Data Warehouse

Overview of Data Warehousing

A data warehouse is a centralized, curated repository designed to support analytical workloads by consolidating data from multiple sources, organizing it in an optimized schema, and enabling fast, reliable reporting and decision-making.

Why Do Organizations Need a Data Warehouse?

Companies generate data everywhere—applications, mobile devices, SaaS tools, IoT sensors, and more. Operational databases (OLTP systems) are optimized for real-time transactions: inserts, updates, deletes. They are not designed for complex joins, large scans, or historical analysis. Attempting to run heavy, analytical queries directly on production systems slows them down and often yields inconsistent, siloed results.

A data warehouse solves this by:

• Centralization: Blending disparate data sources into a single source of truth.
• Historical storage: Keeping years of data for trend analysis and regulatory compliance.
• Performance: Columnar storage, specialized indexing, and MPP (massively parallel processing) accelerate analytical queries.
• Data quality & governance: Cleansing, deduplication, slowly changing dimension handling, and metadata management make analytics trustworthy.
• Self-service analytics: Business users and data scientists can explore data without impacting production systems.

Core Components

1. Source Systems

Operational databases (e.g., PostgreSQL, MySQL), SaaS APIs (Salesforce, Stripe), files, streaming platforms, and more.

2. ETL / ELT Pipelines

Extract data from sources, Transform (cleanse, standardize, aggregate), and Load into the warehouse. Modern cloud platforms often favor ELT—load first, transform in-warehouse using SQL.

3. Staging Area

Raw ingested data lands here for auditing and troubleshooting. It is rarely used directly by analysts.

4. Presentation Layer

Curated schemas—often dimensional (star or snowflake), data marts, or semantic layers—are exposed to BI tools, notebooks, and SQL editors like Galaxy.

5. Metadata & Governance

Catalogs, lineage tools, and access controls ensure transparency, compliance, and security.

Architectural Approaches

On-Prem vs. Cloud

Traditional on-prem warehouses (Teradata, Oracle Exadata) require substantial hardware investment and upkeep. Cloud warehouses—Amazon Redshift, Google BigQuery, Snowflake, Azure Synapse—offer elastic storage/compute, pay-as-you-go pricing, and fully managed infrastructure.

Dimensional Modeling

Popularized by Ralph Kimball, dimensional models organize data into fact tables (measurable events) and dimension tables (context). They simplify querying and align with how the business thinks.

Data Vault

An alternative modeling technique that separates business keys (hubs), relationships (links), and descriptive data (satellites) to provide agility and auditability—useful for rapidly changing environments.

Lakehouse & Modern Stack

With cheap object storage and engines like Delta Lake or Iceberg, organizations blend data lakes and warehouses. The lakehouse paradigm keeps raw, semi-structured files in a lake while exposing curated layers through warehouse-like SQL engines (Databricks SQL, Snowflake Iceberg). The boundaries between warehouse and lake are blurring, but the governance and performance principles remain.

Best Practices

• Incremental Loads: Use change data capture or watermarks to avoid full reloads.
• Schema Versioning: Track changes with tools like dbt or migrations to keep models reproducible.
• Partition & Clustering: Align storage with common filter keys (dates, regions) for efficient pruning.
• Document Everything: Column comments, ERDs, and data dictionaries reduce tribal knowledge.
• Automated Testing: Validate row counts, unique keys, and business logic during pipeline runs.
• Cost Governance: In cloud warehouses, monitor compute time and auto-suspend idle clusters.

Working Example

Imagine an e-commerce company loading order data into a Snowflake warehouse and querying it with Galaxy:

-- Aggregate revenue by customer for 2023
SELECT c.customer_id,
c.full_name,
SUM(f.quantity * f.unit_price) AS revenue
FROM fact_sales f
JOIN dim_customer c USING (customer_id)
WHERE f.sale_date >= '2023-01-01'
AND f.sale_date <= '2023-12-31'
GROUP BY c.customer_id, c.full_name
ORDER BY revenue DESC;


In Galaxy’s desktop SQL editor, autocomplete surfaces fact_sales and dim_customer with column descriptions. The AI copilot can automatically suggest adding LIMIT 100 or rewriting the query to filter a specific region when the underlying model changes.

Common Misconceptions

“We can just query the production database.” Analytical joins and scans will degrade OLTP performance and lack historical snapshots.
“A data lake replaces a warehouse.” Data lakes store raw data cheaply but don’t inherently provide the curated, governed layer analysts need.
“ETL is obsolete.” Even ELT still requires transformation—just later in the pipeline and often in SQL.

How Galaxy Fits In

While Galaxy is not a data warehouse, it is the interface through which engineers and analysts interact with one. With Galaxy you can:

• Connect to popular warehouses (Snowflake, Redshift, BigQuery, Postgres) in seconds.
• Use the AI copilot to convert natural-language prompts into warehouse-optimized SQL.
• Share vetted queries in Collections, ensuring teams reuse consistent warehouse logic instead of pasting SQL in Slack.
• Track query history, execution plans, and cost estimates to optimize warehouse spend.

Next Steps

Assess whether your organization’s analytics have outgrown ad-hoc spreadsheets or direct reads from production databases. If so, start with a small cloud warehouse proof-of-concept, adopt a modeling standard (star schema or data vault), and use a modern SQL editor like Galaxy to accelerate adoption.