Data Analysis with SQL for Spreadsheet-Native Analysts: A 2026 Guide
Explore data analysis with sql for spreadsheet-native analysts with practical guidance on features, use cases, and implementation strategies.
Eoin McMillan
January 28, 2026 • 9 min read
Data analysis with SQL involves querying structured tables to filter, join, aggregate, and window data for insights. For spreadsheet-native analysts, learning core SQL patterns-SELECT, JOIN, GROUP BY, subqueries, and window functions-unlocks faster analysis on larger datasets. AI-enabled tools like Sourcetable can generate or optimize these queries automatically.
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What is Data Analysis with SQL?
Data analysis with SQL is the process of extracting insights from databases using structured query language, similar to spreadsheet tasks but scalable for larger datasets. It revolves around four key operations:
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Filtering: Selecting specific rows based on conditions, akin to Excel filters.
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Joining: Combining data from multiple tables, more powerful than VLOOKUP.
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Aggregating: Summarizing data with functions like SUM or AVG, comparable to PivotTables.
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Windowing: Calculating rolling metrics or rankings over data partitions, advanced beyond basic spreadsheets.
According to ThoughtSpot's SQL tutorial, these operations form the foundation of analytical querying.
Why Should Spreadsheet Users Learn SQL for Data Analysis?
Spreadsheet users should learn SQL to overcome the limitations of tools like Excel when handling large or complex data. According to hiring surveys, SQL remains one of the most in-demand skills for data analysts, with 2026 skills reports highlighting SQL and spreadsheet proficiency as a powerful combination for business roles.
Research shows that analysts who know SQL can work with significantly larger datasets than spreadsheet-only peers, enabling faster and more reproducible analyses. Data indicates that many common spreadsheet tasks map directly to a handful of SQL query patterns, making the transition intuitive. Moreover, AI tools like Sourcetable can automate SQL generation, reducing the learning curve.
What Are the Basic SQL Syntax and Concepts for Analysts?
SQL syntax mirrors spreadsheet logic but operates on tables. Key clauses include:
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SELECT: Specifies columns to retrieve, similar to highlighting cells in Excel.
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FROM: Indicates the table source, like referencing a sheet.
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WHERE: Filters rows based on conditions, comparable to filter tools.
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GROUP BY: Aggregates data into summaries, akin to PivotTables.
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HAVING: Filters aggregated results, useful post-grouping.
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ORDER BY: Sorts output, like Excel's sort function.
For example, to sum sales by region: SELECT region, SUM(sales) FROM transactions GROUP BY region ORDER BY SUM(sales) DESC. According to Mimo's SQL for Data Analysis guide, mastering these basics allows analysts to query databases directly, bypassing spreadsheet bottlenecks.
How Do Joins Compare to VLOOKUPs in Excel?
SQL joins are more flexible and powerful than Excel's VLOOKUP for combining data from multiple tables. While VLOOKUP searches for a match in one column, SQL joins can link tables based on complex conditions and handle missing data gracefully. Key join types include:
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INNER JOIN: Returns rows with matches in both tables, similar to VLOOKUP with exact matches.
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LEFT JOIN: Includes all rows from the left table and matched rows from the right, akin to VLOOKUP with IFERROR to preserve left data.
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RIGHT JOIN: Inverse of LEFT JOIN, less common.
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FULL OUTER JOIN: Returns all rows from both tables, with NULLs for non-matches, beyond Excel's native capabilities.
For deeper insights on outer joins, see our guide on Outer Joins, Subqueries, and Window Functions.
SQL Joins vs Excel VLOOKUP Comparison
| Operation | SQL Join | Excel Equivalent |
|---|---|---|
| Exact match between tables | INNER JOIN | VLOOKUP with exact match |
| Include all left rows, match right | LEFT JOIN | VLOOKUP with IFERROR to handle misses |
| Include all rows from both tables | FULL OUTER JOIN | Manual merging with complex formulas |
| Multiple condition matching | JOIN with ON clause | INDEX-MATCH with arrays |
What Are Subqueries and How Are They Used in Analytics?
Subqueries, or nested queries, allow you to embed one query within another, enabling complex analytics without multiple steps. Common patterns include:
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Scalar subqueries: Return a single value, used in SELECT or WHERE clauses, like calculating a threshold dynamically.
