Applying/Executing Data Fabric
The implementation of Data Fabric involves several steps, including data discovery, data integration, data quality, and data governance.
Data discovery involves identifying data sources and understanding the data that is available. This includes identifying data formats, data types, and data quality issues.
Data integration involves integrating data from multiple sources into a centralized data repository. This may involve data cleansing, data transformation, and data normalization.
Data quality involves ensuring that data is accurate, complete, and consistent. This involves data profiling, data validation, and data enrichment to improve data quality.
Data governance involves establishing policies, procedures, and controls for managing data. This includes data security, data privacy, and compliance with regulations such as GDPR, CCPA, and HIPAA
Once Data Fabric is implemented, it can be used to support a variety of use cases, such as business intelligence, analytics, and machine learning