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Data warehouse: What is it, and how does it work?

Quick and precise decision-making is vital in our data-centric economy. A data warehouse facilitates this process by combining diverse data into one central repository. In this article, we’ll explore how data warehouses transform raw data into a clean, organized format ready for analysis. Whether you’re a business leader, data analyst, or tech enthusiast, this guide will deepen your understanding of data warehouses’ role in modern data management.

Data warehouse: What is it, and how does it work?

Table of Contents

Table of Contents

What is a data warehouse?

Data warehouse definition

A data warehouse is a centralized repository for data collected from various sources. The primary goal of data warehouses is to make sure that the data gathered across an organization has a common point of reference and can be used for comparison.

Essentially, a data warehouse collects and organizes large volumes of data. With such extensive data at its disposal, an organization can conduct advanced analytics that go beyond the capabilities of standard databases.

This thorough analysis provides valuable insights, aiding in more informed decision-making. Additionally, over time, the data warehouse compiles a comprehensive historical record, invaluable for data scientists, engineers, decision-makers, and business analysts.

What is the data warehouse used for?

Data warehouses are crucial for businesses that want to stay competitive. These warehouses provide analysts and managers with the tools needed to extract insights, monitor performance, and make informed decisions. Some real-world applications of data repositories include:

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    Integrating data from various systems into one central location. For example, a retail chain uses a data warehouse to combine sales data from its online store, mobile app, and physical stores. This integration allows the retail chain to view comprehensive sales performance across all channels in one unified dashboard.
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    Storing extensive historical data to enable organizations to analyze trends and patterns over time. For example, a healthcare provider uses a data warehouse to store decades of patient records and treatment outcomes. By analyzing this data, a healthcare provider can identify trends in disease prevalence and treatment effectiveness over time.
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    Allowing users to create and share customized reports. For example, a marketing agency employs a data warehouse to allow clients to access and customize marketing campaign performance reports. Clients can filter data by demographics, campaign type, and results to tailor insights to their needs.
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    Providing a platform for business analysis and comparison. For example, a financial services company uses a data warehouse to compare performance metrics across different branches and periods. Executives can use this operational data to make strategic decisions about resource allocation and branch operations.
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    Facilitating an environment for AI training. For example, an e-commerce company uses its data warehouse to train machine learning models on customer purchase history and browsing behavior. These AI models predict future buying trends and recommend personalized product suggestions to customers.

Data warehouse architecture

Usually, a data warehouse design has a three-tier structure — bottom tier, middle tier, and top tier.

  • Bottom tier. This is where the data is collected and stored. The bottom tier of a data warehouse architecture consists of a server, typically a relational database system (RDB), that gathers, cleans, and transforms data from various sources. These operations are accomplished through a process called extract, transform, and load (ETL).
  • Middle tier. The middle tier features an online analytical processing (OLAP) server, which ensures quick query response times. This tier can utilize one of three OLAP models: ROLAP (relational), MOLAP (multidimensional), or HOLAP (hybrid). The choice of the OLAP model depends on the type of database system in use.
  • Top tier. This is the user interface layer where end users can access and interact with the data through tools and applications like reports, dashboards, and data visualization software.
data warehouse architecture

A typical data warehouse has four main components: a relational database (RDB), ETL, metadata, and access tools. However, several more components became important as data warehouse architecture evolved and became more popular.

  • A relational database (RDB) is a database management system that organizes stored data into rows and columns. While traditional data warehouses typically utilize relational databases due to their structured query language (SQL) capabilities and efficient handling of structured data, modern data warehouses may also incorporate NoSQL databases to manage semi-structured and unstructured data, enhancing flexibility and scalability.
  • Extract, transform, load (ETL) is a data integration process that manipulates raw data from a source system into a format suitable for analysis. However, many modern data repositories, especially in the cloud, often switch to ELT processes. The choice between ETL and ELT largely depends on an organization’s data strategy needs.
  • Metadata organizes and labels data in a system to make it searchable. Metadata includes details like the author, date, and location of an article, as well as the creation date and size of a file. It also includes structural metadata about how data is organized (such as schemas and tables) and administrative metadata, which deals with data usage and access controls.
  • Data warehouse access tools help users retrieve and analyze data. These tools range from query and reporting to more advanced data mining and predictive analytics tools. They are often integrated with business intelligence (BI) applications and dashboards, providing a more interactive and user-friendly way to explore and visualize data.
  • Client analysis tools focus on thorough data analysis and visualization. While these analytics tools are often installed on a client’s computer, they can also be part of cloud-based BI solutions. They connect to various sources, including data warehouses, and offer advanced features like data modeling or complex calculations to generate detailed insights.
  • Advanced analytical applications that use data science and artificial intelligence (AI) algorithms are often integrated directly into the data warehouse environment. These applications enable seamless analytics using machine learning and AI without the need to export data to a separate platform.

