The Growth of Self Service Streaming Platforms and Flink SQL at Flink Forward Berlin

Companies increasingly understand how critical streaming data is to success and are looking for better, more self service ways to democratize streaming data across their organization.

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Joining Kafka Streams Using SQL to Enrich and Route Data

Joins are an important and powerful part of the SQL language. You can perform joins in SQLStreamBuilder to enrich data and create net new streams of useful data.

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SQLStreamBuilder October Feature Update

Our mission has remained clear—to build the best way to create, manage stream processing jobs using SQL, so you can work with your Kafka clusters, databases, and processing logic like the databases you know.

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SQLStreamBuilder Feature Update

I thought I would share a couple of improvements and features we recently added to SQLStreamBuilder. If you aren’t familiar, SQLStreamBuilder allows you to run Streaming SQL jobs against streams of data—initially on Kafka.

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Introducing SQLStreamBuilder

Today, we are launching SQLStreamBuilder (SSB): an interface to declare stream processing jobs using SQL. SQLStreamBuilder provides a powerful interface for writing SQL as well as managing SQL jobs.

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Apache Flink: Checkpoints And Savepoints

To understand the value of Apache Flink, it’s still important to know the difference between a checkpoint and a savepoint. These mechanisms for saving state are similar in design but used for two different purposes.

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A 2018 Year In Review: Massive Growth And Exciting Moves

With 2018 wrapping up, I wanted to talk a bit about this last year. This year, and the massive growth we’ve enjoyed, has been a rollercoaster of the best kind for the entire team.

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The Flink Table And SQL API With Apache Flink 1.6

Learn more about what (awesome) changes Flink 1.6 brought to the Flink table and SQL API with a step-by-step guide for using them.

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The New System Of Record

The defacto system-of-record for the enterprise is moving to a distributed log architecture, and the movement is well underway.

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The Eventador Stream Processing Stack

Eventador allows you to quickly build and manage modern streaming data workflows on top of these state of the art platforms – Kafka and Flink.

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Flink, JSON and Twitter

While Twitter and WordCount are probably two of the most common ways to get started with streaming data. In this post, I’m going to explore some techniques to normalize, window, and introspect the data.

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The Flink Table And SQL API

Apache Flink offers two simple APIs for accessing streaming data with declarative semantics – the table and SQL APIs. We dive in and build a simple processor in Java using these relatively new APIs.

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From Zero To Stream Processing

This week, I gave a talk at the Austin Kafka/Stream Processing Meetup. It was a great time and we had a fantastic turnout. I wanted to share the slides, examples, and a couple of thoughts.

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Apache Flink® On Eventador.Io

With the addition of Apache Flink – Eventador.io has a true end-to-end enterprise-grade stream processing platform. We run the complex infrastructure and provide support, you can focus on your streaming code.

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