Our five key takeaways:

  1. What is a data warehouse? It’s a repository for enterprise data from various sources.

  2. Businesses need access to data from a disparate range of sources, from cloud-based SaaS to on-premises operational systems and databases.

  3. Being able to access all this data in one place allows data analytics tools to provide better business insights and decision-making.

  4. Effective data warehouse solutions should be able to handle large amounts of data, often simply called “big data.

  5. Data integration solutions like Integrate.io leverage ETL, ELT, and other methods to ensure your data warehouse collates as much business data as possible.

What is a data warehouse? A data warehouse is, simply put, a digital repository for all your organization’s data. Being able to access your company’s data is critical to business success. The data warehouse market value is set to rise to $51.8 billion by 2028, so understanding the benefits of a data warehouse for your business is important. In this guide, we'll help you understand what a data warehouse is while exploring how an effective data integration platform like Integrate.io makes your data warehousing solutions even more powerful.

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 (BI) systems so they can query it as a single, integrated whole. 

When considering what a data warehouse is, the term "business intelligence platform" is also important. BI platforms access the aggregated data within a data warehouse to query and analyze it to create profit-boosting insights. That’s why it’s so critical that you get complete and accurate data from as many business systems as possible.

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. 

Types of Data Warehouses

Here are the main types of data warehouses:

  1. Data mart

  2. Enterprise data warehouse

  3. Operational data store

Data Mart

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 Integrate.io.

Popular Data Warehouse Platforms

Some of the most popular data warehouse platforms include:

Modern Data Warehouse Technology

Now that you understand more about what a data warehouse is and BI solutions, let's look at the latest technology for planning your data strategy.

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 needed 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. 

Automated ETL Tools

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 Integrate.io help you integrate diverse types of structured and unstructured data into your data warehouse and BI solution.

Structured vs. Unstructured Data

Structured Data

Technically, data warehouses can only incorporate data with a defined schema, i.e., structured data that’s organized in a way that lets you search it and run queries on it. 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.

While your data warehouse should be able to work with most forms of structured data, you may need to transform the data into a compatible format with an ETL (extract, transform, load) solution like Integrate.io before integrating with the rest of the data warehouse. You can browse the many built-in structured data integrations that Integrate.io offers here.

Unstructured Data

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.

It's worth noting that some of this information is semis-structured, like emails. Emails have a loose structure that includes unstructured text content, (the bulk of the data) but it also includes structured, searchable information like the sender, recipient, and timestamp.

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. Therefore, 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.

How Automated ETL Tools Help Integrate All Types of Data

The benefits of automated ETL tools like Integrate.io 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 Integrate.io 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. 

Data Lakes

There are some types of data that either you can't apply relational structures to with ETL solutions or you have not yet applied relational structures to. 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 "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.

Integrate.io and Data Warehousing

What is a data warehouse? Only as effective as your data integration solutions. Without access to all your disparate data sources, your data warehouse is an ineffectual and potentially expensive tool that’s not living up to its potential. Integrate.io 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. 

Integrate.io’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 Integrate.io.