Confluent Platform is supporting London Metal Exchange’s Kafka Centre of Excellence across a number of projects with the main objective to provide a reliable, resilient, scalable and overall efficient Kafka as a Service model to the teams across the entire London Metal Exchange estate.
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.
In the last few years, Apache Kafka has been used extensively in enterprises for real-time data collecting, delivering, and processing. In this presentation, Jun Rao, Co-founder, Confluent, gives a deep dive on some of the key internals that help make Kafka popular.
- Companies like LinkedIn are now sending more than 1 trillion messages per day to Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
- Many companies (e.g., financial institutions) are now storing mission critical data in Kafka. Learn how Kafka supports high availability and durability through its built-in replication mechanism.
- One common use case of Kafka is for propagating updatable database records. Learn how a unique feature called compaction in Apache Kafka is designed to solve this kind of problem more naturally.
Getting Started with Confluent Schema Registryconfluent
Getting started with Confluent Schema Registry, Patrick Druley, Senior Solutions Engineer, Confluent
Meetup link: https://www.meetup.com/Cleveland-Kafka/events/272787313/
An Introduction to Confluent Cloud: Apache Kafka as a Serviceconfluent
Business breakout during Confluent’s streaming event in Munich, presented by Hans Jespersen, VP WW Systems Engineering at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
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.
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 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
(Randall Hauch, Confluent) Kafka Summit SF 2018
The Kafka Connect framework makes it easy to move data into and out of Kafka, and you want to write a connector. Where do you start, and what are the most important things to know? This is an advanced talk that will cover important aspects of how the Connect framework works and best practices of designing, developing, testing and packaging connectors so that you and your users will be successful. We’ll review how the Connect framework is evolving, and how you can help develop and improve it.
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.
In the last few years, Apache Kafka has been used extensively in enterprises for real-time data collecting, delivering, and processing. In this presentation, Jun Rao, Co-founder, Confluent, gives a deep dive on some of the key internals that help make Kafka popular.
- Companies like LinkedIn are now sending more than 1 trillion messages per day to Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
- Many companies (e.g., financial institutions) are now storing mission critical data in Kafka. Learn how Kafka supports high availability and durability through its built-in replication mechanism.
- One common use case of Kafka is for propagating updatable database records. Learn how a unique feature called compaction in Apache Kafka is designed to solve this kind of problem more naturally.
Getting Started with Confluent Schema Registryconfluent
Getting started with Confluent Schema Registry, Patrick Druley, Senior Solutions Engineer, Confluent
Meetup link: https://www.meetup.com/Cleveland-Kafka/events/272787313/
An Introduction to Confluent Cloud: Apache Kafka as a Serviceconfluent
Business breakout during Confluent’s streaming event in Munich, presented by Hans Jespersen, VP WW Systems Engineering at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
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.
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 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
(Randall Hauch, Confluent) Kafka Summit SF 2018
The Kafka Connect framework makes it easy to move data into and out of Kafka, and you want to write a connector. Where do you start, and what are the most important things to know? This is an advanced talk that will cover important aspects of how the Connect framework works and best practices of designing, developing, testing and packaging connectors so that you and your users will be successful. We’ll review how the Connect framework is evolving, and how you can help develop and improve it.
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/
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe:
Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter.
Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka.
Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments.
Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka.
Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.
The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka.
Use Cases for Data in Motion in the Healthcare Industry:
- Know Your Patient (= “Customer 360”)
- Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.)
- IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc)
Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.
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/
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Full recorded presentation at https://www.youtube.com/watch?v=2UfAgCSKPZo for Tetrate Tech Talks on 2022/05/13.
Envoy's support for Kafka protocol, in form of broker-filter and mesh-filter.
Contents:
- overview of Kafka (usecases, partitioning, producer/consumer, protocol);
- proxying Kafka (non-Envoy specific);
- proxying Kafka with Envoy;
- handling Kafka protocol in Envoy;
- Kafka-broker-filter for per-connection proxying;
- Kafka-mesh-filter to provide front proxy for multiple Kafka clusters.
