Data management platforms perform many key functions—such as error identification and resolution, resource partitioning, and system performance optimization—and automate many of them to reduce costs and increase efficiency. If you decide to use them, it is important that you stay up to date with best practices.
Friday, March 24, 2023
Best Practices for Data Management
Data management platforms perform many key functions—such as error identification and resolution, resource partitioning, and system performance optimization—and automate many of them to reduce costs and increase efficiency. If you decide to use them, it is important that you stay up to date with best practices.
Data management elements
- Extraction, transformation, and load (ETL) tools extract data from a database, reformat it, and load it into a warehouse.
- Data pipelines process incoming data and transfer it from one system to another automatically and immediately.
- Data lakes store large and diverse groups of unstructured data in its original format, allowing you to have a big picture of it.
- The data architecture defines its collection, storage, transmission and use.
- Data models describe the movement of data within the business or application.
- The data network decentralizes analytical information so that all teams can access it from different locations.
- Data grids harness information from all the computers in a company to perform extensive tasks.
- The federation of the data collects it from various sources and prepares it to work together.
Database management systems (DBMS) are used not only to maintain data, but also to automate and monitor all of these elements. Relational DBMSs use the SQL programming language to structure and connect data, while NoSQL databases are ideal for unstructured ones.
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