Redshift SQL Wizard-Redshift SQL script analysis tool

Example

A user submits an SQL query with several joins. Redshift SQL Wizard identifies all permanent and temporary tables used, indicating how they are related.

Scenario

BI teams often deal with large queries pulling data from many tables and views. The Wizard helps them ensure they’re using the right tables, by clearly distinguishing between temporary and permanent datasets, and showing dependencies.

Example

The tool generates a matrix that lists how tables are joined together, showing which table is dependent on another.

Scenario

When optimizing complex queries, users benefit from visualizing how tables are joined to understand potential bottlenecks. Data engineers can use this matrix to evaluate the correctness and efficiency of joins, especially in scenarios involving multi-level joins across multiple fact and dimension tables.

Example

The Wizard analyzes a large SQL query with subqueries and suggests restructuring to avoid inefficient cross-joins or redundant aggregations.

Scenario

Data engineers working with data warehouses often deal with performance lags. By using this function, they can pinpoint where a query is slow, such as detecting if distribution key misalignment is causing data skew, or if joins are being performed inefficiently across large datasets.

Example

A report is generated summarizing a script’s functionality in business terms, e.g., 'The query aggregates customer data to analyze monthly revenue growth.'

Scenario

Business analysts who may not have deep technical SQL expertise still need to understand the outcome of a query. The Wizard provides summaries that bridge the gap between technical SQL logic and business goals, aiding in cross-functional communication.

Example

The Wizard detects a missing `GROUP BY` clause in a query using aggregation functions, preventing potential runtime errors.

Scenario

When writing complex queries, users often miss small but critical elements such as ensuring proper grouping in aggregation queries. The Wizard catches such errors before the script is run, preventing runtime issues.

Example

The Wizard advises replacing `DISTINCT` with `ROW_NUMBER()` in cases where the query would benefit from better performance in Redshift.

Scenario

Redshift users face unique challenges such as choosing the right distribution and sort keys. The Wizard provides actionable recommendations specific to Redshift’s columnar and distributed storage model, helping optimize queries for parallel processing.