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Learn about the 5 most common mistakes deploying Apache Flink, and how you can avoid them from Flink co-creator and PMC member Robert Metzger. Robert will be joined by Decodable CEO and streaming industry veteran Eric Sammer who'll demo some of the most common stream processing patterns using SQL in a form that you can reproduce yourself in minutes.
Decodable Platform Overview
I joined Decodable about a year ago as one of the founding engineers. One year on, we’ve built a self-service real-time data platform supporting hundreds of accounts in multiple regions. In this blog post, I’ll share why we built Decodable, the design decisions we made, and the platform architecture.
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Flink Deployments At Decodable
Decodable’s platform uses Apache Flink to run our customers’ real-time data processing jobs. This blog post explores how we securely, reliably and efficiently manage the underlying Flink deployments at Decodable in a multi-tenant environment.
The Top 5 Streaming ETL Patterns
ETL and ELT are traditionally scheduled batch operations, but as the need for always-on, always-current data services becomes the norm, realtime ELT operating on streams of data is the goal of many organizations - if not the reality, yet.In real world usage, the ‘T’ in ETL represents a wide range of patterns assembled from primitive operations. In this blog we’ll explore these operations and see examples of how they’re implemented as SQL statements.
Not All Flink is Created Equal: Comparing Kinesis Data Analytics with Decodable
Flink is an amazing technology; the power to process real-time data on the stream is something that everyone running Kafka, Pulsar, Kinesis and other popular messaging platforms will want to use eventually. In this blog and video we'll show the relative experience of two Flink-based cloud services; Decodable and Kinesis Data Analytics.
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