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
This document discusses the top 5 use cases and architectures for data in motion in 2022. It describes:
1) The Kappa architecture as an alternative to the Lambda architecture that uses a single stream to handle both real-time and batch data.
2) Hyper-personalized omnichannel experiences that integrate customer data from multiple sources in real-time to provide personalized experiences across channels.
3) Multi-cloud deployments using Apache Kafka and data mesh architectures to share data across different cloud platforms.
4) Edge analytics that deploy stream processing and Kafka brokers at the edge to enable low-latency use cases and offline functionality.
5) Real-time cybersecurity applications that use streaming data
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
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.
Data Streaming with Apache Kafka & MongoDBconfluent
Explore the use-cases and architecture for Apache Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Streaming Data and Stream Processing with Apache Kafkaconfluent
Apache Kafka is an open-source streaming platform that can be used to build real-time data pipelines and streaming applications. It addresses challenges with diverse data sets arriving at increasing rates. The document discusses how Apache Kafka can help with challenges around data integration, stream processing, and managing streaming platforms at scale. It also outlines key features of Apache Kafka like the Kafka Connect API for data integration, the Kafka Streams API for stream processing, and Confluent Control Center for monitoring and management.
This document discusses Apache Kafka and Confluent's Kafka Connect tool for large-scale streaming data integration. Kafka Connect allows importing and exporting data from Kafka to other systems like HDFS, databases, search indexes, and more using reusable connectors. Connectors use converters to handle serialization between data formats. The document outlines some existing connectors and upcoming improvements to Kafka Connect.
Un'introduzione ad Apache Kafka e Kafka Connect APIs (part of Apache Kafka), in particolare come Kafka possa essere usato assieme ad Elasticsearch.
Grazie a Seacom per averci invitato all'evento a Roma.
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.
Kafka Tutorial - basics of the Kafka streaming platformJean-Paul Azar
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have started to expand on the Java examples to correlate with the design discussion of Kafka. We have also expanded on the Kafka design section and added references.
Kafka Summit NYC 2017 - Singe Message Transforms are not the Transformations ...confluent
Single message transformations allow lightweight modifications to individual messages as they are ingested or emitted by Kafka Connect connectors. Some key uses of single message transformations include data masking, event routing, event enhancement, and partitioning. They involve simple, message-at-a-time transformations configured through properties rather than writing complex code. Kafka Streams is better suited for more complex transformations like aggregations, joins, and windowing where the transformed data is stored back in Kafka.
Kafka Streams vs. KSQL for Stream Processing on top of Apache KafkaKai Wähner
Spoilt for Choice – Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka:
Apache Kafka is a de facto standard streaming data processing platform. It is widely deployed as event streaming platform. Part of Kafka is its stream processing API “Kafka Streams”. In addition, the Kafka ecosystem now offers KSQL, a declarative, SQL-like stream processing language that lets you define powerful stream-processing applications easily. What once took some moderately sophisticated Java code can now be done at the command line with a familiar and eminently approachable syntax.
This session discusses and demos the pros and cons of Kafka Streams and KSQL to understand when to use which stream processing alternative for continuous stream processing natively on Apache Kafka infrastructures. The end of the session compares the trade-offs of Kafka Streams and KSQL to separate stream processing frameworks such as Apache Flink or Spark Streaming.
Kafka Connect and Streams (Concepts, Architecture, Features)Kai Wähner
High level introduction to Kafka Connect and Kafka Streams, two components of the Apache Kafka open source framework. See the concepts, architecture and features.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
Apache Kafka can act as both an enemy and a friend to traditional middleware like message queues, ETL tools, and enterprise service buses. As an enemy, Kafka replaces many of the individual components and provides a single scalable platform for messaging, storage, and processing. However, Kafka can also integrate with traditional middleware as a friend through connectors and client APIs, allowing certain use cases to still leverage existing tools. In complex environments with both new and legacy systems, Kafka acts as a "frenemy" - replacing some functions but integrating with other existing technologies to provide a bridge to new architectures.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Apache Kafka® is the technology behind event streaming which is fast becoming the central nervous system of flexible, scalable, modern data architectures. Customers want to connect their databases, data warehouses, applications, microservices and more, to power the event streaming platform. To connect to Apache Kafka, you need a connector!
This online talk dives into the new Verified Integrations Program and the integration requirements, the Connect API and sources and sinks that use Kafka Connect. We cover the verification steps and provide code samples created by popular application and database companies. We will discuss the resources available to support you through the connector development process.
This is Part 2 of 2 in Building Kafka Connectors - The Why and How
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
Netflix changed its data pipeline architecture recently to use Kafka as the gateway for data collection for all applications which processes hundreds of billions of messages daily. This session will discuss the motivation of moving to Kafka, the architecture and improvements we have added to make Kafka work in AWS. We will also share the lessons learned and future plans.
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.
