Normalizing JSON Data in Kafka Topics? SQLStreamBuilder Input Transforms to the Rescue
Input filters allow you to write a javascript function that operates on each message after it’s consumed from Kafka (or any other source) but before you write SQL against it.
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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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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 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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Kafka SASL Authentication
Core support for Simple Authentication and Security Layer (SASL) was added to Apache Kafka in the 0.10.2 release. We are excited to add this authentication mechanism to the Eventador service. Here is how it works.
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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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Planestream – The ADS-B Datasource
When we started Eventador.io in 2016 we needed a simple data source to help us build the platform on. We needed something that exemplified streaming data, massively dynamic, and with a lot of data.
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Microservices Enable Our New Enterprise Plan
Customer began asking for additional deployment options: they wanted the scalability and performance of Kafka teamed with the compute capabilities of Flink, and the support, tooling, management, and ease of use of Eventador.
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0.9: Teams, Github, Plans And More
This release focuses on making the service even more robust, easier to use, and overall customer experience. Many of these features were inspired by direct feedback from you, our customers.
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0.8: PrestoDB & Eventador.io
Since the very first release of Eventador.io, we have had a SQL interface. We believe that SQL is an incredible language for dealing with streaming data and it opens up an ecosystem of utilities.
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0.7 Release And Looking Towards 1.0
Eventador 0.7 is out! We pushed a number of bug fixes and minor improvements that will make our platform more powerful and easy to use.
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