Databases vs data lakes: Which should you be using?

Databases vs data lakes: Which should you be using?

As the transformational power of data is realised, the debate around whether to choose databases or data lakes has intensified

Businesses large and small, along with data scientists, IT professionals and analysts, have been talking about the differences between databases and data lakes with increasingly vocal interest. But what’s the difference? And what are the real-world applications for each approach?

Here’s what you need to know about this dilemma to ensure your company or organisation is positioned for growth and a smoother transition into tomorrow’s even more technologically interconnected world.

The term “data lake” is sometimes used interchangeably with “data warehouse” — but this is not correct. The truth is, although they serve similar functions, there are important distinctions — and if you deploy them strategically, they can complement each other today and into the future.

A data warehouse stores data from a variety of “known sources” from across a company or organisation. This data is referenced by employees and decision-makers and exchanged regularly — between colleagues, the company and a third-party logistics and analytics provider, or between senior management when decisions need to be made.

This type of data storage, to be reductive, is “for human beings.” More specifically: its purpose is to inform management and strategy decisions in the day-to-day and a short while into the near-future.

In comparison, a data lake is more of an unstructured collection of data in its “original format.” In other words, it’s not being stored for immediate use, but rather for its analytical potential. Its “value” isn’t known until the data is called upon and used to gather some kind of insight. This type of data storage is “for machines.” It fuels machine learning and automation.

A database, by design, is highly structured. You can think of it as a “bank” of information from known sources and stored in known formats and file types. The compatibility of this information with other programs, partners, and clients might involve restructuring or converting the data to another format.

This makes databases inherently less “agile” than data lakes. On average, storage costs can be higher than with data lakes because uptime is usually of paramount importance.

Databases have more obvious applications in business than data lakes, currently, although the two are far from mutually exclusive. We’ll speak more about how to choose, depending on your intentions, in a moment.

To reiterate, data lakes store accumulated data in all of their raw, unstructured formats. What this means is that, unlike a database, which relies on structural markers like filetypes, a data lake provides data that can move between processes and is readable by a variety of programs. Storage costs for this type of data management setup tend to be lower than with databases.

Data lakes are a better fit for the data science and IT fields. But why? Let’s look at some questions every organisation will need to ask itself if it wants to know whether a database or a data lake is the appropriate choice.

There is one primary drawback to data lakes versus databases and data warehouses — the technology is still new. The security of data in such a “fluid” environment — with so many potential types of users, and privacy regulations concerning data use — are difficult to ignore. It’s a maturing technology, but it has a lot to offer.

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