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
This document discusses the top 5 use cases and architectures for data in motion in 2022. It describes:
1) The Kappa architecture as an alternative to the Lambda architecture that uses a single stream to handle both real-time and batch data.
2) Hyper-personalized omnichannel experiences that integrate customer data from multiple sources in real-time to provide personalized experiences across channels.
3) Multi-cloud deployments using Apache Kafka and data mesh architectures to share data across different cloud platforms.
4) Edge analytics that deploy stream processing and Kafka brokers at the edge to enable low-latency use cases and offline functionality.
5) Real-time cybersecurity applications that use streaming data
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
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.
Data Streaming with Apache Kafka & MongoDBconfluent
Explore the use-cases and architecture for Apache Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Streaming Data and Stream Processing with Apache Kafkaconfluent
Apache Kafka is an open-source streaming platform that can be used to build real-time data pipelines and streaming applications. It addresses challenges with diverse data sets arriving at increasing rates. The document discusses how Apache Kafka can help with challenges around data integration, stream processing, and managing streaming platforms at scale. It also outlines key features of Apache Kafka like the Kafka Connect API for data integration, the Kafka Streams API for stream processing, and Confluent Control Center for monitoring and management.
This document discusses Apache Kafka and Confluent's Kafka Connect tool for large-scale streaming data integration. Kafka Connect allows importing and exporting data from Kafka to other systems like HDFS, databases, search indexes, and more using reusable connectors. Connectors use converters to handle serialization between data formats. The document outlines some existing connectors and upcoming improvements to Kafka Connect.
Un'introduzione ad Apache Kafka e Kafka Connect APIs (part of Apache Kafka), in particolare come Kafka possa essere usato assieme ad Elasticsearch.
Grazie a Seacom per averci invitato all'evento a Roma.
Building Realtime Data Pipelines with Kafka Connect and Spark Streaming by Ew...Spark Summit
Kafka Connect allows for building real-time data pipelines with Kafka and Spark Streaming by enabling large-scale streaming data import and export to Kafka. It provides a separation of concerns between connectors that are responsible for importing or exporting data and tasks that run in parallel to perform the work. Kafka Connect supports at least once delivery guarantees through automatic offset checkpointing and recovery. When combined with Spark Streaming, it increases the number of systems Spark Streaming can integrate with and reduces the need for Spark-specific connectors by leveraging Kafka as the streaming data storage layer.
Building Realtim Data Pipelines with Kafka Connect and Spark StreamingGuozhang Wang
Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier.
Building Realtime Data Pipelines with Kafka Connect and Spark StreamingJen Aman
This document discusses building real-time data pipelines with Kafka Connect and Spark Streaming. It introduces Kafka Connect as a tool for large-scale streaming data import and export for Kafka. Kafka Connect uses connectors to move data between Kafka and other data systems in a scalable, parallel, and fault-tolerant manner. It then discusses how Kafka Connect can be used together with Spark Streaming to provide real-time data integration capabilities.
Introducing Kafka Connect and Implementing Custom ConnectorsItai Yaffe
Kobi Hikri (Independent Software Architect and Consultant):
Kobi provides a short intro to Kafka Connect, and then shows an actual code example of developing and dockerizing a custom connector.
Integrating Apache Kafka and Elastic Using the Connect Frameworkconfluent
As a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and excels at processing streams of real-time events. Kafka provides reliable, millisecond delivery for connecting downstream systems with real-time data.
In this talk, we will show how easy it is to leverage Kafka and the Elasticsearch connector to keep your indices populated with the latest data from the rest of your enterprise, as it changes.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
Apache Kafka and KSQL in Action: Let's Build a Streaming Data Pipeline!confluent
This document provides an overview and introduction to Apache Kafka and KSQL for building streaming data pipelines. It discusses how Kafka is an event streaming platform that can be used for messaging, streaming data, and stream processing. It then introduces KSQL, which is a streaming SQL engine for Apache Kafka that allows users to perform stream processing by writing SQL-like queries against Kafka topics. The document uses diagrams and examples to illustrate how to build a streaming data pipeline using Kafka Connect to ingest data, Kafka to store and transport streams, and KSQL to perform stream processing, enrichment, and analytics.
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
Scala usergroup stockholm - reactive integrations with akka streamsJohan Andrén
This document discusses Akka Streams, which provide asynchronous back pressured stream processing in Akka. It describes key Akka Stream concepts like sources, sinks, and flows. It also discusses how Akka Streams integrate with other technologies like Reactive Streams, Kafka, HTTP servers and clients. Alpakka is mentioned as a community for developing Akka Stream connectors.
Introduction to Apache Kafka and Confluent... and why they matter!Paolo Castagna
This is a short introduction to Apache Kafka and Confluent (the company founded by the creator of Kafka). The slides cover Apache Kafka APIs including Kafka Connect and Kafka Streams (part of Apache Kafka). Other open source, ASL licensed, projects are mentioned: #KSQL, Schema Registry, REST Proxy, etc.