References:
- https://adam-kotwasinski.medium.com/deploying-envoy-and-kafka-8aa7513ec0a0
- https://adam-kotwasinski.medium.com/kafka-mesh-filter-in-envoy-a70b3aefcdef
With Apache Kafka 0.9, the community has introduced a number of features to make data streams secure. In this talk, we’ll explain the motivation for making these changes, discuss the design of Kafka security, and explain how to secure a Kafka cluster. We will cover common pitfalls in securing Kafka, and talk about ongoing security work.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Ozone is an object store for Hadoop. Ozone solves the small file problem of HDFS, which allows users to store trillions of files in Ozone and access them as if there are on HDFS. Ozone plugs into existing Hadoop deployments seamlessly, and programs like Hive, LLAP, and Spark work without any modifications. This talk looks at the architecture, reliability, and performance of Ozone.
In this talk, we will also explore Hadoop distributed storage layer, a block storage layer that makes this scaling possible, and how we plan to use the Hadoop distributed storage layer for scaling HDFS.
We will demonstrate how to install an Ozone cluster, how to create volumes, buckets, and keys, how to run Hive and Spark against HDFS and Ozone file systems using federation, so that users don’t have to worry about where the data is stored. In other words, a full user primer on Ozone will be part of this talk.
Speakers
Anu Engineer, Software Engineer, Hortonworks
Xiaoyu Yao, Software Engineer, Hortonworks
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.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Deploying Flink on Kubernetes - David AndersonVerverica
Kubernetes has rapidly established itself as the de facto standard for orchestrating containerized infrastructures. And with the recent completion of the refactoring of Flink's deployment and process model known as FLIP-6, Kubernetes has become a natural choice for Flink deployments. In this talk we will walk through how to get Flink running on Kubernetes
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
Common issues with Apache Kafka® Producerconfluent
Badai Aqrandista, Confluent, Senior Technical Support Engineer
This session will be about a common issue in the Kafka Producer: producer batch expiry. We will be discussing the Kafka Producer internals, its common causes, such as a slow network or small batching, and how to overcome them. We will also be sharing some examples along the way!
https://www.meetup.com/apache-kafka-sydney/events/279651982/
Developing Realtime Data Pipelines With Apache KafkaJoe Stein
Developing Realtime Data Pipelines With Apache Kafka. Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers. Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact. Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
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/
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe:
Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter.
Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka.
Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments.
Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka.
Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.
The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka.
Use Cases for Data in Motion in the Healthcare Industry:
- Know Your Patient (= “Customer 360”)
- Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.)
- IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc)
Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.
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/
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Full recorded presentation at https://www.youtube.com/watch?v=2UfAgCSKPZo for Tetrate Tech Talks on 2022/05/13.
Envoy's support for Kafka protocol, in form of broker-filter and mesh-filter.
Contents:
- overview of Kafka (usecases, partitioning, producer/consumer, protocol);
- proxying Kafka (non-Envoy specific);
- proxying Kafka with Envoy;
- handling Kafka protocol in Envoy;
- Kafka-broker-filter for per-connection proxying;
- Kafka-mesh-filter to provide front proxy for multiple Kafka clusters.
References:
- https://adam-kotwasinski.medium.com/deploying-envoy-and-kafka-8aa7513ec0a0
- https://adam-kotwasinski.medium.com/kafka-mesh-filter-in-envoy-a70b3aefcdef
With Apache Kafka 0.9, the community has introduced a number of features to make data streams secure. In this talk, we’ll explain the motivation for making these changes, discuss the design of Kafka security, and explain how to secure a Kafka cluster. We will cover common pitfalls in securing Kafka, and talk about ongoing security work.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Ozone is an object store for Hadoop. Ozone solves the small file problem of HDFS, which allows users to store trillions of files in Ozone and access them as if there are on HDFS. Ozone plugs into existing Hadoop deployments seamlessly, and programs like Hive, LLAP, and Spark work without any modifications. This talk looks at the architecture, reliability, and performance of Ozone.
