Publishing with Apache Kafka at The New York Times | Confluent
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
Introducing KSQL: Streaming SQL for Apache Kafka | Confluent
ksqlDB is the successor to KSQL. Read the announcement to learn more. To get started with ksqlDB in Confluent Cloud, you can sign up for fully managed Apache Kafka as a service and receive $400 of free usage during your first 60 days, plus an additional $60 of free usage when you use the promo code CL60BLOG.* I’m really excited to announce KSQL, a streaming SQL engine for Apache Kafka®. KSQL lower
Exactly-once Semantics is Possible: Here's How Apache Kafka Does it
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 idempotence and transactions features in Kafka enable correct exactly-once
Using logs to build a solid data infrastructure (or: why dual writes are a bad idea) | Confluent
This is an edited transcript of a talk I gave at the Craft Conference 2015. The video and slides are also available. How does your database store data on disk reliably? It uses a log. How does one database replica synchronise with another replica? It uses a log. How does a distributed algorithm like Raft achieve consensus? It uses a log. How does activity data get recorded in a system like Apache
Introducing the Kafka Consumer: Getting Started with the New Apache Kafka 0.9 Consumer Client | Confluent
When Apache Kafka® was originally created, it shipped with a Scala producer and consumer client. Over time we came to realize many of the limitations of these APIs. For example, we had a “high-level” consumer API which supported consumer groups and handled failover, but didn’t support many of the more complex usage scenarios. We also had a “simple” consumer client which provided full control, but
Apache Kafka, Samza, and the Unix Philosophy of Distributed Data | Confluent
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 hard way. Although computers have got faster, data has got bigge
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