Skip to main content
HomeBlogData Science

What Is a Data Warehouse?

A data warehouse is a centralized repository that stores structured and semi-structured data from multiple sources, optimized for analysis and reporting to support business intelligence.
Jul 29, 2024  · 8 min read

Across industries, data needs are constantly increasing. A single organization may collect vast amounts of data from multiple sources. These sources might include transaction systems, social media, IoT devices, and so forth.

Unfortunately, data from different sources is often scattered, inconsistent, and difficult to analyze altogether. This fragmentation makes it challenging to extract meaningful insights and make data-driven decisions.

We can use a data warehouse to address these issues. Data warehouses consolidate and organize data from multiple sources into a structured format by providing a centralized repository for all this data.

This structured format is optimized for reporting, analysis, and decision-making. Data warehouses transform disparate data into valuable assets.

With a data warehouse, we can get comprehensive insights from data consolidated from scattered sources. It enables efficient analysis, ensuring that data is consistent and reliable. Ultimately, a data warehouse empowers us to turn data into actionable insights.

Training 2 or more people? Check out our Business solutions

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Try DataCamp for Business
homepage-hero.png

If you want to learn more about data management, check out my other articles in this series:

What Is a Data Warehouse?

A data warehouse is a specialized storage system designed to collect and organize large amounts of data. It has several key characteristics that define its purpose and functionality.

Subject-oriented

In a data warehouse, data is organized around specific subjects or business areas (e.g., sales, marketing, inventory). This makes it easier to analyze specific aspects of the business.

Integrated

Data from diverse sources is combined and transformed into a consistent format in a data warehouse. This structured integration ensures uniformity and reliability in the data, which makes it easier to analyze and understand.

Time-variant

Data is stored historically in a data warehouse. This means that past versions of the data are not overwritten when new versions are added. This time variance allows for trend analyses and comparisons over time. The historical perspective provided by data warehouses is important for understanding long-term trends and patterns.

Non-volatile

Unlike real-time data systems, data in a warehouse is typically not updated in real-time. Instead, it is loaded in batches for stability and consistency. Once entered, the data is not changed. This non-volatility helps ensure a reliable and consistent record of business transactions.

Data warehouses focus on specific areas of the business, combine data from different sources into a consistent format, keep historical records to track changes over time, and store data in a stable way that doesn't change once it's added.

Data Warehouse Vs. Data Lake

Data warehouses and data lakes are both involved in modern data architecture, serving complementary roles. Both systems are designed to manage and analyze large volumes of data. But they use different approaches.

Feature

Data Warehouse

Data Lake

Data Structure

Structured

Unstructured, semi-structured, and structured

Schema

Predefined schema

Schema-on-read (schema defined during processing)

Data Processing

ETL (Extract, Transform, Load) before storage

Data stored in raw format, processed as needed

Primary Use Cases

Business intelligence, reporting, historical analysis

Exploratory analytics, machine learning, advanced analytics

Query Performance

Optimized for fast queries

Varies depending on processing framework and data organization

Data Storage

Optimized for structured data

Accommodates diverse data types and formats

Flexibility

Less flexible, requires data transformation before storage

More flexible, allows for schema evolution

Common Usage

Used for well-defined reporting and analysis needs

Used for exploratory analysis and discovery of insights

Integration

Often used with data lakes as a source of curated data

Often used alongside data warehouses for raw data storage and processing

Check out this Data Lakes Vs Data Warehouses tutorial for a more in-depth comparison.

Architecture of a Data Warehouse

The diagram below is an example of how data from multiple sources can flow into a data warehouse.

Diagram showing how data from multiple sources is added to a data warehouse.

Setting up a data warehouse involves structuring data from various sources into a centralized repository. Let's explore the components that make up a data warehouse architecture and how they support critical functions in data management and analytics.

Staging area

The staging area can be thought of as a landing area for raw data in the data pipeline. It is an intermediate storage location where raw data is initially extracted from various source systems like databases, APIs, data lakes, and files. This raw data is often diverse and unstructured, requiring initial processing before we can integrate it into the data warehouse.

In the staging area, data undergoes preliminary cleansing and validation. This may include removing duplicates, handling missing values, and ensuring data integrity. You can read more about data cleaning in this Beginner’s Guide to Data Cleaning tutorial or this Data Cleaning in Python course. The data may also be transformed into a standardized format to facilitate seamless integration with the data warehouse schema.

The staging area is a buffer that decouples the extraction process from the process of loading the data into the warehouse. This separation allows for flexibility in managing data flow and ensures that only validated and properly formatted data enters the warehouse. By preparing data in the staging area, we can enhance the quality and reliability of the data that is stored in our data warehouse.

