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This includes the ETL processes that capture source data, the functional refinement and creation of data products, the aggregation for business metrics, and the consumption from analytics, business intelligence (BI), and ML. The data warehouse is highly business critical with minimal allowable downtime.
OnlineAnalyticalProcessing (OLAP) is crucial in modern data-driven apps, acting as an abstraction layer connecting raw data to users for efficient analysis. It organizes data into user-friendly structures, aligning with shared business definitions, ensuring users can analyze data with ease despite changes.
Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.
Each data entity provides an abstract representation of businessobjects within the database, such as, customers, general ledger accounts, or purchase orders. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.
They should also provide optimal performance with low or no tuning. Data repository services Amazon Redshift is the recommended data storage service for OLAP (OnlineAnalyticalProcessing) workloads such as cloud data warehouses, data marts, and other analytical data stores.
Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.
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