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The Complete Guide to Building a Data Warehouse
Articles- Feb 03,23
Data warehouses improve enterprise decision-making while saving time and money, says Emily Newton.
A data warehouse is a unified data-management structure that collects and organizes enterprise data from a variety of sources. A repository like this supports higher-order data analysis and business intelligence and simplifies data organization and governance.
There are many benefits of building data warehouses, including:
- Supports faster-paced decision-making
- Raises the bar for data quality, usefulness and organization
- Simplifies enforcing conscientious data culture
- Unifies infrastructure and policies to minimize the chances of data leaks and breaches
If you need to build a data warehouse, here’s a guide to some of the important decisions you’ll make along the way.
1. Decide between cloud and on-premises options
The primary reasons to invest in data warehouse architecture are typically to:
- Consolidate an organization’s data regardless of the source or format.
- Create a single source of truth for all departments, partners and stakeholders
- Support predictive capabilities, analytical capacity and advanced enterprise decision-making.
Cloud data warehouses take the central concept of data warehouses to their logical conclusion. Whereas legacy, on-premises data warehouse designs may have high operational costs and inflexible architecture, cloud-based warehouses are agile, scalable and built for collaboration.
As you build a data warehouse, this step will require a careful appraisal of your organization’s scope and ambitions. An on-premises data warehouse may be perfectly capable of supporting your mission for the foreseeable future. If rapid growth is part of your business plan, strongly consider cloud-based infrastructure for your data warehouse instead.
2. Anticipate a gradual adoption
It all comes down to the size and complexity of your organization, but if you want to build a data warehouse with modern data analysis and predictive capabilities, it could take months or years. Data warehousing experts recommend against taking an all-at-once approach to this process. Business requirements change over time and trying to solve every problem at once is a good way to court failure and wasted time.
Take a gradual, agile approach instead. Avoid thinking of building a data warehouse as a single task – imagine an iterative process instead. Additionally, don’t undertake any portion of this digital transformation without creating a strategy for continuous feedback.
Ensure the motivations and goals are clearly defined and understood by all stakeholders and everybody tasked with importing, cleaning, organizing, or otherwise handling data. For modern enterprises, such incentives might include:
- Achieving standardization of enterprise data.
- Improving the speed and quality of enterprise decision-making.
- Raising profitability and reducing the cost of doing business by identifying process bottlenecks and market opportunities.
- Helping departments collaborate more seamlessly by sharing actionable data and unifying access processes.
Deploying data warehouse technology in “quick sprints” will keep you focused on your specific aims and operating within your allotted budget. Data warehouses routinely save organizations money, but only if they take a logical and measured approach to adopting the required technologies.
3. Analyze, organize and understand your available data
Data warehouses make it easy to analyze structured and unstructured data from disparate sources. How do you get maximum value and utility out of a data warehouse? You begin by making sure the pools of data you’re trying to unify are organized and labeled and the dependencies between those data sets are clearly defined.
This will make more sense once you adopt an enterprise-wide data model. Your enterprise data model is the tool various personnel will use to understand the nature and purpose of the data they’re organizing or using in their workflows. An enterprise data model will:
- Define what kinds of data are generated and consumed by the organization and where it comes from.
- Describe how structured and unstructured data apply to enterprise planning, corporate values and ongoing goals.
- Create and enforce rules for data access, transmission and alteration.
An enterprise data model exists independently of the infrastructure used to gather and store data. It’s more of a formal collection of written definitions, expectations, access protocols, handling and data recovery instructions, and governance requirements.
Done correctly, it will explain what kinds of data are valuable to the company, the sources it’s gathered from and the form it takes, where it should be stored, who should have access, and instructions for backing up critical data stores. Any data system design – including any variant of a data warehouse – should begin with an enterprise data model already fleshed out and tailored to the organization.
4. Set up the warehouse for batch or real-time loading
Another decision stakeholders will consider is whether to set up the data warehouse for batch processing and loading or to target real-time processing instead.
The advantages of real-time data loading and processing are obvious, but they’re not essential for every enterprise. If speedy processing of transactional or medical data is important to you, then the architecture of your data warehouse will need to support real-time loading and analysis.
Batch processing is highly efficient both time-wise and in terms of the financial outlay. It’s a method that will appeal primarily to organizations making changes and decisions more deliberately and conservatively versus one that needs to stay nimble in the face of rapidly changing circumstances.
5. Separate storage and compute infrastructure
Another consideration worth mentioning is the ability to separate a data warehouse’s computing and storage resources. Modern optimization techniques mean there’s little or no performance impact from doing this, yet it delivers several potential advantages:
- The ability to shut down compute nodes at night – or when not needed – to save energy and money
- Any computing and storage resources can be scaled up or down independently as business needs change
- A higher fault tolerance because storage nodes remain active if compute nodes become unreachable
- Different teams can have dedicated infrastructure, so costlier computations and workflows don’t affect other divisions or budgets
Your data warehouse partner will explore in more detail with you whether your business model and computational demands support this type of infrastructure.
How to build a data warehouse
Data warehouses can save enterprises time and money while making their decisions timelier and more effective. Embark on this journey with clear goals, an iterative process already roadmapped and an understanding of how your business structure and processes will change your data warehousing needs. If you do, you should be well-prepared for success.
About the author:
Emily Newton is a tech and industrial journalist and the Editor-in-Chief of Revolutionized Magazine. Subscribe to the Revolutionized newsletter for more content from Emily.
*Image Courtesy: Freepik.com
Image by <a href="https://www.freepik.com/free-vector/server-room-cloud-storage-icon-datacenter-database-concept-data-exchange-process_3628676.htm#query=data%20warehouse&position=14&from_view=search&track=sph">Image by fullvector</a> on Freepik
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