A data warehouse is the foundation of modern business analytics. If your organization makes decisions based on data — and it should — sooner or later you will need a data warehouse.
What is a data warehouse?
A data warehouse is a specialized database system designed for analytics and reporting. Unlike operational databases (OLTP), which handle ongoing transactions (orders, payments, registrations), a data warehouse stores historical data optimized for analytical queries (OLAP).
Key characteristics of a data warehouse:
- Subject-oriented — data is organized around key business areas (sales, customers, products), not operational processes
- Integrated — data from multiple sources (CRM, ERP, web systems, IoT) unified in a single format
- Non-volatile — historical data is not overwritten, enabling trend analysis
- Time-variant — every record contains a timestamp, allowing you to track changes over time
Data warehouse architecture
Classic three-tier architecture
- Data source layer — operational systems (CRM, ERP, logs, APIs), files (CSV, JSON), data streams (Kafka)
- Integration layer (ETL/ELT) — processes for extracting, transforming, and loading data
- Presentation layer — BI tools (Tableau, Power BI, Looker), reports, dashboards
ETL vs ELT
ETL (Extract, Transform, Load) — the classic approach. Data is transformed BEFORE loading into the warehouse. Requires separate infrastructure for transformation. Popular tools: Informatica, Talend, SSIS.
ELT (Extract, Load, Transform) — the modern approach. Data is loaded into the warehouse in raw form, and transformation happens inside the warehouse (leveraging its computing power). Popular tools: dbt, Fivetran, Airbyte.
In 2026, ELT dominates new projects — especially in the cloud, where computing power is elastically scalable.
Data models
- Star schema — a central fact table surrounded by dimension tables. Simple, fast for queries. The standard in data warehousing.
- Snowflake schema — an extension of the star schema with normalized dimension tables. Saves space but results in slower queries.
- Data vault — a data modeling approach with separation into Hub, Link, and Satellite. Flexible, auditable, popular in enterprise environments.
Data warehouse vs data lake vs data lakehouse
| Feature | Data Warehouse | Data Lake | Data Lakehouse |
|---|---|---|---|
| Data | Structured | Raw (any format) | Both types |
| Schema | Schema-on-write | Schema-on-read | Hybrid |
| Users | BI analysts, management | Data scientists, ML engineers | Everyone |
| Cost | High (storage + compute) | Low (cheap storage) | Medium |
| Queries | SQL, fast | Spark, slower | SQL + Spark |
| Examples | Snowflake, BigQuery, Redshift | S3, ADLS, GCS | Databricks, Delta Lake, Apache Iceberg |
| Data quality | High (ETL guarantees) | Low (raw data) | High (governance layer) |
Data lakehouse is the latest trend (2024-2026) — combining the cheap storage of a data lake with the transactional consistency and performance of a data warehouse. Key technologies: Delta Lake (Databricks), Apache Iceberg, Apache Hudi.
Popular data warehouse solutions in 2026
Snowflake
- Architecture: separation of storage and compute (independent scaling)
- Advantages: multi-cloud (AWS, Azure, GCP), elastic scaling, data sharing
- Pricing: pay-per-query (credits), storage billed separately
- Best for: companies needing flexibility and multi-cloud support
Google BigQuery
- Architecture: serverless, columnar database
- Advantages: zero infrastructure management, fast queries on petabytes, built-in ML (BigQuery ML)
- Pricing: pay-per-query or flat-rate
- Best for: companies in the Google Cloud ecosystem, fast ad-hoc analyses
Amazon Redshift
- Architecture: MPP (Massively Parallel Processing) cluster
- Advantages: deep integration with AWS (S3, Glue, SageMaker), Redshift Serverless
- Pricing: per-node or serverless (per-query)
- Best for: companies with existing AWS infrastructure
Azure Synapse Analytics
- Architecture: unified analytics platform (SQL + Spark)
- Advantages: integration with Microsoft 365, Power BI, Azure ML
- Pricing: per-compute-unit
- Best for: companies in the Microsoft ecosystem
Cost comparison (approximate, 1 TB of data)
| Solution | Estimated monthly cost |
|---|---|
| BigQuery (on-demand) | $200–500 |
| Snowflake (standard) | $300–800 |
| Redshift (dc2.large) | $250–600 |
| Azure Synapse | $300–700 |
When does an organization need a data warehouse?
A data warehouse is justified when:
- Data from multiple sources — CRM, ERP, web systems, marketing automation — you need a single source of truth
- Reports take too long — analytical queries are slowing down operational databases
- Data inconsistency — different departments report different numbers for the same metrics
- Need for historical analysis — trends, forecasts, year-over-year comparisons
- Compliance regulations — requirement for data auditing and archiving
How to get started?
Step 1: Define business objectives
Do not build a warehouse “because everyone has one.” Determine: what business questions do you want to answer? What reports do you need?
Step 2: Inventory your data sources
Where will data flow from? CRM (Salesforce, HubSpot), ERP (SAP, Dynamics), web systems (Google Analytics), Excel files?
Step 3: Choose a platform
For new projects in 2026: Snowflake or BigQuery. If you are on AWS: Redshift. On Microsoft: Azure Synapse.
Step 4: Design the data model
Start with a star schema — simple, fast, understandable. Use data vault for enterprise with multiple sources.
Step 5: Build the ELT pipeline
dbt + Fivetran/Airbyte is the most popular stack in 2026. Open-source alternative: dbt + Airbyte + Airflow.
Step 6: Connect a BI tool
Tableau, Power BI, Looker, or Metabase (open-source). Business users need dashboards, not SQL.
Data warehouse and analytics training
At EITT, we offer training in database and analytics technologies — from SQL fundamentals to advanced data warehousing and BI tools. Explore our database training catalog.
If you are planning a data warehouse implementation in your organization, our training programs will help your team gain the practical skills needed to design, build, and maintain a data warehouse.