Como aprendi Apache Spark: revisitando uma jornada pela Engenharia de Dados
Quando comecei a estudar Apache Spark, o ecossistema de Big Data era muito diferente do que conhecemos hoje. Hadoop e MapReduce dominavam boa parte das conversas sobre processamento distribuído. Trabalhar com grandes volumes de dados normalmente significava pensar em clusters, HDFS, processamento em lote e muitas operações de leitura e escrita em disco. > Foi nesse cenário que o Spark começou a…
This brief summarizes a personal journey through the evolution of Apache Spark, highlighting the changes and enduring concepts in the field of data engineering. The author, who is a professional in the field, revisits their past materials on Apache Spark to understand what has aged and what remains relevant today. The brief emphasizes that while the APIs and some abstractions have evolved, fundamental concepts like distributed processing, partitioning, transformations, and lazy evaluation continue to be essential.
The author also notes that some of the content uses older versions of Spark and APIs that have since been replaced or significantly evolved, and they will clarify these differences by explaining the original functionality, the concept behind it, and the modern approach to the same problem. The piece concludes by acknowledging that legacy code is included to provide historical context, illustrating the journey from Hadoop MapReduce to Spark and the importance of understanding this evolution for modern data engineering practices.
Brief written by urgent.news from Dev.to's own syndicated text. Machine-written — may contain errors; check the original before relying on it.
