Data Driven Enterprise with Apache Kafka
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Presented at AWS Public Sector Summit on June 20, 2018
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Are events the new API? Event driven systems provide some unique properties, particularly for microservice architectures, as they can be used both for notification as well as for state transfer. This lets systems run in a broad range of use cases that cross geographies, clouds and devices.
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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/
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Kafka Streams as Foundation for KSQL
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Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
-Find out how Kafka delivers on a 5-second service-level agreement (SLA) for inside branch tellers.
-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
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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/
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In this talk, I will argue that by designing our systems around a distributed log like Apache Kafka®, we can in fact achieve ACID semantics at scale. We can ensure that distributed write operations can be applied atomically, consistently, in isolation between services, and of course with durability. What seems to be a counterintuitive conclusion ends up being straightforwardly achievable using existing technologies, as an elusive set of properties becomes relatively easy to achieve with the right architectural paradigm underlying the application.
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In this workshop we will set up a streaming framework which will process realtime data of traffic sensors installed within the Belgian road system.
Starting with the intake of the data, you will learn best practices and the recommended approach to split the information into events in a way that won't come back to haunt you.
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Can Apache Kafka Replace a Database? – The 2021 Update | Kai Waehner, Confluent
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 an 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.
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Presenter: Tim Berglund, Senior Director of Developer Experience, Confluent
It has become a truism in the past decade that building systems at scale, using non-relational databases, requires giving up on the transactional guarantees afforded by the relational databases of yore. ACID transactional semantics are fine, but we all know you can’t have them all in a distributed system. Or can we?
In this talk, I will argue that by designing our systems around a distributed log like Apache Kafka®, we can in fact achieve ACID semantics at scale. We can ensure that distributed write operations can be applied atomically, consistently, in isolation between services, and of course with durability. What seems to be a counterintuitive conclusion ends up being straightforwardly achievable using existing technologies, as an elusive set of properties becomes relatively easy to achieve with the right architectural paradigm underlying the application.
Introduction to KSQL: Streaming SQL for Apache Kafka®
Join Tom Green, Solution Engineer at Confluent for this Lunch and Learn talk covering KSQL. Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafka®. It provides an easy-to-use, yet powerful interactive SQL interface for stream processing on Kafka, without the need to write code in a programming language such as Java or Python. KSQL is scalable, elastic, fault-tolerant, and it supports a wide range of streaming operations, including data filtering, transformations, aggregations, joins, windowing, and sessionization.
By attending one of these sessions, you will learn:
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Speaker: Neil Avery, Technologist, Office of the CTO, Confluent
Stream processing is now at the forefront of many company strategies. Over the last couple of years we have seen streaming use cases explode and now proliferate the landscape of any modern business.
Use cases including digital transformation, IoT, real-time risk, payments microservices and machine learning are all built on the fundamental that they need fast data and they need it at scale.
Apache Kafka® has long been the streaming platform of choice, its origins of being dumb pipes for big data have long since been left behind and now it is the goto-streaming platform of choice.
Stream processing beckons as being the vehicle for driving those streams, and along with it brings a world of real-time semantics surrounding windowing, joining, correctness, elasticity, and accessibility. The ‘current state of stream processing’ walks through the origins of stream processing, applicable use cases and then dives into the challenges currently facing the world of stream processing as it drives the next data revolution.
Neil is a Technologist in the Office of the CTO at Confluent, the company founded by the creators of Apache Kafka. He has over 20 years of expertise of working on distributed computing, messaging and stream processing. He has built or redesigned commercial messaging platforms, distributed caching products as well as developed large scale bespoke systems for tier-1 banks. After a period at ThoughtWorks, he went on to build some of the first distributed risk engines in financial services. In 2008 he launched a startup that specialised in distributed data analytics and visualization. Prior to joining Confluent he was the CTO at a fintech consultancy.
Watch the recording: https://videos.confluent.io/watch/rmU6GHrd4EKFaZrRhdTE3s?.
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Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Pivotal Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value.
Apache Kafka® is providing developers a critically important component as they build and modernize applications to cloud-native architecture.
This talk will explore:
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This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
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Kafka, many times is just a piece of the stack that lives in production that often times no one wants to touch – because it just works. At AppsFlyer, a mobile attribution and analysis platform that generates a constant “storm” of 70B+ events (HTTP Requests) daily, Kafka sits at the core of our infrastructure. Recently I inherited the daunting task of managing our Kafka operation and discovered a lot of technical debt we needed to recover from if we wanted to be able sustain our next phase of growth. This talk will dive into how to safely migrate from outdated versions, how to gain trust with developers to migrate their production services, how to manage and monitor the right metrics and build resiliency into the architecture, as well as how to plan for continued improvements through paradigms such as sleep-driven design, and much more.
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Pandora’s Lawrence Weikum will discuss what they’ve done to bring real-time data integration to the team. We’ll review their Kafka-powered data pipelines and how they make the most of Kafka’s Connect API to make it surprisingly system to keep systems in sync.
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Lawrence Weikum, Senior Software Engineer, Pandora
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The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.
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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
Data In Motion Paris 2023
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
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Data Driven Enterprise with Apache Kafka
- 1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Bill McLaren Streaming Data Engineer, Confluent Data Driven Enterprise Real-time Streaming with Apache Kafka®
- 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. 80% of active Kafka Committers Founded September 2014 Technology developed while at LinkedIn Founded by the creators of Apache Kafka About Confluent™ Jay Kreps CEO Cheryl Dalrymple CFO Todd Barnett VP WW Sales Neha Narkhede CTO, VP Engineering
- 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Infrastructure Driven Enterprise
- 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Challenges In Infrastructure Driven Enterprise Divergent • “Normalization” • Combined & Derived • Which one is the source of truth? • What happens if any attributes change? Duplicate • Applications • Orgs • Data centers Slow • Batch processes • Multiple hops add up • Slow in propagating changes Serial • How many hops? • How many different processes? • What happens if one breaks
- 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What is the cost Agility Cost Success
- 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data Driven Enterprise Collapse the space between the data and the mission Time | Distance
- 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data Driven Enterprise Think Event Streaming
- 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Apache Kafka: A Distributed Event Streaming Platform
- 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data Driven Enterprise You have probably seen a diagram like that before
- 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Apache Kafka: An Event Streaming Platform Process streams of data in real time, as they occur 110101 010111 001101 100010 Publish and subscribe to streams of data similar to a message queue or enterprise messaging system 110101 010111 001101 100010 Store streams of data in a distributed highly available fault tolerant way 110101 010111 001101 100010
- 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Open Open Source Multi Cloud & On Premise Explosive Ecosystem
- 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Confluent Platform: a Streaming Platform based on Apache Kafka
- 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Please complete the session survey in the summit mobile app.
- 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Thank you!