In this talk, we will also explore Hadoop distributed storage layer, a block storage layer that makes this scaling possible, and how we plan to use the Hadoop distributed storage layer for scaling HDFS.
We will demonstrate how to install an Ozone cluster, how to create volumes, buckets, and keys, how to run Hive and Spark against HDFS and Ozone file systems using federation, so that users don’t have to worry about where the data is stored. In other words, a full user primer on Ozone will be part of this talk.
Speakers
Anu Engineer, Software Engineer, Hortonworks
Xiaoyu Yao, Software Engineer, Hortonworks
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.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Deploying Flink on Kubernetes - David AndersonVerverica
Kubernetes has rapidly established itself as the de facto standard for orchestrating containerized infrastructures. And with the recent completion of the refactoring of Flink's deployment and process model known as FLIP-6, Kubernetes has become a natural choice for Flink deployments. In this talk we will walk through how to get Flink running on Kubernetes
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
Common issues with Apache Kafka® Producerconfluent
Badai Aqrandista, Confluent, Senior Technical Support Engineer
This session will be about a common issue in the Kafka Producer: producer batch expiry. We will be discussing the Kafka Producer internals, its common causes, such as a slow network or small batching, and how to overcome them. We will also be sharing some examples along the way!
https://www.meetup.com/apache-kafka-sydney/events/279651982/
Developing Realtime Data Pipelines With Apache KafkaJoe Stein
Developing Realtime Data Pipelines With Apache Kafka. Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers. Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact. Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
New Features in Confluent Platform 6.0 / Apache Kafka 2.6Kai Wähner
New Features in Confluent Platform 6.0 / Apache Kafka 2.6, including REST Proxy and API, Tiered Storage for AWS S3 and GCP GCS, Cluster Linking (On-Premise, Edge, Hybrid, Multi-Cloud), Self-Balancing Clusters), ksqlDB.
Au delà des brokers, un tour de l’environnement Kafka | Florent Ramièreconfluent
During the Confluent Streaming event in Paris, Florent Ramière, Technical Account Manager at Confluent, goes beyond brokers, introducing a whole new ecosystem with Kafka Streams, KSQL, Kafka Connect, Rest proxy, Schema Registry, MirrorMaker, etc.
What is Apache Kafka and What is an Event Streaming Platform?confluent
Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent
Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.
Applying ML on your Data in Motion with AWS and Confluent | Joseph Morais, Co...HostedbyConfluent
Event-driven application architectures are becoming increasingly common as a large number of users demand more interactive, real-time, and intelligent responses. Yet it can be challenging to decide how to capture and perform real-time data analysis and deliver differentiating experiences. Join experts from Confluent and AWS to learn how to build Apache Kafka®-based streaming applications backed by machine learning models. Adopting the recommendations will help you establish repeatable patterns for high performing event-based apps.
Confluent Platform 5.5 + Apache Kafka 2.5 => New Features (JSON Schema, Proto...Kai Wähner
In the latest webinar of the Confluent Kitchen Tour, Kai Waehner gave an overview of the release Confluent Platform 5.5, which will make life easier for developers in particular: The improvements in data compatibility and updates to ksqlDB are just a few of many customizations.
Building an event-driven architecture with Apache Kafka allows the transition from traditional silos and monolithic applications to modern microservices and event streaming applications. These advantages bring with them an increased cross-industry demand for Kafka developers. The Dice Tech Salary Report recently ranked Kafka as the highest-paid technological skill of 2019 - last year it was second.
With Confluent Platform 5.5, we're making it even easier for developers to access Kafka and start building event-streaming applications, regardless of the preferred programming language or underlying data formats used in their applications.
