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Data Warehouse

A data warehouse is a data management system that supports business intelligence, such as data analysis. Data warehouses help you make more insightful decisions about your business.

What is a data warehouse?

A data warehouse is a data management system that supports business intelligence, such as data analysis. Data warehouses help you make more insightful decisions about your business.

Say you want to analyze customer data in your e-commerce enterprise. You can move that data to a warehouse system and then run it through business intelligence tools.

The data warehousing process helps you analyze data that exists in all the systems and software tools in your organization, such as:

Data warehouse use cases

Data warehouses have a number of ways they can be used by businesses. In general they can be considered to be a good fit for any situation that involves OLAP style workloads. Below are a few specific examples of how data warehouses can be used.

Data visualization

Data visualization is a common use case for data warehouses, used to effectively create a visual representation of data over time. Any data warehouse that wants to facilitate data exploration and synthesis to generate meaningful business insights would be wise to incorporate data visualization as part of their strategy.

By harnessing the power of data visualization, data warehouses can uncover trends, anomalies, and relationships in data that weren’t easily visible before. This allows businesses not only to identify where their insights lie but also to use those insights immediately in making informed decisions and forging long-term strategies. Data visualization helps data warehouses turn raw data into valuable conclusions that drive higher levels of performance, profitability and overall success.


Data warehouses are also used for creating forecasts to help with long term planning. They can help business leaders to make smart decisions about their company’s future. For example, data warehouses can provide insights into customer trends and other market indicators that can be used to create effective marketing campaigns or inform strategic investments. Another example could be helping predict data center hardware requirements based on historical application metrics.

Data analysis

Data analysis is another common way data warehouses are used. Data analysis tools are used to gain insights from the data warehouse. By applying analytical techniques, it is possible to uncover patterns, correlations, and other trends that can be used to improve operations or identify areas of opportunity. Data analysts use a variety of methods such as statistical analysis, predictive modeling, and machine learning to draw insights from data.

Data warehouse architecture

Data warehouse architecture refers to the technologies and frameworks that make data warehousing possible.

The data warehouse architecture can consist of the following components:

  • A data warehouse is a database that stores information from different data sources in your organization. Some widely used data warehouses include Amazon Redshift, Azure Synapse Analytics, Google BigQuery, and IBM Db2 Warehouse. Data warehouses can be self-managed on your own infrastructure or using a cloud provided managed solution(DBaaS). As with all database systems, data warehouse performance can be viewed through the lens of CAP theorem and tradeoffs need to be made accordingly.
  • ETL tools extract data from sources, transform it into the correct format for analysis and upload it to the warehouse database. These tools remove the need to build manual big data pipelines to push data into your warehouse system.
  • Metadata provides descriptions for data that has moved to your warehouse, helping you manage that data more effectively.

While business intelligence tools live outside warehouse architecture, they are critical components of the data warehousing process. These tools let you identify patterns and trends in the data that has moved to your warehouse. Examples of business intelligence tools include Tableau, Looker, and Microsoft Power BI.

How does a data warehouse work?

A data warehouse works by storing and organizing data from multiple sources in a central location, allowing users to perform queries across the entire dataset in a single unified location. By abstracting away the complexity of data coming from multiple different places, users can be more productive and get more value out of the data.

Here is a high-level overview of how a data warehouse works and the general lifecycle of data as it moves through the system.

Data ingestion

The first step in the data warehousing process is to gather and load data from various sources into the data warehouse. This may involve extracting data from databases, flat files, or other sources, and transforming it into a format that is suitable for storage in the data warehouse. This process will be covered more in depth below in the ETL section.

Data storage

Once the data has been ingested, it is stored in the data warehouse. Data warehouses are typically designed to store large volumes of data, so they may use specialized storage technologies such as columnar databases to optimize performance and scalability. They often take advantage of storage formats like Apache Parquet to compress data and reduce storage costs. Apache Arrow is also gaining adoption as a way to efficiently work with analytics data stored in data warehouses.

Organizing the data

Data in a data warehouse is typically organized in a way that makes it easy to query and analyze. This may involve creating tables and relationships between tables, as well as defining indices and materialized views to improve query performance.

Querying data

Users can perform queries on the data stored in the data warehouse using a SQL or various other query languages. The data warehouse processes the query and returns the results to the user.

Data analysis

The results of a query can be further analyzed using tools for data visualization or processed further with programming languages like Python for things like building machine learning models.

What is ETL in a data warehouse?

A data warehouse is a system that stores data for reporting and analysis. Data warehouses are typically used to store data from multiple sources, including operational databases, transactional systems, and other external data sources. The process of loading data into a data warehouse is known as ETL (extract, transform, load). Data warehouse ingestion refers to the process of moving data from these various sources into the data warehouse.

ETL is a three-step process:

  1. Extract: The first step in ETL is to extract the data from the source systems. This data is typically stored in flat files or relational databases.

  2. Transform: The next step is to transform the data. This may involve cleaning up the data, integrating it with other data, and performing calculations on the data.