Many thanks to Codemotion and Seacom for hosting the event.
Build Real-Time Streaming ETL Pipelines With Akka Streams, Alpakka And Apache...Lightbend
Things were easier when all our data used to be offline, analyzed overnight in batches. Now our data is online, in motion, and generated constantly. For architects, developers and their businesses, this means that there is an urgent need for tools and applications that can deliver real-time (or near real-time) streaming ETL capabilities.
In this session by Konrad Malawski, author, speaker and Senior Akka Engineer at Lightbend, you will learn how to build these streaming ETL pipelines with Akka Streams, Alpakka and Apache Kafka, and why they matter to enterprises that are increasingly turning to streaming Fast Data applications.
1. Kafka is described as a "WAL (write-ahead logging) system" and "the global commit log thingy" that was used as part of LinkedIn's data pipeline architecture.
2. LinkedIn had an ad hoc approach to data pipelines between systems that became more complex over time, so they built pipelines using Kafka.
3. The Kafka ecosystem includes storage using Kafka brokers, publishing and subscribing using producers and consumers, and stream processing using tools like Kafka Streams and KSQL.
This document introduces KSQL, a streaming SQL engine for Apache Kafka. KSQL allows users to write streaming queries using SQL without needing to write code in languages like Java or Python. It provides powerful stream processing capabilities like joins, aggregations, and windowing functions. KSQL compiles SQL queries into Kafka Streams applications that run continuously on Apache Kafka. This lowers the barrier to entry for stream processing on Kafka compared to other systems that require programming.
Kafka Connect is a scalable and resilient tool for integrating Kafka with other systems. There are two main options for integrating a database with Kafka - using the JDBC connector for Kafka Connect, or using a log-based Change Data Capture (CDC) tool which also integrates with Kafka Connect. The JDBC connector allows streaming data between Kafka and any Relational Database Management System (RDBMS) that supports JDBC, while CDC tools provide a log of all changes to a database that can then be streamed to Kafka.
Kafka, Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform (Kafka Core + Kafka Connect + Kafka Streams) for building streaming data pipelines and streaming data applications.
This talk, that I gave at the Chicago Java Users Group (CJUG) on June 8th 2017, is mainly focusing on Kafka Streams, a lightweight open source Java library for building stream processing applications on top of Kafka using Kafka topics as input/output.
You will learn more about the following:
1. Apache Kafka: a Streaming Data Platform
2. Overview of Kafka Streams: Before Kafka Streams? What is Kafka Streams? Why Kafka Streams? What are Kafka Streams key concepts? Kafka Streams APIs and code examples?
3. Writing, deploying and running your first Kafka Streams application
4. Code and Demo of an end-to-end Kafka-based Streaming Data Application
5. Where to go from here?
Similar to Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Cheslack-Postava (20)
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
In our exclusive webinar, you'll learn why event-driven architecture is the key to unlocking cost efficiency, operational effectiveness, and profitability. Gain insights on how this approach differs from API-driven methods and why it's essential for your organization's success.
Santander Stream Processing with Apache Flinkconfluent
Flink is becoming the de facto standard for stream processing due to its scalability, performance, fault tolerance, and language flexibility. It supports stream processing, batch processing, and analytics through one unified system. Developers choose Flink for its robust feature set and ability to handle stream processing workloads at large scales efficiently.
Unlocking the Power of IoT: A comprehensive approach to real-time insightsconfluent
In today's data-driven world, the Internet of Things (IoT) is revolutionizing industries and unlocking new possibilities. Join Data Reply, Confluent, and Imply as we unveil a comprehensive solution for IoT that harnesses the power of real-time insights.
Workshop híbrido: Stream Processing con Flinkconfluent
El Stream processing es un requisito previo de la pila de data streaming, que impulsa aplicaciones y pipelines en tiempo real.
Permite una mayor portabilidad de datos, una utilización optimizada de recursos y una mejor experiencia del cliente al procesar flujos de datos en tiempo real.
En nuestro taller práctico híbrido, aprenderás cómo filtrar, unir y enriquecer fácilmente datos en tiempo real dentro de Confluent Cloud utilizando nuestro servicio Flink sin servidor.
Industry 4.0: Building the Unified Namespace with Confluent, HiveMQ and Spark...confluent
Our talk will explore the transformative impact of integrating Confluent, HiveMQ, and SparkPlug in Industry 4.0, emphasizing the creation of a Unified Namespace.
In addition to the creation of a Unified Namespace, our webinar will also delve into Stream Governance and Scaling, highlighting how these aspects are crucial for managing complex data flows and ensuring robust, scalable IIoT-Platforms.
You will learn how to ensure data accuracy and reliability, expand your data processing capabilities, and optimize your data management processes.
Don't miss out on this opportunity to learn from industry experts and take your business to the next level.