This Online Talk will cover the key features of Confluent Platform 5.5, including
- Support for protobuf and JSON schemas in the Confluent Schema Registry and across the platform
- Exactly Once semantics for non-Java clients (C, C++, .NET., Golang, Python, REST Proxy)
- Administration Functions in the REST Proxy v3 (Preview)
- ksqlDB 0.7 and ksqlDB Flow View in the Conflent Control Center
Concepts and Patterns for Streaming Services with KafkaQAware GmbH
Cloud Native Night March 2020, Mainz: Talk by Perry Krol (@perkrol, Confluent)
=== Please download slides if blurred! ===
Abstract: Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but they provide a more holistic and compelling approach when applied together. In this session Confluent will provide insights how service-based architectures and stream processing tools such as Apache Kafka® can help you build business-critical systems. You will learn why streaming beats request-response based architectures in complex, contemporary use cases, and explain why replayable logs such as Kafka provide a backbone for both service communication and shared datasets.
Based on these principles, we will explore how event collaboration and event sourcing patterns increase safety and recoverability with functional, event-driven approaches, apply patterns including Event Sourcing and CQRS, and how to build multi-team systems with microservices and SOA using patterns such as “inside out databases” and “event streams as a source of truth”.
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.
Zero Down Time Move From Apache Kafka to Confluent With Justin Dempsey | Curr...HostedbyConfluent
Zero Down Time Move From Apache Kafka to Confluent With Justin Dempsey | Current 2022
Kafka has been a crucial facet of the overall SAS Customer Intelligence 360 (CI360) architecture for quite some time. Until 2021, Kafka supporting CI360 was managed on standalone virtual machines. Traditional VM backed infrastructure posed administrative challenges for ensuring consistent software patching, adding scale on demand, and providing a highly available, redundant, and durable message bus for the CI360 microservices.
The goal was clear, the backend Kafka platform needed to move from the aging legacy systems to a more cost effective and stable solution.
The standalone VM backed Kafka clusters were migrated to the Amazon Elastic Kubernetes Service (EKS) with zero down time. Cluster Linking and the Confluent Operator were used as part of this effort. Both technologies were crucial in ensuring that the systems were online and available throughout the migration.
This session details the journey for moving standalone Kafka to Kafka on K8S. During the session, scope of the journey including Total Cost of Ownership (TCO), technical architecture, and the migration itself will be discussed.
NOTE: Experiences related to this effort are being published in a joint case study between SAS and Confluent titled, ""SAS Powers Instant, Real-Time Omnichannel Marketing at Massive Scale with Confluent's Hybrid Capabilities"".
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
ndependent 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 me make sure that all these event 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 amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
Architecture patterns for distributed, hybrid, edge and global Apache Kafka d...Kai Wähner
Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments
Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception. This session gives an overview of several scenarios that may require multi-cluster solutions and discusses real-world examples with their specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments and global Kafka.
Key takeaways:
In many scenarios, one Kafka cluster is not enough. Understand different architectures and alternatives for multi-cluster deployments.
Zero data loss and high availability are two key requirements. Understand how to realize this, including trade-offs.
Learn about features and limitations of Kafka for multi cluster deployments
Global Kafka and mission-critical multi-cluster deployments with zero data loss and high availability became the normal, not an exception.
Similar to Set your Data in Motion with Confluent & Apache Kafka Tech Talk Series LME (20)
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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.
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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.
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
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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.
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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
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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.
Transforming applications built with traditional messaging solutions such as TIBCO, MQ and Solace to be scalable, reliable and ready for the move to cloud
How can applications built with traditional messaging technologies like TIBCO, Solace and IBM MQ be modernised and be made cloud ready? What are the advantages to Event Streaming approaches to pub/sub vs traditional message queues? What are the strengeths and weaknesses of both approaches, and what use cases and requirements are actually a better fit for messaging than Kafka?
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.
