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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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Kumori: an overlay to reinforce inter-datacenter connections resiliency

Networking: an essential aspect of cloud computing infrastructures

If you landed on this technical blog, it shouldn’t be a surprise to you if I tell you that cloud computing is the major trend that has shaped the IT world since its appearance in the form of Amazon’s “pay as you go” computing platform offerings. From then on, Cloud computing delivers its users and customers flexibility and adaptability to demand. Cloud services providers also promise a better resilience in the event of a failure, arguing that as their platform exists in datacenters distributed all around the world, their infrastructure can cope with large failures in a better way than a standalone computing rack that companies’ IT departments might run on their premises.

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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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