La arquitectura impulsada por eventos (EDA) será el corazón del ecosistema de MAPFRE. Para seguir siendo competitivas, las empresas de hoy dependen cada vez más del análisis de datos en tiempo real, lo que les permite obtener información y tiempos de respuesta más rápidos. Los negocios con datos en tiempo real consisten en tomar conciencia de la situación, detectar y responder a lo que está sucediendo en el mundo ahora.
Eventos y Microservicios - Santander TechTalkconfluent
Durante esta sesión examinaremos cómo el mundo de los eventos y los microservicios se complementan y mejoran explorando cómo los patrones basados en eventos nos permiten descomponer monolitos de manera escalable, resiliente y desacoplada.
Q&A with Confluent Experts: Navigating Networking in Confluent Cloudconfluent
This document discusses networking options and best practices for Confluent Cloud. It provides an overview of public endpoints, private link, and peering options. It then discusses best practices for private networking architectures on Azure using hub-and-spoke and private link designs. Finally, it addresses networking considerations and challenges for Kafka Connect managed connectors, as well as planned enhancements for DNS peering and outbound private link support.
Purpose of the session is to have a dive into Apache, Kafka, Data Streaming and Kafka in the cloud
- Dive into Apache Kafka
- Data Streaming
- Kafka in the cloud
Build real-time streaming data pipelines to AWS with Confluentconfluent
Traditional data pipelines often face scalability issues and challenges related to cost, their monolithic design, and reliance on batch data processing. They also typically operate under the premise that all data needs to be stored in a single centralized data source before it's put to practical use. Confluent Cloud on Amazon Web Services (AWS) provides a fully managed cloud-native platform that helps you simplify the way you build real-time data flows using streaming data pipelines and Apache Kafka.
Q&A with Confluent Professional Services: Confluent Service Meshconfluent
No matter whether you are migrating your Kafka cluster to Confluent Cloud, running a cloud-hybrid environment or are in a different situation where data protection and encryption of sensitive information is required, Confluent Service Mesh allows you to transparently encrypt your data without the need to make code changes to you existing applications.
Citi Tech Talk: Event Driven Kafka Microservicesconfluent
Microservices have become a dominant architectural paradigm for building systems in the enterprise, but they are not without their tradeoffs. Learn how to build event-driven microservices with Apache Kafka
Confluent & GSI Webinars series - Session 3confluent
An in depth look at how Confluent is being used in the financial services industry. Gain an understanding of how organisations are utilising data in motion to solve common problems and gain benefits from their real time data capabilities.
It will look more deeply into some specific use cases and show how Confluent technology is used to manage costs and mitigate risks.
This session is aimed at Solutions Architects, Sales Engineers and Pre Sales, and also the more technically minded business aligned people. Whilst this is not a deeply technical session, a level of knowledge around Kafka would be helpful.
This document discusses moving to an event-driven architecture using Confluent. It begins by outlining some of the limitations of traditional messaging middleware approaches. Confluent provides benefits like stream processing, persistence, scalability and reliability while avoiding issues like lack of structure, slow consumers, and technical debt. The document then discusses how Confluent can help modernize architectures, enable new real-time use cases, and reduce costs through migration. It provides examples of how companies like Advance Auto Parts and Nord/LB have benefitted from implementing Confluent platforms.
This session will show why the old paradigm does not work and that a new approach to the data strategy needs to be taken. It aims to show how a Data Streaming Platform is integral to the evolution of a company’s data strategy and how Confluent is not just an integration layer but the central nervous system for an organisation
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Confluent Partner Tech Talk with Synthesisconfluent
A discussion on the arduous planning process, and deep dive into the design/architectural decisions.
Learn more about the networking, RBAC strategies, the automation, and the deployment plan.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
21. Offsets automatically committed and restored
On restart: task checks offsets & rewinds
At least once delivery – flush data, then commit
Exactly once for connectors that support it (e.g. HDFS)
Delivery Guarantees
22. Abstract serialization: 1 connector, many serialization formats
Convert between Kafka Connect Data API (Connectors) and serialized bytes
(Kafka)
JSON and Avro are currently well supported
Converters
23.
24. Confluent Open Source – HDFS, JDBC
Connector Hub: connectors.confluent.io
Examples: MySQL, MongoDB, Twitter, Solr, S3, MQTT, Bloomberg, Apache Ignite, and more
Connectors Today
25. Jenkins connector – Aravind Yarram (Equifax)
Twitter semantic analysis and visualization – Ashish Singh (Cloudera)
Brain monitoring device connector – Silicon Valley Data Science
DynamoDB, Cassandra, Slack, Splunk, and many more
Connectors from the Hackathon
26. Improved connector control via REST API, standardized configs, metrics
Single record transformations
Data pipelines in an app - embedded mode & Kafka Streams integration
Many more connectors
Coming soon…
Hi everyone and thanks for coming. Today I want to tell you about Kafka Connect and how it’s helping to address the challenges of real-time data integration.
