What is Data Warehouse?
Data Warehouse is a centralised repository designed to store, organise, and manage large volumes of structured data from multiple sources, optimised specifically for fast querying and business reporting. It transforms raw data into a consistent, analysis-ready format that supports decision-making across the organisation.
What is a Data Warehouse?
A Data Warehouse is a specialised database system built for analytics and reporting. Unlike operational databases that handle day-to-day transactions (processing orders, updating inventory), a data warehouse consolidates data from across your organisation into a single, structured, and optimised system designed to answer business questions quickly.
When a CEO asks "what were our sales by region last quarter?" or a marketing director needs "customer acquisition cost trends over the past 12 months," the data warehouse is where those answers come from.
How a Data Warehouse Works
A data warehouse operates on a schema-on-write principle, meaning data is structured and organised before it enters the warehouse. This contrasts with a data lake's schema-on-read approach. The process typically follows these steps:
- Extract: Data is pulled from source systems, including ERP, CRM, e-commerce platforms, marketing tools, and financial systems.
- Transform: Data is cleaned, standardised, and restructured to fit the warehouse schema. This includes resolving inconsistencies, deduplicating records, and applying business rules.
- Load: Transformed data is loaded into the warehouse in a structured format optimised for queries.
- Query and report: Business users and analysts query the warehouse using SQL or connect business intelligence tools like Tableau, Power BI, or Looker to create dashboards and reports.
Key Features of a Modern Data Warehouse
- Columnar storage: Data is stored by columns rather than rows, making analytical queries (aggregations, filters, groupings) dramatically faster.
- Scalable compute: Cloud data warehouses like Snowflake, BigQuery, and Redshift can scale processing power up or down based on workload, so you pay only for what you use.
- Historical data: Warehouses are designed to store years of historical data, enabling trend analysis, year-over-year comparisons, and long-term pattern recognition.
- Data modelling: Data is organised into logical models (star schema, snowflake schema) that make it intuitive for business users to navigate and query.
- Concurrency: Modern warehouses support many simultaneous users and queries without performance degradation.
Data Warehouse in the Southeast Asian Context
For businesses operating across ASEAN, a data warehouse addresses several critical needs:
- Unified reporting: Consolidate financial, operational, and customer data from multiple country operations into standardised reports that leadership can compare and analyse.
- Currency normalisation: Automatically convert and standardise financial data across SGD, MYR, THB, IDR, VND, and PHP for consistent cross-market reporting.
- Regulatory reporting: Generate compliance reports required by regulators in different Southeast Asian markets from a single source of truth.
- Performance benchmarking: Compare key performance indicators across markets, product lines, or business units using consistent data definitions and calculations.
Popular Data Warehouse Solutions
| Solution | Best For | Pricing Model |
|---|---|---|
| Google BigQuery | Serverless, pay-per-query | Pay for storage and queries |
| Snowflake | Multi-cloud flexibility | Pay for compute and storage separately |
| Amazon Redshift | AWS-centric organisations | Instance-based or serverless |
| Azure Synapse | Microsoft ecosystem | Flexible, consumption-based |
| Databricks SQL | Unified analytics and ML | Cluster-based |
For SMBs in Southeast Asia, BigQuery and Snowflake are often the most practical starting points due to their serverless architectures and pay-for-use pricing.
Getting Started with a Data Warehouse
- Identify your reporting requirements: What questions does leadership need answered regularly? What reports are currently built manually?
- Map your data sources: List every system that holds data relevant to your reporting needs.
- Choose a cloud warehouse: For most SMBs, a serverless option like BigQuery or Snowflake offers the best balance of capability and cost.
- Design your data model: Work with a data engineer or consultant to design a schema that reflects your business structure.
- Build ETL pipelines: Create automated processes to extract, transform, and load data into the warehouse on a regular schedule.
- Connect BI tools: Link your warehouse to a business intelligence platform that your team will actually use for reporting and dashboards.
A data warehouse is the foundation of data-driven decision-making. Without one, business leaders rely on reports manually compiled from multiple systems, often with inconsistent definitions, stale data, and the risk of human error. With a data warehouse, every person in the organisation works from the same source of truth.
For companies scaling across Southeast Asia, this consistency is critical. When your Singapore, Indonesia, and Thailand teams all report revenue differently because they pull from different systems with different business rules, strategic decisions become unreliable. A data warehouse eliminates this problem by centralising data and applying consistent definitions.
The ROI of a data warehouse is typically measured in the time saved on manual reporting, the quality improvement in business decisions, and the ability to identify trends and opportunities that were previously invisible. For a growing SMB, investing in a cloud data warehouse early creates the analytical infrastructure needed to scale intelligently rather than blindly.
- Choose a cloud-based data warehouse over on-premises to avoid large upfront costs and benefit from elastic scaling. BigQuery and Snowflake are strong choices for SMBs.
- Invest time in data modelling. A well-designed schema makes the warehouse intuitive for business users and ensures queries run efficiently.
- Define business metrics and KPIs consistently before loading data. Agreement on how metrics like revenue, churn, and customer count are calculated prevents confusion downstream.
- Plan for data governance from the start. Define who owns each data source, how data quality is maintained, and who has access to sensitive information.
- Budget for both the warehouse platform and the BI tools that sit on top of it. The warehouse stores and processes data; BI tools make it accessible to non-technical users.
- Start with your most important reporting use case and expand from there. A warehouse that answers three critical business questions well is more valuable than one that theoretically covers everything but is incomplete.
Frequently Asked Questions
What is the difference between a data warehouse and a database?
A database is designed for transactional operations, handling real-time reads and writes efficiently, such as processing e-commerce orders or updating customer records. A data warehouse is designed for analytical queries, optimised for reading and aggregating large volumes of historical data. You would not run your application on a data warehouse, and you would not run complex analytics on a transactional database.
How much does a cloud data warehouse cost for an SMB?
Cloud data warehouses use consumption-based pricing. For an SMB processing moderate analytical workloads, monthly costs typically range from USD 200 to 2,000. Google BigQuery charges per query (USD 5 per terabyte scanned) and per storage (USD 0.02 per gigabyte per month). Snowflake charges separately for compute and storage. Costs scale with usage, so you can start small.
More Questions
Not necessarily, but many growing organisations benefit from both. A data lake is ideal for storing raw, diverse data cheaply and supporting machine learning. A data warehouse is ideal for fast, structured business reporting. If your primary need is dashboards and reports, start with a data warehouse. If you also need to store unstructured data or run ML models, add a data lake. The modern data lakehouse architecture combines both.
Need help implementing Data Warehouse?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how data warehouse fits into your AI roadmap.