Data Warehousing and Business Intelligence
Learn how SaaS companies move operational data into warehouses and build BI dashboards using ETL/ELT, star schemas, and aggregation.
Data Warehousing and Business Intelligence is a free SaaS Architecture & Startup Engineering lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the SaaS Architecture & Startup Engineering learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
OLTP vs OLAP
Your live app database is OLTP (transaction processing): fast small reads and writes. Analytics needs OLAP: large scans over historical data.
Running heavy analytics on the production database harms app performance, so we separate them.
The Data Warehouse
A data warehouse (Snowflake, BigQuery, Redshift) stores large volumes of historical data optimized for analytical queries.
It is the single source of truth for reporting across the whole SaaS business.
ETL vs ELT
Two patterns move data into the warehouse:
- ETL — Extract, Transform, then Load (transform before storing)
- ELT — Extract, Load, then Transform (load raw, transform inside the warehouse)
Modern cloud warehouses favor ELT because they have cheap, powerful compute.
Extracting Data
Extraction pulls data from sources: the app database, event streams, and third-party APIs. It can be a full reload or an incremental pull of only changed rows.
Incremental extraction scales far better as data grows.
The Star Schema
Warehouses model data as a star schema: a central fact table (measurable events) surrounded by dimension tables (descriptive context like date, user, product).
Facts and Dimensions
A fact row records a measurable event with numeric measures and foreign keys to dimensions.
-- fact_sales
-- sale_id, date_key, product_key, customer_key, amount
SELECT SUM(amount)
FROM fact_sales
JOIN dim_date USING (date_key)
WHERE dim_date.year = 2026;Aggregation and Roll-Ups
BI queries aggregate facts: sum revenue by month, count signups by plan, average usage by region.
Pre-computed roll-up tables or materialized views speed up frequently used aggregations.
SELECT plan, COUNT(*) AS signups
FROM fact_signups
GROUP BY plan;Business Intelligence Dashboards
BI tools (Looker, Metabase, Tableau) sit on top of the warehouse and let non-engineers explore data through charts and dashboards.
This democratizes data access across the company.
Tenant Isolation in Analytics
In multi-tenant SaaS, analytics must respect tenant boundaries. Internal dashboards may span all tenants, but customer-facing analytics must filter strictly by tenant ID.
A leak here exposes one customer's business data to another.
Data Freshness vs Cost
Warehouses can update in batches (hourly, nightly) or near real-time via streaming. More freshness costs more compute.
Match the refresh rate to the decision: executive reports rarely need second-by-second data.
Data Quality and Governance
Bad data leads to bad decisions. Apply data quality checks (no nulls in keys, totals reconcile) and governance (access control, documentation) so the warehouse stays trustworthy.
Trust in the numbers is the whole point of BI.
Quick Check
Test your data warehousing knowledge.
Recap
You learned data warehousing and BI:
- OLTP vs OLAP and why to separate them
- ETL vs ELT pipelines with incremental extraction
- Star schemas (facts and dimensions), aggregation, and BI dashboards
- Tenant isolation, freshness/cost trade-offs, and data quality
Frequently asked questions
Is the “Data Warehousing and Business Intelligence” lesson free?
Yes — the full text of “Data Warehousing and Business Intelligence” is free to read here on the web, and the SaaS Architecture & Startup Engineering course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the SaaS Architecture & Startup Engineering course, upgrade to CoddyKit PRO.
What will I learn in “Data Warehousing and Business Intelligence”?
Learn how SaaS companies move operational data into warehouses and build BI dashboards using ETL/ELT, star schemas, and aggregation. You practise SaaS Architecture & Startup Engineering with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start SaaS Architecture & Startup Engineering?
No prior experience is required. SaaS Architecture & Startup Engineering on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Data Warehousing and Business Intelligence” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this SaaS Architecture & Startup Engineering lesson?
Yes. Every SaaS Architecture & Startup Engineering lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Data Pipelines for Analytics
- Integrating AI/ML Services
- Feature Flagging & A/B Testing
- Data Warehousing and Business Intelligence