Traditional model with relational DB with data for OLTP and data was copied into data warehouse for OLAP. There was one primary data store for active data and one for offline, batch analysis.
More types of data stores with specialized functionality – e.g. rise of NoSQL systems handling document-oriented and columnar stores. A lot more sources of data.
Rise of secondary data stores and indexes – e.g. Elasticsearch for efficient text-based queries, graph DBs for graph-oriented queries, time series databases. A lot more destinations for data, and a lot of transformations along the way to those destinations.
Real-time: data needs to be moved between these systems continuously and at low latency.
Unfortunately, as you build up large, complex data pipelines in an ad hoc fashion by connecting different data systems that need copies of the same data with one-off connectors for those systems, or build out custom connectors for stream processing frameworks to handle different sources and sinks of streaming data, we end up with a giant, unmaintainable mess.
This mess has a huge impact on productivity and agility once you get past just a few systems. Adding any new data storage system or stream processing job requires carefully tracking down all the downstream systems that might be affected, which may require coordinating with dozens of teams and code spread across many repositories. Trying to change one data source’s data format can impact many downstream systems, yet there’s no simple way to discover how these jobs are related.
This is a real problem that we’re seeing across a variety of companies today. We need to do something to simplify this picture. While Confluent is working to build out a number of tools to help with these challenges, today I want to focus on how we can standardize and simplify constructing these data pipelines so that, at a minimum, we reduce operational complexity and make it easier to discover and understand the full data pipeline and dependencies.
We refer to this problem as data integration – by which we broadly mean making sure data gets to all the right places. We need to be able to collect data from a diverse set of sources and then feed it to several downstream applications and systems for processing.
This problem isn’t a new one. There were legacy solutions to this problem but the approach of copying data in an ad-hoc way across applications just does not scale anymore. Today data is in motion and it needs to move in real-time and at scale.
I want to start by highlighting some anti-patterns we observe in how people are tackling this problem today.
One-off tools – connect any two given specific systems.
High complexity, operational overhead
Designed to be too specific – n^2 connectors
Overly-generic data copying tools – make few assumptions, connect any and all inputs and outputs, and do a bunch of intermediate transformation as well.
Try to do too much – E, T, and L with weak interfaces
Too abstract – difficult/impossible to make guarantees even when connecting right pairs of systems
Stream processing tools for data integration
Overkill for simple EL workloads
Weaker connector ecosystem – focus is rightly on T
Generic, weak interfaces as found in generic data copying tools result in difficult to understand semantics and guarantees
When we get too specific, handling everything ad hoc, we end up with a ton of different tools for every connection, often times many different tools for doing transformations, and probably the worst case – a lot of different tools that do *all* of ETL for specific systems.
If we have too little separation of concerns, we end up in situations where we use the stream processing framework for literally every step even though they use a specific model that doesn’t map well to ingesting or exporting data from many types of systems. Alternatively, we use overly generic data copying & transformation tools. These tools are so abstract that they can’t provide many guarantees and become overly complex, requiring you to learn a dozen concepts just to setup a simple pipeline.
What we really need is a separation of concerns in ET&L.
One step towards getting to a separation of concerns is being able to decouple the E, T, and L steps. Kafka, when used as shown here, can help us do that.
The vision of Kafka when originally built at LinkedIn was for it to act as a common hub for real-time data.
When streaming data from data stores like RDBMS or K/V store, we produce data into Kafka, making it available to as many downstream consumers as want it.
Save data to other systems like secondary indexes and batch storage systems, which are implemented with consumers.
Stream processing frameworks and custom consumer apps fit in by being both consumers and producers – reading data from Kafka transforming it, and then possibly publishing derived data back into Kafka.
Using this model can simplify the problem as we’re now always interacting with .
To set some context, I want to just quickly list a few of the features that make it possible for Kafka to handle data at this scale. We’ll come back to many of these properties when looking at Kafka Connect.
At its core, pub/sub messaging system rethought as distributed commit log.
Based on an append-only and sequentially accessed log, which results in very high performance reading and writing data.
Extends the model to a *partitioned stream* model for a single logical topic of data, which allows for distribution of data on the brokers and parallelism in both writes and reads. In order to still provide organization and ordering within a single partition, it guarantees ordering within each partition and uses keys to determine which partition to put data in.
As part of its append-only approach, it decouples data consumption from data retention policy, e.g. retaining data for 7 days or until we have 1TB in a topic. This both gets rid of individual message acking and allows multiple consumption of the same data, i.e. pub/sub, by simply tracking offsets in the stream.
Because data is split across partitions, we can also parallelize consumption and make it elastically scalable with Kafka’s unique automatically balanced consumer groups.
Given all these properties, it’s easy to see how Kafka can fit this central role as the hub for all your realtime data, and we can simplify the original image of our data pipeline. However, with the regular Kafka clients, we’re still leaving quite a bit on the table – each connection in the image still requires its own tool or Kafka application to get data to or from Kafka. Each tool uses these relatively low-level clients and has to implement many common features.
