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Data architecture strategy for data quality

IBM Journey to AI blog

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving.

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Unfolding the Details of Hive in Hadoop

Pickl AI

These work together to enable efficient data processing and analysis: ยท Hive Metastore It is a central repository that stores metadata about Hive’s tables, partitions, and schemas. It applies the data structure during querying rather than data ingestion.