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Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
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During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
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- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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Defect reporting
SAP heatmap example with demo
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10. Apache Kafka is a Distributed Commit Log
Process streams of events
and produce new ones
In real time, as they occur
110101
010111
001101
100010
Publish and subscribe to
streams of events
Similar to a message queue
110101
010111
001101
100010
Store streams of events In a fault tolerant way
110101
010111
001101
100010
12
17. What happens inside a producer?
21
Producer
Producer Record
Topic
[Partition]
[Timestamp]
Value
Serializer Partitioner
Topic A
Partition 0
Batch 0
Batch 1
Batch 2
Topic B
Partition 1
Batch 0
Batch 1
Batch 2
Kafka
Broker
Send()
Retry
?
Fail
?
Yes
No
non-retriable
exception
success metadata
Yes
[Headers]
[Key]
18. Make Kafka
Widely Accessible
to Developers
Enable all developers to
leverage Kafka throughout
the organization with a wide
variety of Confluent clients
Confluent Clients
Battle-tested and high performing
producer and consumer APIs (plus
admin client)
20. Connect Any
Application to Kafka
REST Proxy
Non-Java
Applications
Native Kafka Java
Applications
Schema Registry
REST / HTTP
Allows third-party apps to
produce and consume
messages
Communicate via
HTTP-connected devices
Provides a RESTful
interface to a Kafka cluster
REST Proxy
21. 29
Admin REST APIs
Confluent Platform
introduces REST APIs for
administrative
operations to simplify
Kafka management
Admin REST APIs add even greater
flexibility in how you manage Kafka:
Describe, list, and configure brokers
Create, delete, describe, list, and configure
topics
Delete, describe, and list consumer groups
Create, delete, describe, and list ACLs
List partition reassignments
Confluent offers several options to run
admin operations, including Control
Center, the CLI, and Kafka clients...
22. REST Proxy: Key Features
API endpoints:
• Produce messages:
Topic : POST /topics/(string:topic_name)
Partition : POST /topics/(string:topic_name)/partitions/(int:partition_id)
• Consume messages (Note: requires stickyness to REST Proxy instance):
Consumer Group : GET /consumers/(string:group_name)/instances/(string:instance)/records
• Consumer group management:
Add/Remove Instances: POST /consumers/(string:group_name), DELETE
/consumers/(string:group_name)/instances/(string:instance)
Commit/Get Offsets : POST or GET /consumers/(string:group_name)/instances/(string:instance)/offsets
Modify Subscriptions: POST, GET or DELETE /consumers/(string:group_name)/instances/(string:instance)/subscription
Modify Assignments : POST or GET /consumers/(string:group_name)/instances/(string:instance)/assignments
Reposition : POST or GET /consumers/(string:group_name)/instances/(string:instance)/positions
• Get Metadata:
Topic : GET /topics, GET /topics/(string:topic_name)
Partition : GET /topics/(string:topic_name)/partitions/(int:partition_id)
Broker : GET /brokers
• Admin functions (preview):
Create Topic : POST /clusters/(string:cluster_id)/topics
Delete Topic: DELETE /clusters/(string:cluster_id)/topics/(string:topic_name)
List Topic Configs: Partition : GET /clusters/(string:cluster_id)/topics/(string:topic_name)/configs
30
24. Many sources without a policy
causes mayhem in a centralized
data pipeline
Ensuring downstream systems can
use the data is key to an operational
stream pipeline
Even within a single application,
different formats can be presented Incompatibly formatted message