Today, I want to introduce you to Kafka Connect, Kafka’s new large-scale, streaming data import/export tool that drastically simplifies the construction, maintenance, and monitoring of these data pipelines.
Kafka Connect is part of the Apache Kafka project, open source under the Apache license, and ships with Kafka. It’s a framework for building connectors between other data systems and Kafka, and the associated runtime to run these connectors in a distributed, fault tolerant manner at scale.
Goals:
Focus – copying only
Batteries included – framework does all the common stuff so connector developers can focus specifically on details that need to be customized for their system. This covers a lot more than many connector developers realize: beyond managing the producer or consumer, it includes challenges like scalability, recovery from faults and reasoning about delivery guarantees, serialization, connector control, monitoring for ops, and more.
Standardize – configuration, status and connector control, monitoring, etc.
Parallelism, scalability, fault tolerance built-in, without a lot of effort from connector developers or users.
Scale – in two ways. First, scale individual connectors to copy as much data as possible – ingest an entire database rather than one table at a time. Second, scale up to organization-wide data pipelines or down to development, testing, or just copying a single log file into Kafka
With these goals in mind, let’s explore the design of Kafka Connect to see how it fulfills these.
At it’s core, Kafka connect is pretty simple. It has source connectors which copy data from another system into Kafka, and sink connectors that copy data from Kafka into a destination system.
Here I’ve shown a couple of examples. The source and sink systems don’t necessarily have to naturally match Kafka’s data model exactly. However, we do need to be able to translate data between the two. For example, we might load data from a database in a source connector. By using a timestamp column associated with each row, we can effectively generate an ordered stream of events that are then produced into Kafka. To store data into HDFS, we might load data from one or more topics in Kafka and then write it in sequence to files in an HDFS directory, rotating files periodically. Although Kafka Connect is designed around streaming data, because Kafka acts as a good buffer between streaming and batch systems, we can use it here to load data into HDFS. Neither of these systems map directly to Kafka’s model, but both can be adapted to the concepts of streams with offsets. More about this in a minute.
The most important design point for Kafka Connect is that one half of a connection is always Kafka – the destination for sources, or the source of data for sink connectors. This allows the framework to handle the common functionality of connectors while maintaining the ability to automatically provide scalability, fault tolerance, and delivery guarantees without requiring a lot of effort from connector developers. This key assumption is what makes it possible for Kafka Connect to get a better set of tradeoffs than the systems I mentioned earlier.
So now, coming back to the model that connectors need to map to. Just as Kafka’s data model enables certain features around scalability, Kafka Connect’s data model can as well.
Kafka Connect requires every connector to map to a “partitioned stream” model. The basic idea is a generalization of Kafka’s data model of topics and partitions. This mapping is defined by the input system for the connector – the source system for source connectors, and Kafka topics for sink connectors -- and has the following:
A set of partitions which divide the whole set of data logically. Unlike Kafka, the number of partitions can potentially be very large and may be more dynamic than we would expect with Kafka.
Each partition contains an ordered sequence of events/messages. Under the hood these are key/value pairs with byte[], but Kafka Connect requires that they can be converted into a generic data API
Each event/message has a unique offset representing its position in the partition. Since the mapping is determined by the input system, these offsets must be meaningful to that system – these may be quite different from the Kafka offsets you’re used to.
To give a more concrete example, we can revisit the database example from earlier. Previously I only showed a single table, but if we consider the database as a whole, we can apply this model to copy the entire database. We partition by table, delivering each into its own Kafka topic. Each event represents a row that we’ve inserted into the database. The offsets are IDs or timestamps, or even more complex representations like a combination of ID and timestamp. Although there isn’t *actually* a stream for each table, we can effectively construct one by querying the database and ordering results according to specific rules.
As a result of this model, we can see a few properties emerging:
First, we have a built-in concept of parallelism, a requirement for automatically providing scalable data copying. We’re going to be able to distribute processing of partitions across multiple hosts.
Second, this model encourages making copying broad by default – partitioned streams should cover the largest logical collection of data.
Finally, offsets provide an easy way to track which data has been processed and which still needs to be copied. In some cases, mapping from the native data model to streams may not be simple; however, a bit of effort in creating this mapping pays off by providing a common framework and implementation for tracking which data has been copied. Again, we’ll revisit this a bit later, but this allows the framework to handle a lot of the heavy lifting with regards to delivery semantics.
Partitioned streams are the logical data model, but they don’t directly map to physical parallelism, or threads, in Kafka Connect. In the case of the database connector, a direct mapping might seem reasonable. However, some connectors will have a much larger number of partitions that are much finer-grained. For example, consider a connector for collecting metrics data – each metric might be considered its own partition, resulting in tens of thousands of partitions for even a small set of application servers.
However, we do want to exploit the parallelism provided by partitions. Connectors do this by assigning partitions to tasks. Tasks are, simply, threads of control given to the connector code which perform the actual copying of data.
