A data warehouse is a centralized, governed repository that consolidates historical data from multiple operational systems and formats it for fast, reliable analytical querying and reporting.
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.
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:
Operational databases (e.g., PostgreSQL, MySQL), SaaS APIs (Salesforce, Stripe), files, streaming platforms, and more.
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.
Raw ingested data lands here for auditing and troubleshooting. It is rarely used directly by analysts.
Curated schemas—often dimensional (star or snowflake), data marts, or semantic layers—are exposed to BI tools, notebooks, and SQL editors like Galaxy.
Catalogs, lineage tools, and access controls ensure transparency, compliance, and security.
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.
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.
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.
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.
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.
“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.
While Galaxy is not a data warehouse, it is the interface through which engineers and analysts interact with one. With Galaxy you can:
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.
Without a data warehouse, organizations struggle to reconcile siloed data, maintain historical records, and run complex queries without impacting production systems. A well-designed warehouse delivers a single source of truth, supports regulatory compliance, and powers data-driven decisions at scale.
A transactional database (OLTP) optimizes for fast inserts, updates, and deletes to power applications. A data warehouse (OLAP) optimizes for large scans, complex joins, and historical analysis. Each uses different storage layouts, indexing strategies, and workloads.
Galaxy is a modern SQL editor—not a warehouse itself—but it connects to warehouses like Snowflake, Redshift, and BigQuery. It helps engineers explore schemas, generate SQL with an AI copilot, and share vetted queries, accelerating warehouse adoption and collaboration.
Yes. ELT postpones heavy transformations until after loading data into the warehouse, but you still must standardize types, handle nulls, and model dimensions/facts. The difference is where and when transformations happen, not whether they happen.
It depends on regulatory requirements, storage costs, and analytical needs. Many companies retain 5–7 years of detailed data, archive the rest to object storage, and keep aggregates for long-term trend analysis.