In today's digital era, a data warehouse stands as a pivotal cornerstone for businesses. A data warehouse is defined as a digital repository that houses an organization's vast amounts of data, it serves as both a vault and a library, ensuring data is not only safely stored but also easily accessible. Being able to access your company’s data is critical to business success. The data warehouse market value is set to rise to $58.54 billion from 2023-2029, but one has to wonder: what's driving this surge?

Dive in as we delve into the intricate world of data warehouses and their indispensable role in modern business success.

Here are a few key takeaways from the article:

  • Data warehouses aggregate diverse data for enhanced business insights.
  • Types of warehouses range from Data Marts for user groups to comprehensive Enterprise Data Warehouses.
  • Data warehouses focus on structured data, while data lakes handle unstructured content.
  • Data lakehouses blend the benefits of data lakes and warehouses for versatile data storage.
  • The shift to cloud-based solutions and automated ETL tools enhances real-time analytics.

In this guide, we'll help you understand what a data warehouse is while exploring some of the best solutions available for businesses.  From cloud-based solutions, to open source and on-premises options. We'll also explore how automated ETL tools can help you create a more efficient data workflow. 

Table of Contents

What Is a Data Warehouse?

A data warehouse is a system that aggregates, stores, and processes information from diverse data sources to make it available to business intelligence systems (so they can query it as a single, integrated whole). 

When we address the question of "What is a data warehouse?", the term "business intelligence platform" is also important. Business intelligence (BI) platforms access the aggregated data within a data warehouse to query and analyze it to create profit-boosting insights.

The question of data warehouses is on the minds of a lot of people involved with data and analytics these days - and for good reason. There was a time when AI and machine learning insights were only available to large tech firms like Google, Amazon, Facebook, and Microsoft. Today, anyone can set up a data warehouse to produce valuable machine learning insights affordably.

What is the Purpose of a Data Warehouse?

The primary purpose of a data warehouse is to enable companies to access and analyze all of their data to derive the most accurate business insights and forecasting models. 

Data warehouses and BI solutions are currently more essential than ever because (1) their BI insights give companies a competitive advantage, and (2) more companies are using them. Companies that fail to incorporate these advanced data solutions will be at a serious disadvantage.

Types of Data Warehouses

Here are the main types of data warehouses:

  • Data mart: A data mart is a repository that holds data relevant to a group of users with common needs, such as a business department.
  • Enterprise data warehouse: An enterprise data warehouse is a repository containing standardized data from multiple sources. Data is transformed before ingestion into the warehouse, which means that warehouse data is cleansed and ready for relevant business purposes.
  • Operational data store: An operational data store (ODS) contains the latest data from multiple transactional systems and is used for operational reporting. An operational data store feeds data into the Enterprise Data Warehouse for long-term analytics.

How Do Data Warehouses Work?

As well as knowing what a data warehouse is, it's good to get some insight into how they work. A data warehouse collects information from many data sources across an organization. The data is extracted from these systems, transformed into the ideal format, and then loaded into the data warehouse, often using a method called ETL: extract, transform, load. This central repository of data can then be used for analytics and reporting.


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A Data Warehouse Example

Imagine you need to decide how many workstations and all of their requisite components to order for the upcoming year for all of your company's departments. A decision like this requires a bird's-eye view of the following data:

  • Number of employees in each department
  • What workstations employees are using and what's included (monitor, mouse, keyboard, desk, chair, etc.)
  • Roles of each employee
  • Tech/equipment required for different employee roles to perform their responsibilities
  • Prices of different components needed for a complete workstation
  • Potential bulk-rate discounts available for larger equipment purchases
  • Different vendors providing the equipment
  • Equipment attrition rates
  • Employee turnover rates
  • Forecasts that predict employee expansion or reduction
  • Current budget limitations, guidelines, and goals

Instead of trying to gather all of this information from different sources, a data warehouse makes it immediately available in one place, so you can analyze and organize it into easy-to-understand reporting models. 

Of course, to derive these insights, you'll need to pair a business intelligence platform with your data warehouse and invest in an effective data integration platform like

Popular Data Warehouse Platforms

Some of the most popular data warehouse platforms include:

Structured vs. Unstructured Data

A data warehouse primarily stores structured data, which is organized in a specific manner, such as tables, rows, and columns. Structured data includes information found in relational database systems (RDBMSs) such as Oracle RDBMS, IBM DB2, Microsoft SQL Server, Teradata, MySQL, ADABAS, Microsoft Access, and others. This data might relate to your accounting software, payroll records, and information pertaining to advertising, warehousing, distribution, fulfillment, etc.

