Free ebook on SQL for business intelligence: query tables, join data, calculate KPIs, and build reliable dashboard datasets.
Free ebook content
-
SQL for Business Intelligence: Understanding Tables, Rows, and Business Entities
+ Exercise: Why can summing orders.order_total after joining orders to order_items lead to inflated revenue in a BI query?
-
SQL for Business Intelligence: Writing Reliable SELECT Statements
+ Exercise: When preparing a dashboard-ready dataset, which approach best helps ensure a reliable SELECT output?
-
SQL for Business Intelligence: Filtering Data with WHERE and Safe Conditions
+ Exercise: When filtering a timestamp column for all orders in January 2025, which WHERE pattern is safest to avoid accidentally excluding records late on the last day?
-
SQL for Business Intelligence: Sorting and Limiting Results for QA and Insights
+ Exercise: When using LIMIT/TOP to sample rows for BI QA, what is the best practice to ensure the sample is meaningful and consistent across runs?
-
SQL for Business Intelligence: Joining Tables Without Duplicating or Losing Rows
+ Exercise: You need to report total order revenue, but you also want item-derived metrics (like item count) from an order_items table that has multiple rows per order_id. Which approach best avoids inflating revenue?
-
SQL for Business Intelligence: Aggregations and KPI Calculations with COUNT, SUM, AVG
+ Exercise: You need a daily report with both number of completed orders and captured revenue, but joining orders, payments, and order_items multiplies rows and inflates SUM(amount). What is the safest approach to keep both counts and sums correct?
-
SQL for Business Intelligence: GROUP BY for Reporting Tables and Time Series
+ Exercise: When building a monthly revenue report, which approach best prevents getting multiple rows per month?
-
SQL for Business Intelligence: Working with NULLs and Data Quality Edge Cases
+ Exercise: In a BI dashboard, what is the safest SQL approach to calculate a rate when the denominator can be 0 or NULL, while avoiding errors and not silently changing meaning?
-
SQL for Business Intelligence: Subqueries for Reusable Filters and Pre-Aggregation
+ Exercise: In a BI query, what is the safest way to add a 30-day revenue metric to a customer report without accidentally multiplying rows?
-
SQL for Business Intelligence: Common Table Expressions for Readable, Auditable Queries
+ Exercise: You notice a BI query is inflating revenue after joining a daily revenue CTE to order-level rows. Which change best prevents this grain trap while keeping the query auditable?
-
SQL for Business Intelligence: Building Dashboard-Ready Datasets and Validating Results
+ Exercise: When building a dashboard-ready dataset with a daily date spine, what additional step is required if you want every date to include every category × region combination even when there are no sales?
About the free ebook
SQL for Business Intelligence: From Zero to Confident Querying
This free ebook teaches practical SQL skills for turning business data into reliable reports, KPI calculations, and dashboard-ready datasets. It focuses on the query patterns analysts use to answer real questions while checking that results are accurate.
Build confidence with business data
Learn how tables, rows, columns, and business entities relate to one another before writing clear SELECT statements. Practice filtering records with WHERE, sorting outputs for quality assurance, and limiting results to inspect data efficiently.
Query across tables without misleading results
Business intelligence often depends on combining customers, orders, products, and events. The ebook explains joins with an emphasis on avoiding duplicated rows, missing records, and incorrect totals. You will also work with NULL values and common data-quality edge cases.
Calculate KPIs and reporting metrics
Use COUNT, SUM, AVG, and GROUP BY to create reporting tables and time-series summaries. The examples connect SQL syntax to common analytical tasks such as counting customers, measuring revenue, and comparing performance across groups.
Write readable, auditable analysis
Progress from reusable subqueries to Common Table Expressions (CTEs) that make multi-step logic easier to review. The final lessons show how to prepare datasets for dashboards and validate outputs before sharing insights.
What you will be able to do
- Explore and filter business datasets with reliable SQL queries.
- Join related tables while protecting row-level accuracy.
- Aggregate data into KPIs, grouped reports, and trends.
- Handle NULLs and validate dashboard-ready results.
How do SQL joins affect KPI calculations?
Incorrect joins can duplicate or remove rows, producing inaccurate counts, revenue totals, and averages.
What is the difference between WHERE and GROUP BY in SQL reporting?
WHERE filters rows before aggregation, while GROUP BY organizes remaining rows into reporting categories.
Why are CTEs useful in business intelligence SQL queries?
CTEs separate complex logic into named steps, making queries easier to test, audit, and reuse.
This ebook includes:
11 content chapters
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
Ready to get started?
recommended
In the app you will also find...
Over 5,000 free courses
Programming, English, Digital Marketing and much more! Learn whatever you want, for free.
Study plan with AI
Our app's Artificial Intelligence can create a study schedule for the course you choose.
From zero to professional success
Improve your resume with our free Certificate and then use our Artificial Intelligence to find your dream job.
You can also use the QR Code or the links below.
