Data Warehouse: The Ultimate Guide
Data is still king. But none of it is useful if you don’t have a reliable reporting and analysis system in place to make sense of it.
Company owners want to consolidate and integrate their data to facilitate different levels of aggregation, ranging from customer service to top-level executive business decisions. This is precisely where data warehousing comes into place because of its simplified reporting and analysis benefits, making decision-making easier.
In this guide, we’ll delve deeper into the concept of a data warehouse, its applications, and how you can design and build one for your business.
What is a Data Warehouse Anyway?
A data warehouse is a type of software that serves as a platform to host a database of information. It’s responsible for collecting and managing data from varied sources to provide meaningful business insights.
Metaphorically, a data warehouse is like a beehive: it comprises multiple combs (databases) that bees constantly refill with nectar and pollen (data) from different neighboring fields and meadows (a variety of input sources).
You can think of it as a centralized data management system that consolidates all the company’s information from multiple sources in a single storage.
How Does a Data Warehouse Work?
Data warehousing is similar to a modern distribution center.
A distribution center receives and stores products, materials, and supplies in a safe and organized environment. A data warehouse does the exact same thing, except instead of materials and products, it stores data.
Basically, a data warehouse collects and stores data across several organized, individual files. These warehouses receive data from different sources like relational databases and transactional systems. Once ingested, the data is cleansed and normalized before being put into a dedicated database, depending on its type, format, and other characteristics.
Data scientists generally access and retrieve this data through SQL clients, business intelligence tools, and other similar applications.
Data Warehouse Example #1: Investment and Insurance Sector
Data warehouses are mainly used to analyze customer and market trends and other data patterns in this sector.
Data warehousing has a significant impact on forex and stock markets, two major sub-sectors, where even a one-point difference can lead to huge losses. Here, warehouses are usually shared and focused on real-time data streaming.
Data Warehouse Example #2: Healthcare Sector
A data warehouse is used in the healthcare sector to produce treatment reports, forecast possible outcomes, and share crucial data with insurance providers, research labs, and other medical units. In fact, many think these warehouses are the very backbone of healthcare systems, considering how vital treatment information is for saving lives.
Data Warehouse Example #3: Retail Chains
In the retail sector, data warehouses are mainly used for distribution and marketing purposes to track items, keep an eye on promotional deals, and analyze pricing policies and consumer buying trends. Retail chains commonly use the enterprise data warehouse for data intelligence and forecasting requirements.
How to Get Started With a Data Warehouse
Here, we’ll give you an overview of how you can create your own data warehouse to set yourself up for success.
Step 1: Define the Problem
The first step in building a data warehouse is to understand and define the problem and then develop a solution. You must know what data needs to be available, the kind of organization that needs to follow, the transformation to make sense of the data, and so on.
Going through all these aspects will give you a clear set of requirements, which you can then use in step two. For your requirements management, you need to figure out how to:
- Align department goals with the overall project objectives
- Decide on the overall project scope and its relation to your business objectives
- Uncover your future needs and current needs by going through all your data to know what data will help you for analysis) and your existing tech stack, i.e., where your data is currently siloed or where it’s not being put to use
- Prepare a disaster recovery plan for emergencies like a system failure
- Think hard about every security layer for your data warehouse, such as threat detection, threat mitigation, identity controls, monitoring, risk reduction, and so on
- Forecast compliance requirements and remove regulatory risks, if any
Step 2: Identify All Data Sources
A data warehouse pulls data from different sources (also known as data marts). Your job is to identify all the data sources that give you the necessary data to achieve your goals and identify the essential data points/elements from them.
Keep in mind that these data sources can be of any type. You can have an SQL/NoSQL database, various applications, social media, Excel/CSV files, surveys, and sensors/IoT, among others. If you need any data, make sure you feed it into the warehouse.
Step 3: Develop Your Data Model
At this stage, you’ve defined all the data sources and data elements. Next on your list is to create a centralized database for all the elements to form your warehouse.
A database model highlights all the entities and/or objects required for creating a data warehouse and its properties. Snowflake Schema, Star Schema, and Galaxy Schema are generally the three most popular data models for warehouses.
Try to select and develop a data model that guides your overall data architecture within your data warehouse. Remember, the model you select will affect your warehouse structure and data marks, which, in turn, will affect the ways you use ETL tools and run queries on that data.
Moreover, any good data modeling tool can help you engineer the model into a database schema in your RDBMS.
You have to create the diagram of the entities/objects and the relationships between them in the modular and then export it to your database to get things started. Just try to choose a tool that can integrate easily or generate the schema SQL for the RDBMS you plan on using.
Step 4: Do the ETL (Extract, Transform, Load)
After identifying the data sources, data elements, and the warehouse database, you have to figure out how to get the data into the database for analysis. This is what we call Extract-Transform-Load, or ETL.
ETL refers to the process that allows you to pull data out of your current tech stack or existing storage solutions before placing it into your data warehouse.
Here, you’ll extract data from the sources and then load it into the warehouse database. You can use APIs and data files that can be imported into an ETL tool (e.g., Talend) for the extraction.
All the data elements have to be connected or integrated so that they automatically pull from all data sources and intervals and feed them directly into the database. As you may have guessed, the ETA tool connects the data sources and the database and then loads the data from the sources into the database.
Considering ETL takes care of the bulk of the in-between work, you simply cannot select a subpar ETL tool or develop a deficient ETL process. It’ll only break down your entire warehouse. It’s why you should pay careful attention to the ETL solution that you use.
Instead, you need an ETL tool with optimal speeds and good visualization that lets you build straightforward, replicable, and consistent data pipelines between your new warehouse and all of your existing architecture.
Step 5: Document All the Steps and Policies
A successful data warehouse should be a functional part of a company’s operation that evolves as and when the business and data sources evolve. Documenting how things were set up, policies, and conventions for the data warehouse development will help you ensure continuity and easy maintenance down the line.
This step is more operational in nature to make things convenient for you once your warehouse is set up and ready for use.
Step 6: Leverage Business Intelligence and Analytics
Your database already has all the data you need for analysis. All you need now is to create data visualization tools like charts, tables, and grids to reach your ultimate goal of making better-informed decisions.
Note: You should’ve already defined the visualizations you need in step one. If you haven’t, go through all your requirements again to understand what you’ll need based on your objectives.
You can also use business intelligence tools like SAP BI, Power BI, Tableau, and so on for effective data visualization based on your environment, configuration, and budget. If you have the expertise and budget, you can also develop a custom one for your requirements.
Understanding the above steps and tools for each stage will help you develop a reliable data warehouse that can genuinely help with strategic decision-making.
Don’t try to rush into designing and building a data warehouse. Take time from your schedule and research every aspect thoroughly to understand what truly goes into its creation. This way, you’ll have a reliable system that helps you make the right decisions related to your business, accelerating its growth and profitability.