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Summary Databases and analyticsarchitectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. How has that changed the architectural approach to CDPs?
Evolution of the data landscape 1980s — Inception Relational databases came into existence. Databases were overwhelmed with transactional and analytical workloads. Result: Datawarehouse was born. Image by the author Early 1990s — Scale Analytical workloads started to get complex. Data volumes started to grow.
Engineers work with Data Scientists to help make the most of the data they collect and have deep knowledge of distributed systems and computer science. In large organizations, data engineers concentrate on analytical databases, operate datawarehouses that span multiple databases, and are responsible for developing table schemas.
One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source. Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop.
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