What are the benefits of a data warehouse?

Data warehouses offer several benefits for end users.

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    Improves dataset quality. Data warehouse technology boosts data reliability through processes such as cleaning, deduplication, and standardization, ensuring that business decisions are based on accurate information.
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    Facilitates smarter business decisions. Data repositories integrate data from multiple sources into one repository, providing a comprehensive view that allows deeper analysis.
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    Boosts data security. Data warehouses can enhance security by centralizing data and applying consistent security measures across all stored data. This centralized approach makes managing access controls and audit logs easier and ensures that sensitive information is well-protected.
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    Speeds up decision-making. Centralizing data accelerates access and speeds up report generation, analysis, and decision-making. Quicker decisions improve productivity.
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    Supports scalability. Data warehouses efficiently handle increasing amounts of data, ensuring an organization’s performance remains strong as it grows.
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    Strengthens competitive advantage. Data repositories help businesses stay ahead of market trends and better understand their customers by enabling comprehensive data analysis and quicker insight generation.
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    Supports integration with business intelligence tools. Data warehouses integrate seamlessly with various business intelligence tools and platforms, providing a robust foundation for predictive analytics, machine learning models, and other advanced data analysis techniques.

What are the types of data warehouses?

There are three main types of data warehouses.

Cloud data warehouse

Companies traditionally set up data warehouses on local servers within their premises. These on-premises data warehouses offer advantages such as enhanced governance, robust security, data sovereignty, and reduced latency. However, they often lack the flexibility to scale quickly and require meticulous planning to anticipate future capacity needs.

On the other hand, a cloud-based data platform utilizes cloud technology to collect and store data from various sources, providing a modern twist on traditional data storage methods. Cloud data warehouses bring several significant benefits, including:

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    Flexible scaling. Cloud data warehouses provide flexible, scalable support for growing or fluctuating storage and computing power demands.
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    Ease of use. Cloud data warehouses are designed for ease of use, making them accessible to users at all skill levels.
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    Simplified management. These systems are easier to manage, thanks to reduced hands-on upkeep. The finest cloud data warehouses are fully managed and almost self-operating, allowing even novices to set up and run a data warehouse.
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    Cost efficiency. Cloud data warehouses often lead to cost savings due to a pay-as-you-go pricing model. This means you only pay for what you use, including storage and computing power.
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    Versatile data handling. These warehouses can handle both structured and semi-structured data. This versatility is crucial for businesses dealing with a mix of traditional databases and modern data formats.

Enterprise data warehouse (EDW)

This type of warehouse acts as the main database that supports decision-making across the entire company. Its key benefits include:

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    Broad access to information. EDW pulls together data from different parts of the company, providing a complete view of the organization’s activities. This broad access helps different departments align their strategies and goals.
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    Consistent data handling. The enterprise data warehouse standardizes how data is stored and managed, ensuring that information from various sources works well together. This consistency is crucial for accurate analysis and reporting, as it prevents confusion that comes from handling data in different formats.
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    Complex query handling. Enterprise data warehouses are designed to manage complex data queries essential for in-depth analysis, enabling users to discover detailed patterns and connections.

Data mart

A data mart is a focused database designed to help specific segments of an organization, such as individual departments or business units, make better decisions by providing relevant data tailored to their specific needs. Essentially, a data mart is a subset of an organization’s overall data warehouse, optimized to focus on a particular area or theme. Some benefits that make it an appealing choice for managing departmental data include:

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    Improved performance. Data marts enhance the speed and performance of data queries by limiting the scope of the analyzed data.
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    Increased relevance. Since data marts focus on specific business areas, their data is highly relevant to the department’s users.
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    Cost efficiency. Building and maintaining a data mart is generally less costly than managing a large-scale data warehouse because of its limited scope.
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    Simplified management. With a more focused scope of data, data marts are easier to manage compared to handling the entire data warehouse.
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    Reduced impact on central systems. Offloading data needed for department-specific analyses to a data mart significantly reduces the load on the central data warehouse. This maintains the performance of the central warehouse, which is crucial for enterprise-wide analytics.