Each connector is given a thread it can use to monitor the input system for the active set of partitions. Remember that this set can be dynamic, so continuous monitoring is sometimes needed to detect changes to the set of partitions. When there are changes, the connector notifies the framework so it can reconfigure the current set of tasks.
Then, each task is given a dedicated thread for processing. The connector assigns a subset of partitions to each task and the task is the one that actually copies the data for that partition. Given the assignment, the connector implementer handles the reading or writing data from that set of partitions.
And how do we decide how many tasks to generate? That’s up to the user, and it’s the primary way to control the total resources used by the connector. Since each task corresponds to a thread, the user can choose to dynamically increase or decrease the maximum number of tasks the connector may create in order to scale resource usage up or down.
So now we have some set of threads, but where do they actually execute? Kafka Connect has two modes of execution.
Standalone mode works as a single process. This is really easy to get started with, easy to configure.
We like this because it scales down really easily and stays local for testing. It’s also great for connectors that really only make sense on a single node – for example, processing log files, where you need to read the data off the local file system.
If you’ve used systems like logstash or flume, this mode should look familiar. It’s commonly referred to as either standalone or agent mode.
In contrast, distributed mode can scale up while providing distribution and fault tolerance.
Recall that each connector or task is a thread, and we’re considering each to be approximately equal in terms of resource usage.
Connectors and tasks are auto-balanced across workers. Failures automatically handled by redistributing work, and you can easily scale the cluster up or down by adding more workers.
Cool implementation note: reuses group membership functionality of consumer groups. Note how if you replace “worker” with “consumer” and “task” with “topic partition”, the things it is doing look largely the same: assigning tasks to workers, detecting when a worker is added or fails, and rebalancing the work. Kafka already provides support for doing a lot of this, so by leveraging the existing implementation and coordinating through Kafka’s group functionality (with internal data stored in Kafka topics), Kafka Connect can provide this functionality in a relatively small code footprint.
Finally, note that Kafka Connect does not own the process management at all. We don’t want to make assumptions about using Mesos, YARN, or any other tool because that would unnecessarily limit Kafka Connect’s usage. Kafka Connect will work out of the box in any of these cluster management systems, or with orchestration tools, or if you just manage your processes with your own tooling.
In contrast, distributed mode can scale up while providing distribution and fault tolerance.
Recall that each connector or task is a thread, and we’re considering each to be approximately equal in terms of resource usage.
Connectors and tasks are auto-balanced across workers. Failures automatically handled by redistributing work, and you can easily scale the cluster up or down by adding more workers.
Cool implementation note: reuses group membership functionality of consumer groups. Note how if you replace “worker” with “consumer” and “task” with “topic partition”, the things it is doing look largely the same: assigning tasks to workers, detecting when a worker is added or fails, and rebalancing the work. Kafka already provides support for doing a lot of this, so by leveraging the existing implementation and coordinating through Kafka’s group functionality (with internal data stored in Kafka topics), Kafka Connect can provide this functionality in a relatively small code footprint.
Finally, note that Kafka Connect does not own the process management at all. We don’t want to make assumptions about using Mesos, YARN, or any other tool because that would unnecessarily limit Kafka Connect’s usage. Kafka Connect will work out of the box in any of these cluster management systems, or with orchestration tools, or if you just manage your processes with your own tooling.
In contrast, distributed mode can scale up while providing distribution and fault tolerance.
Recall that each connector or task is a thread, and we’re considering each to be approximately equal in terms of resource usage.
Connectors and tasks are auto-balanced across workers. Failures automatically handled by redistributing work, and you can easily scale the cluster up or down by adding more workers.
Cool implementation note: reuses group membership functionality of consumer groups. Note how if you replace “worker” with “consumer” and “task” with “topic partition”, the things it is doing look largely the same: assigning tasks to workers, detecting when a worker is added or fails, and rebalancing the work. Kafka already provides support for doing a lot of this, so by leveraging the existing implementation and coordinating through Kafka’s group functionality (with internal data stored in Kafka topics), Kafka Connect can provide this functionality in a relatively small code footprint.
All of this functionality can be accessed via REST API – submit connectors, see their status, update configs, and so on.
Finally, note that Kafka Connect does not own the process management at all. We don’t want to make assumptions about using Mesos, YARN, or any other tool because that would unnecessarily limit Kafka Connect’s usage. Kafka Connect will work out of the box in any of these cluster management systems, or with orchestration tools, or if you just manage your processes with your own tooling.
I want to mention two important features that also simplify both connector developer’s and user’s lives.
The first feature is offset management, which provides for standardized data delivery guarantees. Delivery guarantees are actually rarely provided in many other systems. They generally offer some sort of best effort, but unreliable, delivery. Ironically, stream processing frameworks often do a better job than tools specifically designed for data copying.