On the other hand, unstructured data doesn't have a specific format, like texts or videos. Even though data warehouses can only work with structured data, most of the information in the world gets classified as unstructured, which means it lacks a defined organization or schema.

The volume of unstructured data is growing by the year. Recent reports suggest that by 2025, there could be 175 billion terabytes of unstructured data in the world. The need to incorporate unstructured data is more paramount than ever, and businesses that integrate it with their data warehouses will derive better, more competitive insights. As the volume of unstructured data grows, businesses have started exploring the concept of data lakes, which are designed to store large amounts of raw, unstructured data.

Data Lakes

Unlike data warehouses that store processed and refined data, data lakes hold vast amounts of raw data in their native format. This data can be structured, semi-structured, or unstructured. Organizations use data lakes when they need to store data before knowing how it will be used.

This unstructured data could be valuable because BI tools might be able to extract valuable insights from unstructured data. For example, you could query a large amount of unstructured text by searching for specific words and phrases.

Even if you don't have an immediate use for the unstructured data, it could be useful later. The problem is, that a traditional data warehouse can't store or work with unstructured information. That's where a "data lake" comes in.

Data lakes work together with traditional data warehouses to store vast quantities of unstructured data. You can import any type of information into a data lake and loosely catalog it—kind of like dumping the information into different file folders. Data lakes accept raw information in real-time from multiple sources—such as data from a network of IoT devices, social media sites, email accounts, and mobile apps.

Here are some more benefits of data lakes:

  • Access to massive unstructured data pools: Data lakes allow machine learning tools to crawl, catalog and index massive pools of unstructured data to produce insights in the form of historical graphs, forecast models, and a "range of prescribe" suggestions. Machine learning platforms that work with data lakes include Presto, Apache Spark, Apache Hadoop, and other business intelligence solutions
  • Game-changing insights from analyzing unstructured data: The insights derived from analyzing previously inaccessible unstructured data can be illuminating. Artificial intelligence (AI) and machine learning could be the key to dealing with large volumes of unstructured data, from geospatial information to sequencing the human genome
  • More valuable research: Giving machine learning tools access to previously off-limits data can reveal profit opportunities. For example, you can incorporate more CRM data to understand what strategies your customers respond to and which ones they reject. Or, you can test hypotheses and assumptions before taking ideas to market. Lastly, by looking at manufacturing data collected by IoT devices, businesses can dramatically boost process efficiency through real-time reporting and immediate response.

As a final word of caution, using data lakes with data warehouses to derive business insights is still relatively new. Therefore, make sure you have a strong support team in place before you use an advanced BI strategy like this.

Data Lakehouses

Another option when it comes to storing data, is a combination of the data lake and the data warehouse - named the "data lakehouse".

The data lakehouse addresses some of the frustrations that come along with data lakes and data warehouses, such as:

  • Data warehouses feature rigidly structured data, readable to those who know the business, and usable for other applications. However, there are restrictions and constraints on a warehouse, especially with schemas and the tight coupling of computing and storage.
  • Data lakes offer data scientists and models plenty of options for analysis - but might not provide the definitive, actionable information decision-makers need.

The "data lakehouse" is a compromise attempt to bring in the strengths of both models. It provides the readability and structure of a data warehouse with the scalability and agility of a data lake.

Take a look at our in-depth article on data lakehouses for more information. 

Modern Data Warehouse Technology

Data Lakehouse technology provides great flexibility when managing and analyzing data, but it's not the only option. Leveraging modern data warehouse technology can provide businesses with a robust infrastructure that can handle large amounts of data quickly and efficiently.

For example, cloud-based data warehouses provide an alternative to on-premise solutions, allowing businesses to take advantage of the scalability and cost savings that come with a cloud infrastructure.

Cloud-Based Data Warehouses

In the past, data warehouses required physical, on-site servers. These days, companies have either moved their information systems to cloud-based data warehouses already or they’re considering it. 

