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A short introduction of Apache Beam

Introduction

Once upon a time, in the wide Big Data’s world, several frameworks and runtime environments existed to design, code and execute ETL , streaming and batch tasks. It was very complicated for developers and architect to wisely choose the good one corresponding to the use case. It should be evolutive and could scale effectively in production. There were Apache SPARK SQL for queries, Apache Flink for real-time streaming, Hadoop Map reduce for batch usages and so on. In this way, it was very complicated and a bit overwhelming to pick and deploy in production the right tool for the right job.

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How Regular Expressions within IDE saved my time

Recently, I had a use case in one of my project which seemed quite painful but was ultimately really easy thanks to modern Integrated Development Environments and regular expressions. This case was quite specific to my needs, and the solution was to use something I was used to. All in all, you might think the solution was obvious after reading this article, yet I think it is worth detailing.

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