Anatomy of a data driven architecture - Tamir Dresher
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The document outlines the anatomy of a data-driven architecture, emphasizing the significance of data for decision-making and product recommendations. It discusses various data processing architectures like ETL, ELT, and streaming, along with platforms for data integration and real-time processing. Additionally, it covers the components of data ingestion, transformation, storage, and consumption, highlighting tools and frameworks used in these processes.
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Editor's Notes
- #4: Data comes from many sources and feed our application We need it for the OLTP obviously but being the new gold we can use the data feed to and historical data to gain more insights BI ML/AI data-driven decision making (analytic systems) and drive data-powered products
- #5: Traditionally , orgs moved the data from the OLTP to the OLAP (DWH) with ETL Modern architectures now relies on ELT for batched load And to get the best real time response streaming is the way to go
- #6: Traditionally , orgs moved the data from the OLTP to the OLAP (DWH) with ETL Modern architectures now relies on ELT for batched load And to get the best real time response streaming is the way to go
- #8: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-to-build-a-data-architecture-to-drive-innovation-today-and-tomorrow#
- #17: https://leventov.medium.com/comparison-of-the-open-source-olap-systems-for-big-data-clickhouse-druid-and-pinot-8e042a5ed1c7