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.
What’s the difference between databases and data lakes?
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.
The data lake continues to grow deeper and wider in the cloud era
In the modern world of data lakes, CDOs and CIOs will face three major challenges: how to migrate their users, how to live with a hybrid infrastructure for a while and how to future-proof their data platform. Read here
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.
What to know about databases
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.
How to choose the best enterprise storage solution for your business
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.
What to know about data lakes
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.
How to choose one over the other
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.
That’s the one caveat out of the way. So how do you choose? Companies just beginning their journey into big data and operational analytics — or research firms, or IT architects, or any number of other types of teams and organisations — can use the following three questions to see if the time is right to consider building a data lake.
1. Is your operation complex enough, or are you anticipating growing quickly?
Operations that are large and complex — or that anticipate significant growth — need a flexible, scalable data storage solution. That probably means a data lake.
Nothing’s future-proof, but data lakes, thanks to their unstructured natures, offer technology for scientists and business analysts who prefer to take the long view.
2. Do you really need real-time analytics?
A data lake is a system that gathers data from many very different sources, including connected production equipment, delivery vehicles, customer feedback, sales data, forecasting algorithms and even social media feeds.
With the right analytics software, it delivers considerable value in the form of real-time analytics of operations data and even more accurate forecasting.
Maximum value: Why and how should you make data analytics more accessible?
This is a resource-intensive business asset, which means it comes with ongoing costs attached, including energy use. Overtaxing your resources exposes you to the risk of power failures and data loss, among other threats to your bottom line. Don’t expand your efforts on building this kind of infrastructure unless you’re sure it’s something you need — and your other operational necessities are already taken care of.
3. Do you have multiple divisions, all fueled by data?
What do we mean by multiple divisions? Your company, firm, or research team might employ several or even all of the following divisions under your roof:
- Data scientists.
- Market analysts.
- Marketers and salespeople.
- Business strategists.
- Acquisition experts.
- Financial planners.
The marketing implications of big data — and, later on, data lakes — introduced the world to what’s possible with analytics. But tomorrow’s companies are going to need data accessibility and technology savviness in each of the business areas named above, as well as others.
Big data (and AI) in the enterprise
Data lakes are more agile and accessible to a broader variety of users and technology platforms. But they also inherently encourage operations to store everything and sort its usefulness later.
There’s no “prioritisation” — only “gathering.” Data is not necessarily being gathered with a specific “mission” in mind.
There is noticeable latency in data lakes vs. databases for this reason. Latency, along with other risk factors, is one way data can become lost or corrupted.
Consequently, business analytics systems can use data lakes to perform automated reporting and serve analytical insights to digital dashboards. But for day-to-day functions that require access to more structured data assets, reports and other types of files and resources, a business might have a database or data warehouse in addition to a data lake.
The former is for everyday business decisions and back-end functionality, while the latter is for higher-order analytical processing, data science and the automation of some business functions.
Both of these technologies are helping lower the barrier of entry for mid-sized and smaller businesses — not raising it.
And as they receive even more widespread adoption in the worlds of commerce and data science, it will probably become more attractive to invest in both types of data storage and analysis systems.
They provide similar, but importantly different, functionality — and their strengths and weaknesses complement each other well.