The Challenge of
Data Compatibility
at Scale
App 03
App 02
App 01
32
26. Schema Registry: Key Features
• Manage schemas and enforce schema policies
Define, per Kafka topic, a set of compatible schemas that are “allowed”
Schemas can be defined by an admin or by clients at runtime
Avro, Protobuf, and JSON schemas all supported
• Automatic validation when data is written to a topic
If the data doesn’t match the schema, the producer gets an error
• Works transparently
When used with Confluent Kafka clients, Kafka REST Proxy, and Kafka Streams
• Integrates with Kafka Connect
• Integrates with Kafka Streams
• Supports high availability (within a datacenter)
• Supports multi-datacenter deployments
34
28. Kafka Connect
No-Code way of connecting known systems (databases, object storage, queues, etc)
to Apache Kafka
Some code can be written to do custom transforms and data conversions though
maybe out of the box Single Message Transforms and Converters exist
Kafka Connect Kafka Connect
Data
sources
Data
sinks
29. Kafka
Cluster
Kafka Connect
Durable Data
Pipelines
Schema
Registry
Worker
Integrate upstream and
downstream systems with Apache
Kafka®
• Capture schema from sources, use
schema to inform data sinks
• Highly Available workers ensure
data pipelines aren’t interrupted
• Extensible framework API for
building custom connectors
Kafka Connect
Worker
Worker
Worker
30. Instantly Connect Popular Data Sources & Sinks
Data Diode
210+
pre-built
connectors
90+ Confluent Supported 60+ Partner Supported, Confluent Verified
31. Confluent HUB
Easily browse connectors by:
• Source vs Sinks
• Confluent vs Partner supported
• Commercial vs Free
• Available in Confluent Cloud
confluent.io/hub
Instantly Connect
Popular Data
Sources & Sinks
34. Stream Processing by Analogy
46
Kafka Cluster
Connect API Stream Processing Connect API
$ cat < in.txt | grep "ksql" | tr a-z A-Z > out.txt
35. Kafka Streams
Scalable Stream
Processing
Build scalable, durable
stream-processing services with
the Kafka Streams Java Library
• Simple functional API
• Powerful Processing API
• No Framework needed, it’s a
Library, use it and deploy it as any
other JVM Library
builder.stream(inputTopic)
.map((k, v) ->
new KeyValue<>(
(String) v.getAccountId(),
(Integer) v.getTotalValue())
)
.groupByKey()
.count()
.toStream().to(outputTopic);
36. Where does the processing code run?
49
Brokers?
Nope!
App
Streams
API
Same app, many instances
App
Streams
API
App
Streams
API
37. Leverages Consumer Group Protocol
50
App
Streams
API
Same app, many instances
App
Streams
API
App
Streams
API
39. Stream Processing in Kafka
52
Flexibility Simplicity
Producer/Consume
r
Kafka Streams API
● subscribe()
● poll()
● send()
● flush()
● filter()
● map()
● join()
● aggregate()
ksqlDB
● Select…from…
● Join…where…
● Group by..
40. ksqlDB provides one solution for capturing events, stream processing, and serving both push
and pull queries
Simplify Your Stream Processing Architecture
DB
APP
APP
DB
PULL
PUSH
CONNECTORS
STREAM
PROCESSING
STATE STORES
ksqlDB
1 2
APP
41. Streaming app with 4 SQL statements
59
Serve lookups against
materialized views
Create
materialized views
Perform continuous
transformations
CREATE SOURCE CONNECTOR jdbcConnector WITH (
‘connector.class’ = '...JdbcSourceConnector',
‘connection.url’ = '...',
…);
CREATE STREAM purchases AS
SELECT viewtime, userid,pageid,
TIMESTAMPTOSTRING(viewtime, 'yyyy-MM-dd HH:mm:ss.SSS')
FROM pageviews;
CREATE TABLE orders_by_country AS
SELECT country, COUNT(*) AS order_count, SUM(order_total) AS order_total
FROM purchases
WINDOW TUMBLING (SIZE 5 MINUTES)
LEFT JOIN purchases ON purchases.customer_id = user_profiles.customer_id
GROUP BY country
EMIT CHANGES;
SELECT * FROM orders_by_country WHERE country='usa';