Data warehouse vs. database: What is the difference?

Databases and data repositories play distinct yet complementary roles in an organization’s data management strategy. Operational databases optimize for fast, reliable transactions, which are crucial for daily operations and ensuring immediate consistency.

Conversely, data warehousing focuses on aggregating historical data to improve query performance for complex, long-term analytical tasks. This arrangement allows each system to specialize, efficiently supporting specific data management needs, from operational processing to strategic analysis.

Let’s explore more of these differences in the table below.

Data warehouseDatabase
Data typeSummarized historical dataBoth current and historical data
UsersData warehouse analysts, business intelligence (BI) analysts, data warehouse engineersDatabase administrators, database architects, data analysts
PurposeAnalyzing dataRecording and reporting data
ProcessingOnline analytical processing (OLAP)Online transactional processing (OLTP)
QueryComplex analytical queriesSimple transaction queries

Data lake vs. data warehouse: What is the difference?

Data lakes and data warehouses both store big data but serve different purposes and handle data distinctly. Data lakes store all data in its raw form, often for undefined future use, while data warehouses store processed data tailored for immediate, specific analytical needs.

Consequently, data lakes are more adaptable and suitable for managing vast amounts of diverse raw data. In contrast, data warehouses are designed for structured data analysis.

Essentially, seven key factors differentiate a data lake from a data warehouse:

Data lakeData warehouse
Data typeRaw structured, semi-structured, or unstructured dataProcessed structured data
UsersData scientists and data engineersBusiness users, such as analysts and stakeholders
PurposeMachine learning, big data analyticsFast analytics, business intelligence, reporting
Data sourcesMultiple sourcesCore business systems
SchemaSchema is defined after the data is stored (schema-on-read)Schema is defined before the data is written into the warehouse (schema-on-write)
ProcessingELT (extract, load, transform)ETL (extract, transform, load)
Scalability and costEasy to scale at a low costDifficult and expensive to scale

Data mart vs. data warehouse: What is the difference?

A data mart and a data warehouse are systems used for storing and analyzing data, but they serve different purposes. A data warehouse integrates and stores comprehensive data from across an entire organization, supporting broad business analysis for strategic decision-making. It handles large volumes of historical data and is optimized for complex querying.

In contrast, a data mart is smaller and specifically designed to meet the needs of individual departments or business functions. This factor makes it less costly and easier to manage. By focusing on specific areas, data marts provide quicker access to relevant data, aiding operational decisions. While data warehouses offer extensive insights for strategic planning, data marts deliver targeted information to support specific operational goals.

Take a look at the table below to see the key differences between a data mart and a data warehouse.

Data martData warehouse
Data typeSegments of organizational data, summary dataStructured, semi-structured, and unstructured data
PurposeOriented towards a specific business line or teamServes the entire organization
DesignSimpler and smallerLarger and more complex
Data sourcesLimitedA wide array of sources
UsersOperational teams, customer service teams, department-specific analystsBusiness analysts, data scientists, IT and data management professionals

What are the challenges of a data warehouse?

Data warehousing offers significant advantages for data-driven decision-making. However, it also presents challenges that can impact their efficiency and effectiveness.

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    Limited support for unstructured data. Since traditional data warehouses are designed to handle only structured data, organizations seeking to fully leverage unstructured data — such as images, text, and IoT data — for AI applications might find it challenging to find data warehouses that support unstructured data.
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    Incompatibility with AI and machine learning. Data warehouses are optimized for typical tasks such as historical reporting, BI, and querying. They were not initially built to support machine learning workloads, which might require organizations to seek other technologies for these advanced analytics.
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    Restrictive language support. Data repositories traditionally support only SQL. This limitation often excludes Python and R, the preferred languages for app developers, data scientists, and machine learning engineers.
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    Data duplication. Many organizations use both data warehousing and data marts alongside data lakes. This setup leads to duplicated data, excessive redundant ETL processes, and a lack of a single source of truth.
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    Synchronization challenges. Maintaining data consistency between data lakes and warehouses is complex and error-prone. This difficulty can lead to data drift, resulting in inconsistent reporting and inaccurate analysis.
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    High costs. Data warehousing typically incurs charges for both data storage and analysis.