The Eventador Blog | Page 2 of 4 | Insights Into Streaming Data

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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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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