Podcast: The Anatomy of Successful Streaming Data Teams with special guest Jesse Anderson
In this episode of the Eventador Streams podcast, we had the pleasure of chatting with Jesse Anderson, managing director of the Big Data Institute and author of the upcoming book “Data Teams,” about what the anatomy of a successful streaming data team looks like.
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Podcast: All Things Apache Flink and Why Its Popularity is Rising
In the latest episode of Evenatdor Streams, Kenny and I sat down to chat about a topic that’s fairly central to many of the conversations we have here at Eventador: Apache Flink.
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Unlocking Kafka’s Value for Machine Learning and Data Science Pipelines
It’s a topic that’s come up for us with both current customers and in other conversations we have—data science teams are hungry for better access to Kafka-based streaming data.
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Eventador Professional Services for Streaming Systems
We’re taking our hands-on approach to managing customers’ Apache Flink and Kafka implementations a step further by offering consulting services to help organizations build, scale, and optimize their streaming systems.
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Introducing Materialized Views on Data Streams with the Eventador Platform 2.0
The release of the Eventador Platform v2.0 introduces a significant new capability – the creation of materialized views against streams of data. This new capability closes the gap between streaming platforms and applications.
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Flink, SQL, and More with the Community at Flink Forward San Francisco
We’re gearing up for Flink Forward next month and are excited for what we know is shaping up to be a stellar lineup of sessions! We always enjoy the conversations we have with the Apache Flink community.
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Fraud Detection and Analysis With Flink and Kafka Using the Eventador Platform
There are a number of mechanisms to build fraud and risk engines that can be employed in modern stream processing paradigms and on the Eventador Platform.
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What is Continuous SQL? Stream Processing with Continuous SQL Explained.
It has become clear that streaming or real-time data has more value than data at rest. Continuous SQL enables organizations to deliver on the promise of, and handle the pressures of, streaming data.
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Top 3 Reasons Why Kafka + SQL is an Amazing Combination
The combination of Kafka and SQL can be a game-changer for your business. It simplifies streaming deployments, accelerates streaming app time-to-market, and helps you to unlock increased value from your Kafka deployment.
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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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Javascript Functions in Flink with SQLStreamBuilder
Javascript Functions allow you to create arbitrary functions and call them directly from SQL. You con’t need to restart your system, stop your cluster, or compile/recompile anything. Just specify a function and get to business.
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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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