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IN subqueries: Check if a value exists in a result set, similar to Excel's FILTER function.
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Correlated subqueries: Reference outer query columns, useful for row-by-row comparisons.
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EXISTS subqueries: Test for row existence, efficient for large datasets.
For example, to find employees with above-average sales: SELECT name FROM employees WHERE sales > (SELECT AVG(sales) FROM employees). According to Microsoft Learn, subqueries optimize data retrieval by reducing intermediate tables.
When Should You Use SQL Window Functions?
SQL window functions perform calculations across rows related to the current row, ideal for rolling metrics, rankings, and cumulative sums without collapsing data. Use them when you need:
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Rolling averages: Calculate moving averages over time, like 7-day sales trends.
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Rankings: Assign ranks with ROW_NUMBER() or RANK(), useful for leaderboards.
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Partitioned aggregates: Compute sums per group while retaining detail rows, e.g., department sales alongside individual records.
Syntax involves PARTITION BY (to group) and ORDER BY (to sequence). For instance, SELECT date, sales, SUM(sales) OVER (ORDER BY date) AS running_total FROM transactions. This advanced capability, highlighted in ThoughtSpot's tutorial, goes beyond standard Excel functions.
This video provides a practical tutorial on using SQL for data analysis, complementing the concepts covered here.
How Can AI Tools Like Sourcetable Simplify SQL Analysis?
AI tools like Sourcetable bridge the gap between spreadsheets and SQL by generating queries from natural language or spreadsheet interactions. They simplify SQL-powered analysis through:
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Automated query generation: Describe your analysis in plain English, and Sourcetable's AI writes the SQL, reducing coding effort.
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Query optimization: AI suggests improvements for performance, leveraging database statistics as noted in Microsoft Learn resources.
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Spreadsheet integration: Results populate directly into a familiar spreadsheet interface, enabling further manipulation.
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Learning assistance: Beginners can explore generated SQL to understand patterns, accelerating skill development.
This approach multiplies productivity, allowing analysts to focus on insights rather than syntax.
Why should spreadsheet users learn SQL for data analysis?
Spreadsheet users should learn SQL to handle datasets too large for Excel, automate repetitive reporting tasks, and perform complex joins or aggregations efficiently. SQL skills are highly valued in data-driven roles, and AI tools like Sourcetable can help generate SQL, making the transition smoother.
What are the most important SQL concepts for analysts?
The most important SQL concepts for analysts are SELECT queries for data retrieval, JOINs for combining tables, GROUP BY for aggregations, subqueries for nested logic, and window functions for advanced calculations like rankings and rolling metrics. Mastering these enables comprehensive data analysis.
How do joins and subqueries work in SQL compared to VLOOKUPs in Excel?
SQL joins combine entire tables based on matching columns, offering more flexibility than VLOOKUP's single-column search. Subqueries allow embedding queries within others for dynamic calculations, whereas Excel often requires multiple steps or helper columns. SQL handles larger datasets and complex conditions more efficiently.
What are SQL window functions and when should I use them?
SQL window functions perform calculations across a set of table rows related to the current row, such as running totals or rankings. Use them for rolling averages, partitioned aggregations, or sequential comparisons where you need to retain detail rows without grouping, scenarios challenging in basic spreadsheets.
How can AI tools help me write SQL if I am new to coding?
AI tools like Sourcetable can translate natural language requests into SQL queries, provide examples, and optimize existing code. They act as a bridge, allowing beginners to perform complex analyses without deep coding knowledge, while learning SQL patterns through generated examples.
Key Takeaways
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SQL is essential for analyzing datasets beyond spreadsheet limits, with hiring surveys showing high demand.
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Joins in SQL are more flexible than VLOOKUPs, enabling complex table combinations.
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Window functions allow advanced analytics like rolling averages without collapsing data.
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AI tools can automate SQL query generation, making it accessible to spreadsheet users.
Sources
- According to Mimo's SQL for Data Analysis guide, SQL is foundational for data professionals. [Source]
- Microsoft Learn notes that database statistics optimize query performance for large datasets. [Source]
- ThoughtSpot's SQL tutorial highlights key operations like filtering and joining for analytical querying. [Source]
