How can you determine which data sources to integrate into a data warehouse?

3 Define your data integration strategy

When defining your data integration strategy, you need to consider the complexity and diversity of your data sources, the latency and frequency of your data updates, the scalability and performance of your data integration system, the quality and consistency of your data, and the governance and compliance of your data. Common strategies include batch data integration, which is suitable for large volumes of static or historical data that do not require real-time updates; real-time data integration, which is suitable for small volumes of dynamic or streaming data that require low latency and high availability; and hybrid data integration, which is suitable for heterogeneous and complex data environments that require flexibility and efficiency.

4 Design your data warehouse schema

The fourth step is to design your data warehouse schema, the structure and organization of your data for analysis and reporting. You need to ensure that your data warehouse schema is compatible and consistent with your data sources and integration strategy. Commonly used designs include the Star schema, which has a central fact table with measures or metrics, and several dimension tables with attributes or categories; the Snowflake schema, which further normalizes dimension tables to reduce redundancy and improve data quality; and the Data Vault schema, consisting of hub tables with business keys, link tables with relationships, and satellite tables with attributes and histories. This schema is flexible, scalable, and resilient to changes in data sources and business rules.

5 Implement and monitor your data integration process

The final step is to implement and monitor your data integration process, which requires executing, testing, and validating your data integration strategy and data warehouse schema. You must guarantee that your data integration process is dependable, precise, and secure, while also meeting your business needs and goals. Moreover, you need to monitor and measure the process, as well as recognize and address any potential issues or errors. Common data integration challenges include data quality issues (e.g., missing, inaccurate, inconsistent, or duplicate data), security issues (e.g., unauthorized access, leakage, or breach of data), compatibility issues (e.g., incompatible data types, formats, or schemas), and performance issues (e.g., slow, inefficient, or failed extraction, transformation, or loading).

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?