This will provide you with primary and foreign keys to identify the same entities across multiple data sources, enabling you to join data from multiple sources and analyze the combined data for better insights. In order to make the best of your analytics, you will need schemas designed for analytics and the ability to connect data between sources. But the schemas for data sources - whether they’re databases such as MySQL, PostgreSQL or SQL Server, or SaaS applications such as Salesforce, Facebook Ads, or Zuora - are often designed with operations rather than analytics in mind.Īnd while data sources such as SaaS apps may provide some general analytics functionality, they cover only the data from that single application. In the data analytics world, both data sources and data warehouses use schemas to define data elements.
The opposite means extra data modeling, the slow retrieval of data and the consumption of more resources (such as time and processing power) when creating reports for analysis. Analysts have a clear, easily understood starting point for analytics.Ī schema designed for analytics means faster access to data for creating reports and dashboards.Analysts don’t have to reverse-engineer the underlying data model.Analysts don’t have to clean data or preprocess it before analyzing it.When a schema is well-designed for analytics: In all of these scenarios, database schemas can ensure consistent formatting and the maintenance of unique primary and foreign keys. You may design a database schema when designing a database from scratch, importing data from a different source into your own application or a data warehouse or reverse-engineering an existing database for better performance. A well-designed schema helps ensure data integrity.
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