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This is how the OnlineAnalyticalProcessing (OLAP) cube was born, which you might call one of the grooviest BI inventions developed in the 70s. It’s a snapshot of data at a specific point in time, at the end of a day, week, month or year. Saving time and headaches with onlineanalyticalprocessing tool.
Large, untested workloads run the risk of hogging all the resources. For traditional analytics, they are bringing data discipline to their use of Presto. They ingest data in snapshots from operational systems. In some cases, the queries run out of memory and do not complete. It lands as raw data in HDFS.
This practice, together with powerful OLAP (onlineanalyticalprocessing) tools, grew into a body of practice that we call “business intelligence.” Such BI methodologies are built on a snapshot of what happened in the past. Businesses can no longer afford to be fixated on the rear-view mirror.
Additionally, organizations must carefully consider factors such as cost implications, security and compliance requirements, change management processes, and the potential disruption to existing business operations during the migration. The data warehouse is highly business critical with minimal allowable downtime.
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