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Wherever you are in your data streaming journey, you'll find the explainer videos and tutorials you need to advance your skills here.
Currently, Apache Kafka® uses Apache ZooKeeper™ to store its metadata. Data such as the location of partitions and the configuration of topics are stored outside of Kafka itself, in a separate ZooKeeper cluster. In 2019, we outlined a plan to break this dependency and bring metadata management into Kafka itself. So what is the problem with ZooKeeper? Actually, the problem is not with ZooKeeper its
Everybody is talking about creating an agile and flexible architecture with microservices, a term that is used today in many different contexts. Although microservices are not a free lunch, they do provide many benefits, including decoupling. Decoupling is the process of organizing a system around business capabilities to form an architecture that is decentralized. Smart endpoints and dumb pipes e
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Should You Put Several Event Types in the Same Kafka Topic? If you adopt a streaming platform such as Apache Kafka, one of the most important questions to answer is: what topics are you going to use? In particular, if you have a bunch of different events that you want to publish to Kafka as messages, do you put them in the same topic, or do you split them across different topics? The most importan
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At The New York Times we have a number of different systems that are used for producing content. We have several Content Management Systems, and we use third-party data and wire stories. Furthermore, given 161 years of journalism and 21 years of publishing content online, we have huge archives of content that still need to be available online, that need to be searchable, and that generally need to
Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications.
What does it even mean to query streaming data, and how does this compare to a SQL database? Well, it’s actually quite different to a SQL database. Most databases are used for doing on-demand lookups and modifications to stored data. KSQL doesn’t do lookups (yet), what it does do is continuous transformations— that is, stream processing. For example, imagine that I have a stream of clicks from use
This post was originally published in 2017 and last updated March 2025. I’m thrilled that we have hit an exciting milestone the Apache Kafka® community has long been waiting for: we have introduced exactly-once semantics in Kafka in the 0.11 release and Confluent Platform 3.3. In this post, I’d like to tell you what Kafka’s exactly-once semantics mean, why it is a hard problem, and how the new id
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Let’s take an example. Consider a Facebook-like social networking app (albeit a completely hypothetical one) that updates the profiles database when a user updates their Facebook profile. There are several applications that need to be notified when a user updates their profile — the search application so the user’s profile can be reindexed to be searchable on the changed attribute; the newsfeed ap
How to build retrieval-augmented generation (RAG) for real-time Generative AI applications with a data streaming platform
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Apache Kafka, Samza, and the Unix Philosophy of Distributed Data One of the things I realised while doing research for my book is that contemporary software engineering still has a lot to learn from the 1970s. As we’re in such a fast-moving field, we often have a tendency of dismissing older ideas as irrelevant – and consequently, we end up having to learn the same lessons over and over again, the
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