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The data Artisans Blog

Apache Flink, stream processing, event-driven applications, and more.

Author: Markos Sfikas

Stream processing: An Introduction to Event Time in Apache Flink

Apache Flink supports multiple notions of time for stateful stream processing. This post focuses on event time support in Apache Flink. In the following sections we define what event time is with Apac...

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Broadcast State Pattern in Apache Flink: 4 important considerations

This episode of  Flink Friday Tip explores the Broadcast State pattern that was introduced in Apache Flink 1.5.0. In the following sections, we describe what is a Broadcast State Pattern, how Broadca...

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3 differences between Savepoints and Checkpoints in Apache Flink

This episode of our Flink Friday Tip explains what Savepoints and Checkpoints are and examines the main differences between them in Apache Flink. In the following paragraphs, we explain what Savepoint...

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Stateful Stream Processing: Apache Flink State Backends

This episode of our Flink Friday Tip explores stateful stream processing and more precisely the different state backends available in Apache Flink. In the following sections, we present the 3 state ba...

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How Apache Flink manages Kafka consumer offsets

In this episode of our Flink Friday Tip, we explain with a step-by-step example how Apache Flink works with Apache Kafka to ensure that records from Kafka topics are processed with exactly-once guaran...

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An introduction to ACID guarantees and transaction processing

Last month we introduced data Artisans Streaming Ledger, our new technology that brings serializable, distributed ACID transactions directly on data streams. In this blog post, we take a step back to ...

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