Data developers often debate where to perform initial data transformations. Transforming raw records in the wrong layer hurts report performance. However, choosing between SQL views and Power Query optimizes processing efficiency. Enrolling in top power bi training hyderabad clarifies data transformation architecture. Learning optimal ETL practices helps developers build scalable corporate reports.
Here is how to choose between SQL views and Power Query.
Shifting Heavy Data Cleansing Upstream into Database SQL Views
Processing millions of rows in Power Query taxes desktop system memory. Therefore, move heavy row filtering and data type conversions upstream. First, write SQL views that execute transformations inside powerful database engines. Next, connect Power BI directly to pre-cleansed relational database views.
As a result, your scheduled semantic model refreshes complete much faster.
Leveraging Query Folding for High-Speed Power Query Steps
Applying native Power Query steps does not always degrade performance. However, ensure your M code supports automatic native database query folding. First, place simple filtering and column selection steps early in Power Query. Next, verify that Power Query translates M steps into native SQL.
Consequently, the source database handles heavy transformation workloads seamlessly.
Handling Complex Business Logic inside Database Views Relentlessly
Writing overly complex conditional logic in Power Query creates maintenance bottlenecks. Instead, encapsulate intricate business rules directly inside database SQL views. First, standardize financial calculations and text parsing inside database view logic. Next, expose clean standardized tables directly to report design teams.
Therefore, multiple reporting tools share identical business logic consistently.
Using Power Query for Ad-Hoc File Merging and Non-SQL Sources
Relational database views cannot transform local flat files or web API endpoints. However, utilize Power Query for combining non-database enterprise data sources. First, merge Excel spreadsheets and CSV files inside Power Query directly. Next, combine local file queries with database view tables seamlessly.
Thus, completing power bi training hyderabad sharpens your complete ETL architecture skills.
Summary
Executing transformations inside SQL views offloads processing to database servers. Query folding in Power Query maintains high-speed database pushdown performance. Standardizing logic inside views ensures consistency across enterprise reporting tools. Master ETL architecture today to design fast and scalable corporate dashboards.