  3. Load: The final step in ETL is to load the transformed data into the target system, which is usually a relational database.

The ETL process is essential for populating a data warehouse with the necessary data for reporting and analysis. It is the foundation for the data warehouse architecture, enabling organizations to access, transform, and store large volumes of data from multiple sources.

Benefits of using a data warehouse

Single source of truth

Data warehouses provide a single source of truth for all the data in your organization. Instead of switching between different systems and software tools to locate data, you can centralize it in a warehouse for easier access. As a result, data integration is one of the most popular use cases for warehousing.

Historical data analysis

Running warehouse data through business intelligence tools lets you perform historical data analysis. For example, you can study market or customer behavior over a period of time and learn new ways to generate sales and drive revenue.

Data mining

Data warehousing lets you analyze large blocks of operational data to discover valuable information that benefits your enterprise. For example, you can identify relationships and correlations in data from time series databases that improve organizational problems and predict future business outcomes.

Improved performance and cost efficiency

Data warehouses are optimized for analytics workloads on huge amounts of data. This means that not only will they provide the performance you need but they will also save you money compared to trying to use a non-dedicated tool.

Tiered storage is one example of an optimization that allows queries to be answered quickly while also saving money by using cheaper storage for infrequently requested data when possible.

There are typically three tiers of storage in a data warehouse:

  • Hot storage - Hot storage is used for data that is accessed frequently and needs to be available for fast querying. This may include data that is used for real-time analytics or data that is used for daily business operations. Hot storage uses high-performance storage like solid-state drives (SSDs) or RAM.
  • Warm storage - Warm storage is used for data that is accessed less frequently than hot storage data, but still needs to be readily available for querying. This may include data that is used for reporting or data that is accessed on a weekly or monthly basis. Warm storage may be implemented using faster storage media such as hard disk drives. Generally when data is pulled from warm storage it will be kept in hot storage until it is evicted after a period of time not being used.
  • Cold storage - Cold storage is used for data that is accessed infrequently and does not need to be immediately available for querying. This may include data that is used for long-term retention or data that is used infrequently for ad-hoc analysis. Cold storage may be implemented using lower-cost storage media such as tapes or object storage.

Data warehouse vs. data lake: key differences

Data warehouse Data lake
A data warehouse contains structured data — data formatted in rigidly defined fields like those in a relational database — that has been extracted from data sources like CRM systems and operational databases and transformed into the correct format for data analytics. A data lake contains raw unstructured data — data not arranged to a preset schema or model such as audio, videos, and images — that has been extracted from data sources but not necessarily transformed into the correct format for analytics. (Data transformation happens within the data lake.)
Data warehouses typically store smaller preformatted data sets. Data lakes can handle substantial raw data sets.
ETL is the data integration process best suited for data warehouses. ELT is the data integration process best suited for data lakes.

Data warehouse vs. database: key differences

Data warehouse Database
A data warehouse collects data that allows users to perform data analysis. A database is a collection of data for easy access, storage, and management.
Data warehouses contain data from multiple data sources. Databases serve as a single source of data.
A data warehouse organizes and stores data in columns. A database collects and stores data in rows.

Data warehouse vs. data mart

Data warehouse Data mart
A data warehouse is a data management system that compiles data from multiple sources. A data mart is a type of data warehouse.
Data warehouses are frequently larger than 100GB. Data marts are often smaller than 100GB.
A data warehouse serves an entire enterprise. A data mart serves a single group of users, such as sales reps.

Types of data warehouses

Cloud data warehouse

A cloud warehouse is a software-as-a-service (SaaS) offering that requires no physical hardware or architecture. You can move data to the public cloud rather than keeping it on-premises.

Some benefits of cloud data warehouses include increased data storage, lower ownership costs, higher speeds, and enhanced performance. In addition, these warehouses are optimized for data analysis, helping you generate intelligence about your organization.

The most popular cloud warehouses include Amazon Redshift, Google BigQuery, Microsoft Azure, and Snowflake.

Data warehouse appliance

A data warehouse appliance (DWA) is a packaged system containing hardware and software tools for data analysis. You can use a DWA to build an on-premises data warehouse. These systems might include a database, server, and operating system. Teradata and Oracle Exadata are examples of DWAs.

DWAs are becoming increasingly uncommon as data-driven businesses move to the public cloud. Cloud-based warehouses can offer more scalability and performance than DWAs, making them the preferred option for warehouse architecture.

Potential challenges with data warehouses

Data warehouses when used correctly provide a number of advantages that have been covered in this article, but that doesn’t mean they are perfect. In this section we will look at some common challenges that businesses face when implementing and using data warehouses.

Data ingest

One of the biggest challenges with implementing a data warehouse is storing the data that is often coming from many different places. This can be a complex process, as data may be stored in different formats and structures and may need to be transformed and cleaned before it can be loaded into the data warehouse.

