Data management is the process of gathering, arranging, safeguarding and preserving an organisation’s data so that it may be examined for business choices. It helps simplify and shorten the time required for critical tasks. For instance, to prepare raw data for analysis, it must be cleaned, formatted and corrected.
It may also involve merging different datasets like .csv, .tsv, and .xlsx. These could be structured, semi-structured or unstructured.
ETL and ELT
The automated flow of data between systems is made possible by data pipelines. The purpose of ETL (extract, transform, load) is to load data into an organisation’s data warehouse after transforming it from one system. ELT (extract, load and transform) performs data transformations directly within the data warehouse. Unlike ETL, ELT allows raw data to be sent directly to the data warehouse, eliminating the need for staging processes.
Data lakes and data warehouses
A data lake is a repository that stores all your organisation’s data (structured, semi-structured and unstructured) while data warehouses are locations where different data sources may be combined to support specific business intelligence and analytics needs.
Data architecture documents an organisation’s data assets, maps how data flows through its systems, and provides a blueprint for managing data.
How is data management different from data governance?
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Bu hikaye Open Source For You dergisinin September 2024 sayısından alınmıştır.
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