Over the years, we’ve observed massive shifts in the way that data warehouses are built. The two most significant innovations in the last few years are cloud computing and text analytics. Despite the changes in architecture and new-found applications, data warehouses’ core functions and purpose remain the same: consolidated, accurate, reliable, timely, complete, insightful information to support decision making.
In this second instalment of our series, read on as we discuss the implications of cloud computing and new data sources on your data warehouse decisions.
What You’ll Learn
Like many buzzwords thrown around in 2021, the meaning of cloud computing can be unclear. Simply put, it’s all about ownership. Think of a cloud service as a car lease. You pay a monthly amount to use a vehicle for a fixed period. After that, the car doesn’t belong to you; it goes back to the dealership. Almost everything can be “cloudified” these days, and data warehouse components are no exception. In our previous blog post, we talked about data management tools, data storage options, query managers, analytics tools, end-user access tools, and hardware. All of these are offered as a cloud service.
Data warehouses allow businesses to formalise how they discover insights. As a result, they can innovate faster than ever before.
Cloud Data Warehouses enable more organisations to experience the benefits a data warehouse can bring to your company. Lower initial capital outlays are just the beginning. Here are a few more:
Cloud providers maintain servers stored in secure facilities with the optimal temperature, round-the-clock security, with layered redundancies. If one server facility is unavailable, a separate facility can pick up where the last one failed. This ensures your applications will always be accessible.
Cloud providers use high-end hardware that may have been out of a smaller organisation’s budget. By sharing the resource, economies of scale are met, and pricing becomes less prohibitive.
With as-a-service models, cloud data warehouses can adjust to an organisation’s demands at a specific point in time. Companies can reallocate resources quickly depending on the current requirement. This flexibility means that you only pay for your actual usage, not fixed amounts.
Consuming what you need and sharing resources with others doesn’t only make our bottom lines healthier; we’re also doing the planet a favour.
By outsourcing functions that aren’t part of your core business, your team is free to focus on what they do best.
Before cloud-based options, an organisation needed to buy hardware and software to implement a data warehouse. Now, vendors typically offer pay-per-use options at much lower rates. For an SMB, an upgraded Internet connection is probably all that is needed to access such advanced tools.
A data warehouse is considered a cloud data warehouse when at least one of its components uses a cloud-based tool. Needless to say, a data warehouse can be fully on-premise or legacy, fully cloud-based or a hybrid of the two.
Another major innovation in data analytics is the use of newer sources of information. With improved artificial intelligence and machine learning algorithms, many type of unstructured data can now be processed.
Unstructured data is information that cannot be modelled in table form. At the risk of oversimplifying it, images, video and long forms of text are stored and managed through hierarchies and metadata tags.
Data warehouses are for structured information, while data lakes can be used for both structured and unstructured data. A Data lake is an excellent storage option if your data does not fit into a data warehouse or has not yet been modelled. You can store the information in a data lake until such a time you are ready to process it. As David Loshin writes, a data lake is “a resting place for raw data in its native format until it’s needed.”
What’s Hiding in Your Unstructured Data?
As seen in the previous article in this series, the market is full of products and offerings, each with its pros and cons. We’ve barely scratched the surface. New cloud and technologies for handling unstructured data add even more choices to the list. To guide you in designing a data warehouse fit for your organisation, we’ve created a framework to help you assess your different options.
Arrange the criteria above in order of importance, assigning 1 as the lowest and 7 as the highest. This will be the “weight” of each criterion.
Rank your component bundle option per category, multiply by the weight. Add the weighted scores to reveal a ranking.
This can be visually represented in this radar chart.
Radar chart of Data Warehouse Designs.
Almost every other article on the topic of choosing the “right” data warehouse will rush to sell you certain technologies’ pros and cons. This is where this series differs. We want you to take a few step backs to fully appreciate the big picture. Now that you have a handle on how to build your data warehouse, we want to take the time to hash out why you should build a data warehouse. This step is often neglected and brushed off as less of a priority to beat a deadline. Like many things in life, the why is more often more important than the how. Read the third and last instalment of this series to help you build your data warehouse like a boss.
The post How to Build Your First Data Warehouse Like a Boss Part 2 appeared first on MyDataforce.
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