Data quality

Ensuring the quality and integrity of the data in the data warehouse is also a significant challenge. This may involve identifying and correcting errors and inconsistencies in the data, as well as implementing processes to ensure that the data remains accurate and up-to-date.

In the past it was common for data warehouses to not be updated in real-time, instead using batch processing to update the warehouse periodically which meant that stale data could be an issue depending on the use case. Modern data warehouses can typically handle streaming data so this is less of an issue.


As the volume of data in a data warehouse grows, it can become challenging to scale the data warehouse to meet the increasing demand for data processing and storage. This may require investing in additional hardware or optimizing the data warehouse’s design and configuration to improve performance. Ideally your chosen data warehouse will have support for distributed deployment and horizontal scaling out of the box.

Scaling issues can be a result of high volumes of new data being written or the number of queries coming from end users. Many modern data warehouses have adopted architectures that separate compute from storage which helps alleviate this problem


Ensuring the security of a data warehouse is critical as having a huge amount of data all stored in one place makes it a prized target. Some potential security strategies involve implementing measures such as data encryption, access controls, and monitoring to protect the data from unauthorized access or misuse. Strategies like anomaly detection by monitoring query and data access patterns can help prevent major data breaches early.

User adoption

Another challenge can be getting users to adopt and effectively use the data warehouse. This may involve providing training and support to help users understand how to use the data warehouse and extract insights from the data. Making sure your data warehouse easily integrates with the tools the end users are already using is critical for gaining adoption and reducing friction.

Implementing and using a data warehouse properly can be a complex and time-consuming process that requires careful planning and management to ensure success. It is important to carefully consider the specific needs and requirements of your organization and to allocate sufficient resources to the project to ensure that it is successful.

Data warehouse tools

Below we will look at some projects that can be used as data warehouses as well as some other tools that can be used for helping to move data into a warehouse and also query that data for analysis.

Apache Hadoop

Apache Hadoop is an open source software framework for storing and processing big data. It is designed to handle large amounts of data, both structured and unstructured. Hadoop is scalable, meaning it can be used on small or large clusters of computers. Hadoop can be used as a component of a data warehouse system.

Apache Presto

Presto is an open source SQL query engine that is designed for interactive analytics on large data sets. Presto supports a variety of data sources, including relational databases, NoSQL databases, and HDFS.

Apache Spark

Apache Spark is a fast and flexible data processing engine for large-scale data processing. It can be used to perform data transformations and analyses on data stored in a data warehouse. Spark is often used for ETL type workloads and integrates with many storage backends.


Apache Superset is an open source data visualization and business intelligence tool that allows users to create dashboards and interactive reports based on data from a data warehouse. It offers a range of features such as SQL lab and data exploration, making it suitable for data analysts and developers.


Power BI is a data visualization and business intelligence tool from Microsoft that allows users to create interactive dashboards and reports based on data from a data warehouse. It offers a range of features such as data modeling and predictive analytics, making it suitable for advanced analytics tasks.


Metabase is an open source data visualization and business intelligence tool that allows users to create interactive dashboards and reports based on data from a data warehouse. It is easy to use and offers a range of customization options, making it suitable for business users and data analysts.


Tableau is a popular data visualization tool that allows users to create interactive charts, graphs, and dashboards based on data from a data warehouse. It offers a range of customization options and can be used to create visualizations that are both informative and visually appealing.


Is data warehouse SQL or NoSQL?

Data warehouses can be either SQL or NoSQL databases, depending on the specific technology that is used to implement them. Which should be used depends on the specific needs and requirements of the organization using them. Some organizations may use a combination of both SQL and NoSQL data warehouses to store and analyze different types of data.

SQL data warehouses use a traditional, table-based relational database management system (RDBMS) to store data. This means that the data is organized into tables with rows and columns, and queries are written in SQL to retrieve and manipulate the data. SQL data warehouses are typically used for structured data, such as data from transactional systems or data that follows a well-defined schema.

NoSQL data warehouses use a non-relational database management system to store data. NoSQL databases can store data in a variety of formats, including documents, key-value pairs, and graphs. They are typically used for unstructured data, such as data from social media platforms or sensor networks.

Data lakehouse vs data warehouse

A data lakehouse is a hybrid data management system that combines the functionality of a data lake and a data warehouse. A data lake is a centralized repository that allows users to store and process large volumes of structured and unstructured data at scale. A data warehouse is a database designed specifically for fast querying and analysis of large datasets.

A data warehouse is optimized for storing and querying structured data and is typically used for reporting and business intelligence tasks. It is typically implemented using a traditional relational database management system (RDBMS) and is optimized for fast querying and analysis of structured data.

The main difference between a data lakehouse and a data warehouse is that a data lakehouse is designed to handle both structured and unstructured data, while a data warehouse is typically used to store and analyze structured data. A data lakehouse can ingest and store a wide range of data types, including log files, social media data, and sensor data, in addition to traditional structured data. This makes it a good choice for organizations that need to store and analyze diverse data types.

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