ETL process

Once in the staging area, data goes through the normal steps of Extraction, Transformation, and Loading (ETL). The ETL process is an important component of data management. Let's briefly outline these three stages:

  1. Extraction: Raw data is gathered from various sources, such as databases, files, or APIs, and placed into the staging area.
  2. Transformation: This is where the magic happens. Data is cleaned (removing errors and inconsistencies), integrated (combined from different sources), and transformed into a suitable format for analysis. This ensures that data is accurate and compatible with the data warehouse.
  3. Loading: The transformed data is transferred from the staging area to the data warehouse, making it accessible for analysis and reporting.

The ETL process is a fundamental part of data management, ensuring that raw data is converted into a usable and valuable asset for an organization.

Data storage

The storage layer of a data warehouse dictates how the data is stored, managed, and accessed. Modern data warehouses often use a combination of storage solutions to leverage the strengths of different technologies. Let’s explore a few common storage solutions for data warehouses: relational databases, columnar databases, and cloud-based storage.

Relational database

Relational databases are traditionally the backbone of data storage in data warehouses. These databases use tables with predefined relationships between them, following a relational model. Structured data is stored in rows and columns, making them easily interpretable. SQL (Structured Query Language) is often used to manage and query data in relational databases.

Relational databases excel in scenarios with structured transactional data, such as financial systems and customer relationship management systems. However, they can struggle with performance issues when dealing with very large volumes of data. Horizontal scaling (adding more servers) can also be complex and expensive. As a result, while relational databases are suitable for many applications, they are often complemented by other types of storage optimized for different needs.

Columnar database

Columnar databases are a more specialized type of data storage, designed specifically for read-heavy operations and analytical queries. Instead of storing data in rows, columnar databases store data in columns, hence the name. This allows for more efficient retrieval and processing of large datasets. They are particularly well-suited for data warehousing and business intelligence applications.

One of the key benefits of columnar databases is their ability to optimize storage and query performance for analytical workloads. By storing each column separately, these databases can quickly access the necessary data points without scanning entire rows. This significantly speeds up query times. Additionally, columnar databases often provide better data compression, reducing storage costs.

Despite their advantages for analytical queries, columnar databases can be less efficient for transactional workloads that involve frequent writes and updates. We generally use them in scenarios requiring fast aggregation and summarization of large datasets. They are not as suited to systems where frequent updates and insertions are common.

Cloud-based storage

Cloud-based storage has become an increasingly popular choice for data warehousing due to its scalability, flexibility, and cost-effectiveness. Cloud storage solutions are provided by services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, which offer a range of tools and services to support data storage and processing.

One of the primary advantages of cloud-based storage is its scalability. Organizations can easily scale their storage needs up or down based on demand without the need for significant upfront investments in hardware. This flexibility is particularly beneficial for handling large and fluctuating volumes of data. Cloud storage also provides high availability and disaster recovery capabilities, ensuring that data is accessible and secure even in the event of hardware failures.

Cloud-based platforms often integrate both relational and columnar databases, meaning we can take advantage of the best features of each. This hybrid approach supports diverse data types and workloads, optimizing performance and cost. 

However, data security and privacy can be concerns, as sensitive data is stored off-premises. Latency issues can also arise depending on the proximity of data to cloud servers. Additionally, we need to be mindful of long-term cost considerations and the potential for vendor lock-in.

The data storage layer of a data warehouse can incorporate a mix of relational databases, columnar databases, and cloud-based storage to meet the diverse needs of modern data management and analytics.

Data marts

Data marts are smaller, focused subsets of the data warehouse tailored for specific departments or business functions. It’s essentially a segment of a data warehouse, designed to serve the particular needs of a specific group of users within an organization.

Unlike the enterprise-wide data warehouse that consolidates data from across the entire organization, a data mart focuses on a single subject area or business function. By providing a more focused view of data, data marts empower departments to perform more targeted analyses relevant to their specific needs.

Data marts are particularly useful when specific departments require quick access to relevant data for decision-making. They provide tailored data solutions without the complexity and cost of a full-scale data warehouse. Data marts are also useful for incrementally developing a data warehousing solution, starting with smaller, focused data marts.

However, data marts are not suitable for every situation. Data marts can encounter integration issues, requiring robust ETL processes and data governance to ensure alignment with the central data warehouse. Maintaining performance and scalability as data volumes grow can also be difficult. Data marts require careful planning and resource allocation, especially when scaling.

If you find yourself using lots of data marts, you might consider switching strategies to use a data mesh.

Diagram showing how data marts fit into a data warehouse structure.

The above diagram shows how subsections of the data warehouse can be set up as independent data marts.

Reporting and analysis tools

To access and analyze data in the warehouse, we use a variety of tools designed for data retrieval, manipulation, and visualization.

SQL clients

A SQL client is a software application that allows us to connect to a database. They help us to write and execute SQL queries to retrieve and manipulate data. SQL clients help us perform tasks like querying data, updating records, and creating tables. You may be familiar with some popular SQL clients like MySQL Workbench and pgAdmin. If you’re interested in managing server systems, Introduction to SQL Servers is a great place to start! Or check out the SQL Server Developer Track.