Capture data
43. Confluent
Control Center
The simplest way to operate
and build applications with
Apache Kafka
For Operators
Centrally manage and monitor
multi-cluster environments and security
For Developers
View messages, topics and schemas,
manage connectors and build ksqlDB
queries
44. Messages
Browse messages, and search
offsets or timestamps by
partition
Topics
Create, edit, delete and view all
topics in one place
Schemas
Create, edit and view topic
schemas, and compare schema
versions
Accelerate Application Development and
Integration
45. Adhere to Established
Event Streaming SLAs
Monitor and optimize
system health
• Broker and ZooKeeper uptime
• Under replicated partitions
• Out of sync replicas
• Disk usage and distribution
• Alerting
Broker overview
Cluster overview
48. What is Health+?
● Intelligent alerts: manage Health+
intelligent alerts via Confluent Cloud’s UI
● Accelerated Confluent Support:
Support uses the performance
metadata to help you with questions or
problems even faster.
● Monitoring dashboards: view all of your
critical metrics in a single cloud-based
dashboard.
● Confluent Telemetry Reporter: send
performance metadata back to
Confluent via Confluent Telemetry
Reporter plugin.
49. Intelligent alerts There are 50+ alerts available today (with
many more to come), including:
● Request handler idle percentage
● Network processor idle percentage
● Active controller count
● Offline partitions
● Unclean leader elections
● Under replicated partitions
● Under min in-sync replicas
● Disk usage
● Unused topics
● No metrics from cluster in one hour
50. How does it work ?
● Confluent Telemetry Reporter is a plugin
that runs inside each Confluent
Platform service (only brokers at the
moment) to push metadata about the
service to Confluent
● Data is sent over HTTP using an
encrypted connection, once per minute
by default
● _confluent-telemetry-metrics topic
is where metrics are stored
55. 75
Tiered Storage
Tiered Storage enables
infinite data retention
and elastic scalability by
decoupling the compute
and storage layers in
Kafka
Event Streaming is storage-intensive:
...
Micro-
service
...
SFDC App
Splunk
...
Device
Logs
Object
Storage
Main-
frame
...
Hadoop
Data Stores
3rd Party Apps Custom Apps /
Microservices
Logs
56. 76
Tiered Storage
Tiered Storage enables
infinite data retention
and elastic scalability by
decoupling the compute
and storage layers in
Kafka
Tiered Storage allows Kafka to
recognize two layers of storage:
Brokers
Cost-effective
Object Storage
Offload old data
to object store
57. 77
Tiered Storage
Tiered Storage enables
infinite data retention
and elastic scalability by
decoupling the compute
and storage layers in
Kafka
Tiered Storage delivers three primary
benefits that revolutionize the way
our customers experience Kafka:
Infinite data retention
Reimagine what event streaming apps can do
Reduced infrastructure costs
Offload data to cost-effective object storage
Platform elasticity
Scale compute and storage independently
59. 80
Multi-Region
Cluster (MRC)
A cluster stretched across multiple
regions that can replicate synchronously
and asynchronously. It requires 3 data
centers/regions minimum (at least for
zookeeper).
It is offset preserving and has automatic
client failover with no custom code.
Note: A rack can only have synchronous
or asynchronous replicas for a topic, not
both. But you can have multiple racks in
a DC/Zone
61. 84
Cluster Linking
Cluster Linking allows you to directly connect
clusters together and mirror topics from one
cluster to another without the need for Connect.
Cluster Linking makes it much easier to build
multi-datacenter, multi-cluster, and hybrid
cloud deployments.
62. Sharing data between independent
clusters or migrating clusters presents
two challenges:
1. Requires deploying a separate Connect
cluster
2. Offsets are not preserved, so messages
are at risk of being skipped or reread
85
Cluster Linking
Cluster Linking
simplifies hybrid cloud
and multi-cloud
deployments for Kafka
1
2
0 1 2 3 4 ...
4 5 6 7 8 ...
Topic 1, DC 1:
Topic 1, DC 2:
DC 1: DC 2:
63. 86
Cluster Linking
Cluster Linking
simplifies hybrid cloud
and multi-cloud
deployments for Kafka
Cluster Linking requires no additional
infrastructure and preserves offsets:
Migrate
clusters to
Confluent
Cloud