What's the difference between a data warehouse and database?

Databases handle transactions (OLTP) - fast reads/writes for operations. Warehouses handle analytics (OLAP) - complex queries across large datasets. Different optimisations for different purposes.

Do I need a data warehouse or is a data lake enough?

Different purposes. Warehouses: structured data, SQL queries, business users. Lakes: raw/unstructured data, data science, flexible schemas. Many organisations use both (lakehouse pattern).

How much does a cloud data warehouse cost?

Pay for storage + compute. Storage is cheap (~$20-40/TB/month). Compute varies by usage. Small businesses might spend $100-500/month; enterprises much more. Monitor and optimise queries.

How long does it take to implement a data warehouse?

Basic warehouse: 4-8 weeks. Comprehensive with multiple sources: 3-6 months. Ongoing as you add sources and use cases. Start focused, expand incrementally.

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Data Warehouse

A centralised repository that stores integrated data from multiple sources for reporting and analysis. Optimised for query performance rather than transaction processing.

In-Depth Explanation

A data warehouse is a system designed for storing and analysing large volumes of structured data from various sources. Unlike operational databases, warehouses are optimised for complex queries and reporting.

Data warehouse characteristics:

Subject-oriented: Organised around business subjects (customers, sales)
Integrated: Consistent data from multiple sources
Time-variant: Historical data preserved
Non-volatile: Data stable once loaded

Warehouse architecture:

ETL/ELT: Data ingestion pipelines
Staging area: Landing zone for raw data
Data marts: Subject-specific subsets
Presentation layer: Query and reporting interface

Modern cloud warehouses:

Snowflake, Databricks, BigQuery
Google BigQuery, Amazon Redshift
Separate storage and compute
Near-infinite scalability

Business Context

Data warehouses are the foundation of business intelligence. They enable cross-functional reporting and analytics that operational systems can't support.

How Clever Ops Uses This

We implement cloud data warehouses for Australian businesses, enabling analytics, AI training data preparation, and unified reporting.

Example Use Case

"Consolidating sales from POS, inventory from ERP, and marketing from CRM into Snowflake for unified revenue and customer analytics."

Frequently Asked Questions

Related Terms

ETL Business Intelligence Data Lake

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