How can you manage data structure fragmentation?

2 Design a data integration strategy

In order to manage data structure fragmentation, it is important to design a data integration strategy that aligns with your data goals and needs. Data integration is the process of combining data from different sources into a unified and consistent view. There are various approaches and methods for doing this, such as ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), and data virtualization. With ETL, data is extracted from the source, transformed into a common format and schema, and loaded into a target destination such as a data warehouse or a data lake. ELT involves extracting the data from the source, loading it into a target destination, and transforming it on-demand or in batches using tools such as SQL or Spark. Data virtualization allows for the querying of data from the source without moving or copying it by using a layer of abstraction that provides a unified view of the data.

3 Choose the appropriate data structures and formats

When managing data structure fragmentation, it is important to choose the right data structures and formats for data integration and analysis. These will affect how the data is stored, organized, accessed, and processed. Factors to consider include the volume of data, its variety, and its velocity. For large and fast-growing data, scalable and distributed structures such as NoSQL databases or Hadoop file systems may be necessary. Structured and relational data can be stored using tabular or hierarchical structures like CSV or XML. Semi-structured or unstructured data can be stored with flexible and schema-less structures like JSON or Parquet. Finally, for real-time or streaming data, low-latency and high-throughput structures like Kafka or Redis are recommended.

4 Implement data quality and governance measures

The final step to manage data structure fragmentation is to implement data quality and governance measures to ensure the accuracy, consistency, and security of your data. These processes and policies define and monitor the standards and rules for data collection, integration, storage, and usage. Data validation is key, as it checks and corrects the data for errors, anomalies, or inconsistencies. Data lineage is also important, as it tracks and documents the data flow and transformations from the source to the destination. Lastly, data access should be controlled and audited for different users, roles, or applications using methods such as encryption, authentication, or authorization.