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Data Warehouse 101: What You Need to Know in 5 Minutes

Article Written By Arpit Malviya with support from Bridget Mess, John Latham, Jules Perryman, Ian Linsell, and Sandeep Ghosh, Perficient

A data warehouse, or enterprise data warehouse (EDW), is a system to aggregate your data from multiple sources so it’s easy to access and analyze. Data warehouses typically store large amounts of historical data that can be queried by data engineers and business analysts for business intelligence.

Instead of only having access to your data from individual sources, a data warehouse will funnel all your data from disparate sources (transactional systems, relational databases, and operational databases) into one place. Once it’s in the warehouse, it’s accessible and usable across the business to get a holistic view of your customers. When your data is in one place, you can analyze related data from different sources, make better predictions, and ultimately make better business decisions.

There are two ways to go about implementing a new data warehouse. You can have one on-premises, designed and maintained by your team at your physical location, or you can use a cloud data warehouse—one that lives on the cloud and does not require any physical hardware. Cloud data warehouse architecture makes it easier to implement and scale, and they can be less expensive than on-premise data warehouse systems. Below, we’ll discuss what to consider for the best data warehouses.

Data Warehouses vs. Databases: How They’re Different

Databases and data warehouses are related but not the same.
A database is a way to record and access information. A database often handles real-time data to support day-to-day business processes like transaction processing. A data warehouse is a way to store historical information from multiple sources to allow you to analyze and report on related data (e.g., your operational daily production data, supply chain data, well data, etc.) Unlike a database, the information is not updated in real-time and is better for data analysis of broader trends.

Data Warehouses & Data Lakes: How They Work Together
Data lakes are largely used for storing any unstructured data that may or may not yet have an intended use case. Data Lakes are typically the landing zone for raw data sources. On the other hand, a data warehouse holds curated and filtered data, ready to be used and analyzed.

A data lake, hosted on big data platforms, is ideal for data scientists and analysts to store raw data until they know what they want to do with it or as a repository to store large amounts of unstructured data.

A data warehouse stores structured and semi-structured data that can then be used by businesses to gain business intelligence and by data scientists to perform predictive analytics.

When and Why to Use a Data Warehouse
If you’re bored with the insights your current analytics tools can provide, it’s time to integrate a data warehouse into your tech stack. You’ll be able to dive deeper than you can with individual database management.

When to Use a Data Warehouse
You should consider a data warehouse if you want to:

  • store all of your historical data in a central repository
  • analyze your web, mobile, CRM, and other applications together in a single place
  • get deeper business insights than traditional analytics tools by allowing a broad range of people to analyze the data directly using the tools and languages that meet their business needs
  • provide multiple people access to the same data set simultaneously

Why to Use a Data Warehouse

Data warehousing helps you answer those tough analytical questions that your board may be asking that aren’t possible to address with your standard data analytics tool. Reports and analyses you run in data warehouses can include elements from every one of the data sources you’ve connected to it—pretty powerful stuff! This data integration means you can analyze data from your website and app and other platforms you may use.

For example, some platforms can give you a good sense of customers’ actions on your website or app. However, you may be limited to asking questions that can be answered with the number of variables, properties, and types of charts that it provides. For example, when you hook up Google Analytics to your data warehouse, you can tie that information to data from your CRM, sales platform, and so on for a complete view of your customers.

When you have all your data in one place, you can efficiently run queries directly in your warehouse or through a business intelligence tool to automate and visualize those queries and aid decision-making.

6 Factors to Consider When Choosing a Data Warehouse

Now you know the benefits of a data warehouse—but how do you pick one? Consider these factors when determining which data warehouse best suits your business needs.

1. Data Types
There are three types of data that you might want to store for your business: structured, unstructured, and semi-structured. Most data warehouses support structured and semi-structured data management, but unstructured data is better for data lakes.
  • Structured data is quantifiable data that can be organized neatly into rows and columns (e.g., sales records or customer contacts).
  • Unstructured data is data that can’t be easily managed and analyzed. Think of written content (like blog posts or answers to open-ended survey questions), images, videos, audio files, and PDFs. If you’re looking to store purely unstructured data, you should consider a data lake instead of a data warehouse.
  • Semi-structured data is a mix of structured and unstructured data. Take an email, for example. The content of that email is unstructured, but there are quantifiable aspects to the email, such as who sent it, when they sent it, when it was opened, etc. Similarly, an image itself is unstructured, but you also often have access to structured data, like when the photo was taken, device type, photo size, geotags, etc.
  • Data Quality - you must consider data quality in the warehouse design and when doing analytics. Amalgamating data from multiple sources into a warehouse can merge semantic conflicts, etc. Although you may have access to a plethora of data sources, that does not necessarily mean that every source should be ingested.
2. Scaling for Data Storage
Most data warehouses typically allow you to store massive amounts of data without much overhead cost. You probably won’t need more than what they offer, especially if analytics is the primary use case.

However, you’ll want to consider how a particular warehouse scales data storage during times of demand. For example, some platforms will require you to manually add more nodes (the basic structures in data warehousing that store data and execute queries) when you need more storage and computing power. On the other hand, others offer an auto-scale function that dynamically adds and removes clusters of nodes as needed.

3. Scaling for Performance The performance of a data warehouse refers to how fast your queries can run and how you maintain that speed in times of high demand. Performance increases as you scale up the nodes in your warehouse. When looking at performance optimization and scaling it’s important to consider the amount of compute resources that you’ll need to complete all your processing needs within your SLA or time constraints, especially if there are large volumes of data or concurrent pulls on your warehouse cluster.

Like a data warehouse’s storage scales, you can add and remove nodes for faster queries. For some warehouses, you need to do that manually, but you’ll be able to tune it as precisely as you like. With others, it will happen automatically for a hands-off experience.

4. Maintenance
You likely want your engineers focused on building and maintaining your products instead of worrying about ETL pipelines and the day-to-day management of your warehouse—especially if you have a small team. In that case, you’ll want a self-optimizing data warehouse.

However, by maintaining your warehouse manually, experienced data warehouse architects can have greater control and flexibility to optimize it precisely for your company’s needs.

5. Ecosystem
Consider using a data warehouse within the ecosystem of the applications you already use. For example, most cloud platform providers offer one or more data storage solutions suitable for data warehousing. These are easily usable within the platform and inherently work with the other capabilities of the platform. This will simplify implementation since you already have an infrastructure in place.

You may still need to write custom solutions or use other vendor’s products to get data into your warehouse, but the goal is to minimize that work.

6. Cost
Many factors go into data warehouse pricing, including storage, warehouse size, run time, and queries. For some warehouses, you pay per hour based on nodes or per bytes scanned. Other options, however, have both a flat-rate and a per-query model. Ultimately, you want to choose the data warehouse that will meet your business requirements, not just the cheapest option.


Find the Right Data Warehouse for your Business
Ready to add a data warehouse to your stack? Before you choose a tool, don’t forget to consider:

  • the type and volume of data you want to store
  • how dynamically do you need it to scale
  • how fast do you need your results
  • number of concurrent users
  • your use case(s)
  • growth / scalability needs
  • whether you want manual or automatic maintenance
  • the compatibility of the data warehouse with your existing tech stack
  • Enabling capabilities, and your costs

Once you have selected the data warehouse that best fits your business needs, you’ll be well on your way to becoming a data driven organization and being able to leverage your data to strategic decisions and drive growth. You’ll be able to analyze it, identify trends, make better predictions for the future—and ultimately make better business decisions.