Data engineering articles

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HyperLogLog explained

Counting the number of distinct elements can appear a simple task in classical web service-based applications. After all, we usually have to deal with a small subset of data that simply fits in memory and can be automatically counted with the data structures as sets. But the same task is less obvious in Big Data applications where the approximation algorithms can come to the aid.

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Dynamo paper and consistent hashing

One of previous posts presented partitioning strategies. Among described techniques we could find hashing partitioning based on the number of servers. The drawback of this method was the lack of flexibility. With the add of new server we have to remap all data. Fortunately an alternative to this "primitive" hashing exists and it's called consistent hashing.

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Data partitioning strategies

Every data processing pipeline can have a source of contention. One of them can be the data localization. When all entries are read from single place by dozens or hundreds of workers, the data source can respond slower. One of solutions to this problem can be the partitioning.

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Enforcing consistency in stateful serverless processing with idempotence

Since the gain of popularity of cloud operators, serverless processing became one of serious alternatives to the cluster-based data pipelines. It's often cheaper to have event-based applications than different processings in the clusters. However, using serverless (and not only) in distributed and stateful computing can sometimes be difficult. But often one property can help in a lot of problems - idempotence.

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Zeta architecture

Almost every year new concept of data-centric architecture appears. In 2014 Kappa conception was published by Jay Kreps. One year after another concept emerged - the architecture called Zeta.

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Big Data tools

Previously we discovered two popular architectures in Big Data systems - lambda and kappa. Because it was new and pretty long concepts to explain, we expressly ignored tools.

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Big Data architectures

When some years ago I done a small POC Hadoop/MapReduce project based on a million song dataset (my old blog in French), I expressly omitted the part about architecture. It was a mistake because correctly designed architecture is as important as code written behind.

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Big Data glossary

Originally, Big Data can seem to be strictly related to one tool - Hadoop. However, it's a misunderstanding of the concept because it hides more interesting stuff.

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