BI platforms

Business Intelligence platforms provide a comprehensive suite of tools for data analysis, reporting, and visualization. They empower us to create detailed reports and gain insights from our data. Visualization tools, such as Tableau, Power BI, and Looker, are particularly popular. They make it easy to create interactive dashboards and visualizations. Learn more about common BI tools in Tableau Fundamentals or PowerBI Fundamentals.

Analysis tools like these empower us to harness the full potential of the data stored in our warehouse.

Benefits of Using a Data Warehouse

A data warehouse offers advantages that can enhance an organization's data management capabilities.

Centralized data

A data warehouse offers the benefit of centralized data management. It provides a single source of truth for reporting and analysis. This centralization ensures consistency and reliability across an organization’s data, eliminating inconsistencies that can occur when data is scattered across multiple sources.

Historical context

Another significant advantage is the ability to perform historical analysis. Data warehouses store historical data, allowing us to conduct trend analysis and make historical comparisons. This ability provides insights into long-term trends and patterns in our data, which can be important when planning or forecasting future trends.

Improved data quality

Improved data quality is a key benefit of using any centralized data management system, including a data warehouse. The data is standardized and cleaned to ensure accuracy and consistency. Any analysis or reporting based on this data is more reliable because of this consistency.

Faster query performance

Data warehouses are optimized for complex analytical queries. This optimization provides quicker and more efficient data retrieval, which is particularly important when we rely on timely insights to drive business decisions.

Business intelligence integrations

BI tools leverage the centralized data in the warehouse to provide actionable insights. Most common BI tools are well-equipped to use data from a data warehouse.

Data mining and ML

A data warehouse serves as a rich source of data for advanced analytics, including data mining and machine learning. The structured nature of data in a warehouse makes it easy to develop sophisticated predictive models. These models, in turn, help us anticipate trends, identify opportunities, and mitigate risks.

When to Consider a Data Warehouse

While a data warehouse is not ideal for every situation, there are circumstances where we should definitely consider if it’s the right solution for our needs. Let’s cover a few situations where we should consider using a data warehouse.

If our organization faces challenges with managing data scattered across various systems and applications, we may consider using a centralized data management system, like a data warehouse. A data warehouse can consolidate this diverse data landscape, streamlining comprehensive analyses.

For situations requiring historical analysis or where we need to understand trends and patterns over time, a data warehouse may offer the necessary historical context.

If our operational systems struggle with running complex queries efficiently, creating a more structured data system may improve efficiency. In this case, we may consider using a data warehouse. A data warehouse optimizes performance, supporting quicker access to data.

A data warehouse can be part of a robust data management plan in many situations. And a good data management plan is necessary to making our data work for us.

Conclusion

A data warehouse is a powerful tool for harnessing the full potential of our data. Data warehouses provide a centralized, integrated, and structured repository for our data.

Learn more about data warehouses in DataCamp’s Data Warehousing Concepts course. I also recommend What Is a Data Federation and What Is a Data Fabric as good further reading on this subject.

Strengthen Your Data Privacy & Governance

Ensure compliance and protect your business with DataCamp for Business. Specialized courses and centralized tracking to safeguard your data.

homepage-hero.png

Photo of Amberle McKee
Author
Amberle McKee
LinkedIn

I am a PhD with 13 years of experience working with data in a biological research environment. I create software in several programming languages including Python, MATLAB, and R. I am passionate about sharing my love of learning with the world.

Topics

Learn AI for business!

Course

Implementing AI Solutions in Business

2 hr
18.4K
Discover how to extract business value from AI. Learn to scope opportunities for AI, create POCs, implement solutions, and develop an AI strategy.
See DetailsRight Arrow
Start Course
See MoreRight Arrow
Related

blog

Data Lakes vs. Data Warehouses

Understand the differences between the two most popular options for storing big data.
DataCamp Team's photo

DataCamp Team

4 min

blog

What Is Data Fabric?

Data fabric is a unified data architecture that connects disparate data sources, simplifying access and management while ensuring consistency and security across the entire data landscape.
Amberle McKee's photo

Amberle McKee

16 min

blog

What Is a Data Federation?

Data federation is a data integration technique that provides a unified view of data from disparate sources without requiring physical data movement or consolidation.
Amberle McKee's photo

Amberle McKee

12 min

blog

What is Data Governance? Elevate Your Business Strategy and Decision-Making

Enhance your organization’s decision-making through a data-driven strategy, transforming siloed Information into strategic assets.
Vinita Silaparasetty's photo

Vinita Silaparasetty

15 min

blog

What Is a Data Mesh?

A data mesh is a decentralized data architecture where domain-specific teams own and manage their data as products, using a shared infrastructure and adhering to federated governance principles.
Amberle McKee's photo

Amberle McKee

10 min

tutorial

The Complete Guide to Data Warehousing on GCP with BigQuery

Discover how Google BigQuery provides scalable, flexible, and cost-effective data warehousing and analytics solutions that seamlessly integrate with other GCP services.
Josep Ferrer's photo

Josep Ferrer

30 min

See MoreSee More