Kafka Connect handles offset checkpointing for connectors, and this fits in as a natural extension to Kafka’s offset commit functionality. For sources this works with offsets that have complex structure (e.g. timestamps + autoincrementing IDs in a database) and requires no implementation support from the connector beyond defining the offsets and being able to start reading from a saved offset. For sinks, we can leverage Kafka’s existing offset functionality, but in order to ensure data is completely written, sinks must also support a flush operation. Commits are automatically processed periodically. By default, this mode of managing offsets will provide at least once delivery; internally both sources and sinks are simply flushing all data to the output and the committing offsets.
Note that some connectors will opt out of this functionality in order to provide even stronger guarantees. For example, the HDFS connector manages its own offsets because (carefully) tracking them in HDFS along with the data allows for exactly-once delivery.
The second feature I want to mention are converters. Serialization formats may seem like a minor detail, but not separating the details of data serialization in Kafka from the details of source or sink systems results in a lot of inefficiency:
A lot of code for doing simple data conversions are duplicated across a large number of ad hoc connector implementations.
Each connector ultimately contains its own set of serialization options as it is used in more environments – JSON, Avro, Thrift, protobufs, and more.
Much like the serializers in Kafka’s producer and consumer, the Converters abstract away the details of serialization. Converters are different because they guarantee data is transformed to a common data API defined by Kafka Connect. This API supports both schema and schemaless data, common primitive data types, complex types like structs, and logical type extensions. By sharing this API, connectors write one set of translation code and Converters handle format-specific details. For example, the JDBC connector can easily be used to produce either JSON or Avro to Kafka, without any format-specific code in the connector.
With all these pieces you can see how we can tie together Kafka and Kafka Connect with stream processing frameworks and applications to not only simplify building these data pipelines and solve data integration challenges, but also transform how your company manages its data pipelines.
Kafka provides the central hub for real-time data and Kafka Connect simplifies operationalization: one service to maintain, common metrics, common monitoring, and agnostic to your choice of process and cluster management.
You can centrally managed Kafka Connect cluster running in distributed mode, and accessed via REST API, allowing your ops team to provide data integration as a service to your entire organization.
For developers who want to build a complex data pipeline, they can submit jobs to copy data into and out of Kafka – it’s zero coding (assuming a connector is available)
Then, they can easily leverage either the traditional clients or stream processing frameworks to transform that data. The output is stored back into another Kafka topic or served up directly.
As a side benefit, standardizing on Kafka encourages reuse of existing data (both raw and transformed). Providing this service not only makes it easy to build your *own* complex data pipeline, it encourages other people in the org to build on top of your existing work.
Confluent Platform also provides additional tools that make this setup even more powerful. For example, the schema registry controls the format of data in each topic, and besides ensuring data quality and compatibility, it also encourages decoupling of teams by allowing anyone to discover what data is in a topic, grab its schema, and immediately start utilizing that data without ever adding coordination overhead with another team.
A stream data platform built around Kafka and Kafka Connect allows you to scale to handle your entire organization’s real-time data, while maintaining simple management and easy operationalization of your data pipeline.
Kafka Connect provides the framework, but I want to spend a few minutes describing the current state of the connector ecosystem. While the framework ships with Apache Kafka, connectors use a federated approach to development. Confluent helped kick off connector development with a few key open source connectors – JDBC for importing data from any relational database and HDFS, for exactly once delivery of data into HDFS and Hive. Confluent will be continuing to add more open source connectors.
We’ve also started tracking connectors that the community has been developing on a page we’re calling the Connector Hub. We’ve already got a dozen or so connectors, and more are popping up every week. We’ll be working to make this index as useful to users as possible, offering information about the current state of the connector implementations and feature sets.
I also wanted to highlight a few of the winning connectors and applications that came out of the Stream Data Hackathon we held last night to help demonstrate the variety of systems Kafka Connect can be used with.
Finally, the Kafka Connect framework is only a few months old and there are lots more improvements and refinements in the pipeline. I want to highlight a few here.
The upcoming release of Kafka will include many improvements to the REST API, including better control over connectors, status tracking, standardized APIs for exposing configs, and improved metrics.
I emphasized earlier isolating copying data from transforming it. This is important to do, but there are some small, limited transformations which are sometimes important to perform *before* the data hits Kafka, for example removing personally identifiable information. We’re planning to add support for this in a way that won’t compromise the guarantees Kafka Connect provides and also doesn’t require connectors to duplicate the implementation of this functionality.
We’re also looking at leveraging both Kafka Connect and the new Kafka Streams library in a single application to enable a “data pipeline in an app”. The key enabling feature in Kafka Connect that will enable this is a sort of embedded mode which can scale up and down like a distributed cluster, but which you start and run from your own application. With both Kafka Connect and Kafka Streams able to scale up and down with more or fewer processes, your entire data pipeline can be easily scaled.
And, of course, we’re also actively working with the community to develop many more connectors
With that, I’d like to say thank for listening and I’d be happy to take any questions.