Data warehousing (and all the processes and technologies involved with it) is a vast but important subject. In this guide, we'll help you understand the topic of data warehousing and the question of "What is a data warehouse?" across the following sections:

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.

The following graphics from iMPACT show just how many companies are devoting resources to business intelligence. Note that these graphics refer to business intelligence as "CI" (competitive intelligence).

The first image shows the number of employees dedicated to business intelligence by size:

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*Image source: iMPACT. These graphics refer to business intelligence as "CI" (competitive intelligence).

The next image shows the number of employees dedicated to business intelligence by annual contract value:

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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

A data mart is a repository that holds data relevant to a group of users with common needs, such as a business department. Related reading: What are Data Marts?

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. Related reading: What is a Data Warehouse?

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 the long term analytics.

How Do Data Warehouses Work?

A data warehouse collects data 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 (related reading: What is ETL?). 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 the departments of your company. 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
  • Forecasting that predicts 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.

 

Related Reading: The True Cost of a Data Warehouse

Some of the most popular data warehouse platforms include:

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Modern Data Warehouse Technology

Now that you understand more about data warehouses and BI solutions, let's look at the latest technology to look for when 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. For example, Nielsen recently moved the processing for its National Television Audience Measurement service from on-site servers to Amazon Web Services' (AWS) cloud-based ecosystem.

Nielson's Head of Product, TV, and Audio, Scott N. Brown, explains why moving to a cloud-based system was important:

"The move to a cloud-based engine is a significant undertaking and underscores Nielsen's commitment to invest in technology that will benefit our clients and the industry as a whole. Cloud processing allows for greater flexibility and velocity, as we build new and enhance existing products. It will also give us the opportunity to spend more time innovating and less time on managing infrastructure."

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) fees 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. This translates into cost elasticity because your data solution can scale up or down with you as required.
  • Faster, better insights: Businesses used to suffer with 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. 

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.

 Related Reading:

Automated ETL Tools

In the past, integrating incompatible data formats into a data warehouse required time-consuming and costly hand-coded programming. These days, automated ETL (exchange, transform, load) tools like Xplenty 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 gets 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 Xplenty before integrating with the rest of the data warehouse. You can browse the many built-in structured data integrations that Xplenty offers here.

Unstructured data

Even though data warehouses can only work with structure data, most of the information in the world gets classified as unstructured, which means it lacks a defined organization or schema. According to the Journal of Accountancy, unstructured data could include the following:

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. According to some experts, 80 percent of the world's data will be unstructured at or around the year 2025. 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 Xplenty 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 Xplenty 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, a traditional data warehouse can't store or work with unstructured information. That's where a "data lake" comes in.

Related Reading: How to Make a Data Lake Worthwhile for Your Business

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.

According to a survey from Arcadia Data, firms using data lakes are enjoying excellent results:

  • 76% of survey participants believe “BI/analytics for our data lake increases the number and value of analytics for business users.
  • 72% of survey participants felt the data lake they use “fosters better decisions and actions by business users.

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. In this example, a business intelligence team analyzed employee performance, first by looking at the structured data and then by looking at the unstructured data. The structured data masked biases that only became clear after the BI team applied machine learning algorithms to the unstructured free-written portions of the surveys. The unstructured data analysis revealed systemic age and gender biases in the language managers used to describe certain genders and ages of employees. Because data lakes allow your BI tools to analyze unstructured data, they can open up the door to previously invisible insights like this.
  • 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? 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 to 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. 

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Xplenty and Data Integration

We hope we've provided a good response to the question of "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, Xplenty is here to help. When you use Xplenty's out-of-the-box ETL solutions, integration is quick, fast, and painless. Why wait? Contact the Xplenty team now to find out how our technology can blow through your data integration bottlenecks.