Here are the benefits of a cloud-based data warehouse:

  • Zero startup costs: It used to be very expensive to purchase and install the hardware for physical, on-site servers. With cloud-based data warehouses, you don’t have to invest in any hardware when you launch a cloud-based server. Just select the server configuration you require via the internet, launch the server, and you’re ready to go. Instead of buying expensive equipment, you pay a SaaS (software as a service) fee as you go.
  • Near-instant deployment: Data warehousing formerly required painstaking preparation to ensure you purchased the right equipment. However, with cloud-based data warehouses, if you don’t estimate your needs correctly, you can upgrade the solution by adjusting the server configuration. This eliminates the need for complicated preparations before launching your data solution.
  • Scalability and cost elasticity: Another financial benefit of cloud-based data warehousing is that you only pay for what you need as you need it. Let’s say you have to run a lot of complex queries in the summer months—so you’ll pay more during those months. The rest of the year, when your data needs are less, you won’t pay as much in costs. Your data integration solution can scale up or down with you as required.
  • Faster, better insights: Businesses used to suffer from sluggish server hardware and crippling storage constraints because they weren’t financially ready to invest in an upgrade. The elasticity of cloud-based solutions eliminates the threat of “slow query syndrome” to deliver faster, better BI insights.
  • Eliminate server maintenance costs: Cloud-based data warehouse users enjoy automated patches, upgrades, and security updates. They also automate many of the tasks you need an in-house tech team to implement. This reduces your server maintenance costs and frees up your technical team and developers to worry about more important issues. 

The most popular cloud-based data warehouses include Redshift, Snowflake, Db2, and Google BigQuery. The most popular on-site data warehousing solutions—including IBM, Microsoft Azure, Teradata, and Oracle—have also developed hybrid platforms with a mix of cloud and on-site features.

Further Reading: What to Consider When Selecting a Data Warehouse for Your Business

Automated ETL Tools

Automated ETL (Extract, Transform, Load) tools are another modern data warehouse technology businesses can leverage to streamline their data workflow. ETL tools allow for automatic and frequent data integration from multiple sources into a unified database. This helps ensure that businesses are able to quickly access and effectively use all of the available data stored in their warehouses without the need for costly technical teams.

In the past, integrating incompatible data formats into a data warehouse required time-consuming and costly hand-coded programming. These days, cloud-based ETL tools like help you integrate diverse types of structured and unstructured data into your data warehouse and BI solution.

How Automated ETL Tools Help Integrate All Types of Data

The benefits of automated ETL tools like include:

  • Fast and easy connections: With one-to-one, hand-coded integrations it could take months to establish a reliable data connection between a particular data source and your data warehouse. Maintaining these connections after they're built presents more time-consuming challenges. However, cloud-based data integration services like have pre-built connectors and adapters to instantly connect your valuable data from services like Salesforce, Facebook, Google services, Excel, MySQL, and more.
  • Access more data: By integrating previously incompatible data, you open your data warehouse and BI tools to more information for better, more accurate reporting to support better business decisions.
  • Real-time availability: The faster you get the BI insights you need, the better decision-makers can lead your organization. When your competitors adopt real-time reporting systems, receiving insights and reports once or twice a day won’t allow you to be competitive. Reliable data integration is the best way to achieve real-time reporting like this.
  • Improved data quality and integrity: Data integration strategies help to preserve data quality and data integrity when integrating different information into your data warehouse. This supports your BI solutions to provide more accurate insights. 

The Future of Data Warehousing

As we move forward, the lines between data lakes and data warehouses will blur. The focus will shift towards real-time analytics and more integrated BI platforms. With the growth of AI and machine learning, predictive analytics will become a cornerstone of business intelligence, leveraging data from warehouses to forecast trends and make proactive decisions.

Data warehousing remains an essential tool in the ever-evolving landscape of business intelligence. As businesses generate more data, the importance of efficiently storing, analyzing, and leveraging this data becomes paramount. Staying updated on the latest trends and technologies in data warehousing is vital for businesses to maintain a competitive edge. and Data Warehousing

We hope we've provided a good response to the question "What is a data warehouse?" Hopefully, by now you should have a good understanding of data warehouses and why they are important in modern business. Now, you've got to set up a data warehouse and load all your different sources of information into it. That's not easy--unless you have the right people and tools.

If you or your BI team have delayed integrating key data streams into your data warehouse and BI platform, is here to help. is a new data integration platform, leveraging ETL and ELT technology to link to all your existing business data.

A super-fast CDC (change data capture) option allows you to bring across historical data and then only update it as and when changes are made. This promotes a much more efficient data warehouse and prevents it from becoming slow and clunky which can impact the effectiveness of your BI tools.

Reverse ETL allows you to take data from the data warehouse and upload it into other systems, like Salesforce. This keeps your systems up-to-date, but also improves the experience of your customers or clients, as real-time data updates make the systems they have access to more accurate if you set up automation to handle this.’s intuitive interface has a shallow learning curve for end users, employing a no-code environment with drag-and-drop options for creating data pipelines. There are over a hundred pre-built connections right out of the box, plus API creation and management for any services or data that fall outside the many popular integrations the platform supports. Schedule an intro call today to find out how data warehousing is so much simpler with