Data Warehouse
A data warehouse is a centralized repository that stores and organizes structured data from multiple sources. It’s optimized for reporting and analysis, enabling businesses to get a unified view of their historical and current data.
In depth
A data warehouse brings together data from transaction systems, operational databases, spreadsheets, and third-party applications. Unlike transactional databases designed for everyday operations, a data warehouse is built for query efficiency and complex analysis. It follows four key principles:
Subject-oriented: Data is organized around core business subjects like customers, products, and sales.
Integrated: Data from diverse sources is cleaned and standardized to eliminate inconsistencies.
Time-variant: Historical snapshots are preserved, so you can analyze trends over days, months, or years.
Non-volatile: Once loaded, data is rarely changed or deleted, ensuring a stable foundation for reliable reporting.
Most modern data warehouses use an ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) process. Data is first extracted from source systems, then transformed—cleaned, enriched, and modelled—before being loaded into the warehouse. Popular architectures include star and snowflake schemas, which optimize query performance by organizing data into fact and dimension tables.
Pro tip
When designing your data warehouse, focus on clear naming conventions and consistent data models. This helps everyone—from analysts to executives—find the right tables and metrics quickly.
Why it matters
Businesses need a single source of truth to make confident, data-driven decisions. A data warehouse:
Eliminates data silos by bringing information into one place.
Speeds up report generation with pre-aggregated, indexed data.
Supports advanced analytics and BI tools for deeper insights.
Ensures data consistency and accuracy across teams.
Data Warehouse - In practice
Imagine a retail company tracking sales, inventory, and marketing spend. Instead of manually combining spreadsheets, the company uses an ETL process to load point-of-sale data, CRM records, and ad campaign metrics into a data warehouse. Analysts then build metrics that show up-to-date revenue trends, inventory levels, and campaign ROI—helping leadership spot issues and opportunities in minutes, not days.
Product-specific notes
Klipfolio PowerMetrics connects directly to leading data warehouses like Snowflake, Google BigQuery, and Amazon Redshift. With live queries and data that is not replicated, you can build interactive dashboards on top of your warehouse data.
Related terms
Data Governance
Data governance is the system of people, policies, and tools that keeps data accurate, secure, and usable across your company. Think of it like hiring a skilled librarian for a massive library. Every book is cataloged, protected, and easy to find, so readers trust what they pick up and can act quickly. With solid governance, your team works from the same definitions, follows clear rules for access and use, and treats data as a business asset.
Read moreOnline Analytical Processing (OLAP)
Online analytical processing (OLAP) is a technology that enables fast, ad-hoc analysis of multidimensional data. By organizing information into “cubes” of measures and dimensions, OLAP lets you slice, dice, and pivot large datasets in near real time.
Read moreMeasure
A measure, in the context of data, is a quantifiable numeric value used to track and analyze data. It represents a calculation—like sum, average or count—that you perform on raw data points.
Read moreData Lineage
Data lineage maps the journey of your data from origin to destination. It visually shows where data comes from, how it’s transformed, and where it’s used.
Read moreData Lake
A data lake is a central repository that stores raw, structured, semi-structured, and unstructured data. Think of it as a data sandbox where you collect everything in its original format until you’re ready to analyze it.
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