What is a Data Warehouse?

The Data Warehouse is your company's Central Data Store. All businesses require a Data Warehouse since it serves as the "Single Source of Truth" for all data in the organization.

At first, you may be employing your normal database to execute SQL queries for analytics. However, as the quantity of data and people using it to conduct various analyses grows, your standard database becomes prohibitively sluggish when performing query processing.

This is where businesses recognized the necessity for Data Warehouses, which are built to handle huge quantities of data. It enables you to efficiently filter, sort, aggregate, and analyze the data.

Business/Data Analysts may use all of the data at their disposal thanks to this capability. The majority of data analysis cases fall into one of these two categories.

1. Measuring Performance – To evaluate how various activities are performing within the company. E.g. Evaluating Sales Performance across certain product lines.
2. Validate Certain Hypothesis – The idea of testing and retesting a hypothesis in relation to growth, revenue, engagement, etc. This is to find new information or to confirm previously undetected possibilities. For example, are the consumers who were acquired through Google more likely to buy more than those purchased through Instagram?

The warehouse comprises data from a variety of sources, including internal databases (many different Databases from various systems and Microservices), behavioral data (data on how customers use your product across various digital platforms), and third-party SaaS applications including Google Analytics and your CRM.

Data Warehouse Basics

A Data Warehouse is a type of data storage and management system that allows organisations to extract and analyze information from multiple sources and integrate it with other data sets. The need for Data Warehouse to provide business reports, analysis, data integration into BI tools, and ad-hoc queries becomes an important element of efficient processing.

In short, a Data Warehouse is an upgrade beyond standard databases offering a permanent storage facility with greater computing capacity to analyse and process data.

A key function of the Data Warehouse is to consolidate data from multiple sources, such as Applications (APIs), Databases, Cloud Storage, and other sources using ETL (Extract Transform Load) technique.

If you haven't already read our 'What is a Data Pipeline?' post it's worth a read and will introduce you to the different 'Varieties' of data that have come to define part of modern Data Pipelines which feature heavily a mix of data formats from a variety of sources.

There are numerous products that can assist you in loading data into a Data Warehouse and automatically modifying it. This is a crucial part of building and maintaining a Data Warehouse that is fit for its purpose.

Data Warehouse Basics in summary:

1. A Data Warehouse combines data from many sources and stores it in a form that is immediately workable for analysts, data scientists, and other executives.
2. Data Warehouses are built to be fast to query and when routinely paired with a graphical interface (unlike a standard database) allow users to create notations, field alterations, and metadata to help others understand the data.
3. Data Warehouses save the user time, delivering clean, quality data quickly all in one place.

Do I need a Data Warehouse?

The first question is, why do I need a Data Warehouse over my existing systems, particularly if we already have costly BI tools? This was the state of things for some time but gradually, enterprises and now even medium-sized companies have realised  the need for Data warehouses. Let's take a look at some of the reasons why utilizing a Data Warehouse is so crucial in Business Analytics and ultimately making data-informed decisions.

Serving as a single location for all company data, aligned between teams and projects giving full control over access between departments and execs - using a Data Warehouse assures the following:

• Quality, standardised data
• Unstable data in reports
• Data consistency even between multiple sources
• High-speed querying
• Fast analysis even with big data volumes
• A buffer between databases and BI reporting tools meaning that changes in source data won't affect business reports. (Validation of data into the Data Warehouse is an essential part of ETL processes - see below)
• Added scalability with Cloud Data Warehouse products. Services from Google, Amazon & Microsoft allow flexible scalable pricing aligned to storage, querying volumes, and compute power allowing you to scale costs as your needs and capabilities grow.

Challenges of Setting Up a Data Warehouse

The number one challenge we hear from customers is combining data in a uniformed way from a variety of sources all in real - or close to - real-time, as the sources' contents constantly change. Even the form of the data that comes from these sources can change regularly with erroneous values and metrics constantly thrown into the mix. It's critical to have more control over exactly what data is being transferred and how it's organised.

ETL (Extract Transform & Load) is a vital, intermediary process in building and managing a Data Warehouse and assuring that only the very best data - assessed by the quality and in relation to your analysis/reporting objectives - make the cut.

Scalability is a huge factor as a constant stream of data from multiple sources over time gets is exponentially bigger. It is then vital that you have the system architecture in place to assure that querying and general usability is not hindered as the volume, variety, and velocity of data increases.

A reliable and robust architecture to gather data from various sources is the most important step towards ensuring that your teams trust the data to make decisions.

Conclusion

You now know what a Data Warehouse is, the need for them and why many companies and organisations are pursuing them to make more and indeed better data-driven business decisions saving time, resources, and frustration in the process.

Data Pipelines was built to help you with the ETL part of the process, seamlessly connecting to almost any source (we add more upon request) and allowing users to transform data (with our built-in no-code tools) and then automate it to single or multiple new locations from self-hosted or cloud-based Data Warehouses to a simple Google Sheet. Data Pipelines comes with a limited free Adopter account and a 7-Day Free Trial of our Processor package. Get started here.