There are many approaches to data architecture you can invest in, including hybrid solutions. Here, we’ll go over the most popular ones to find the one that fits your business needs best.

Data warehouse: discovering trends

Data warehouse is the oldest solution to data architecture that is commonly used in any business that needs to analyze data to make informed decisions, such as retail, banking, or health care.

Let’s consider the following scenario – you’d like to up your cross-selling game on your e-commerce site by recommending similar products right before checkout. And while it’s easy to predict that batteries go along with a remote, it might be a hit or miss in case of other products. And if your inventory is truly huge, the task might be close to impossible.

Here’s where the data warehouse comes in; by combining data pulled from different sources, your business analyst will be able to provide patterns and trends of your current and past customers, allowing you to quickly learn which products tend to be bought together.

Your business analyst could do that because the data warehouse mainly integrates structured data. This type of data is strictly formatted in a table, making it easy to retrieve, update, delete, and analyze – for example, you can remove batches of incorrect data, create rich reports for your marketing and sales teams, as well as use data science to better understand the collected information and shed a new light on customer behavior.

Think of a data warehouse as a personal library: all the books are carefully selected and displayed in the order of the owner’s choosing. This makes it easy to count, list, remove, and add the books, as well as get a glimpse into the owner’s psyche.

Data lake: powerful, yet chaotic

When businesses started collecting more and more data at a quick rate, data warehouses weren’t ready just yet to meet the requirements of the new use cases, in particular those surrounding big data. Moreover, many businesses were looking for ways to optimize costs, and so were on the lookout for cost-efficient solutions.

Data lake was supposed to be such a new solution: cheaper, easily scalable, low-maintenance, and accepting all types of data. You no longer needed structured files – you could welcome semi-structured and unstructured files as well.

The one company that makes the best use of data lakes is Netflix. In the pursuit of studying the consumer preferences, the streaming giant has created its own artwork personalization algorithm to decide which version of the title’s poster should be shown to each user. The sheer amount of data needed to pull this off couldn’t have been possibly handled by a data warehouse.

Think of data lake as an old antique bookshop: all the books are placed randomly, with no regard to genre, author, or edition. If you want to find a specific book, you need to ask the owner or depend on the vague labels to make your way through the collection.

Data lakehouse: modern hybrid

But while data lakes can be relied on in many cases, they also come with their own challenges.

For example, a data lake can be difficult to govern and requires upholding discipline across all teams that access the data through different tools. With no proper data management, you can end up with a fragmented data stack that has incomplete semantic context and many inconsistencies that only grow with time.

Inadequate date governing policies can easily turn your date lake into a data swamp, where information becomes not only unreliable — because it’s filled with old and inaccurate data — but also difficult to access.

Another result of bad governance is inefficient security. When you can’t easily find, update, or delete data, meeting the requirements of new laws, regulations, or standards become an issue. Moreover, additional work required can ramp up your costs.

Clearly, there was a need for a solution that would combine data warehouse’s organizational skills with data lake’s openness to all types of data – and that’s how data lakehouses were born.

This architecture is cost-effective, flexible, and scalable, all while making real-time reporting and machine learning possible, allowing you to manipulate all the data as much as you want.

But while data lakehouses seem perfect on paper, they might be an overkill for many small to medium businesses.

Think of a data lakehouse as a central library: not only can you find any book you might need, but it’s also easy to locate thanks to the intuitive management system.

When to use each data architecture?

Frankly, there are no straightforward answers. Nowadays, all data architecture approaches have undergone significant changes to keep up with the needs of modern businesses, making all of them plausible choices.

What’s more important is to make sure that the chosen solution fits our use case. Otherwise, we’d overpay for unnecessary services, which only shows how important it is to find a reliable partner who can steer us in the right direction.

Still, we can describe the most common use cases where each data architecture approach usually fits best:

• Data warehouse: your amount of data isn’t staggering, and it won’t grow rapidly; you operate on structured data and want to make better informed decisions based on it,
• Data lake: you have a huge amount of data you need to store in all shapes and forms, and would like to use machine learning to analyze it and detect patterns,
• Data lakehouse: you have a huge amount of data you need to store which you want to manipulate however you want, from creating reports to machine learning.

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