Which Is the Better Data Architecture, a Data Lake or a Data Warehouse?

Data architecture is a significant decision for a business undergoing a digital transformation. Choosing the appropriate model is a critical first step in any such initiative. However, given the breadth of available options and the perplexing terminology, selecting a solution that meets the business's needs while remaining within its budget is no easy task.

The terms "data warehouses" and "data lakes" are frequently used to refer to two of the most popular options. Consider a data warehouse in the same way that you would a shopping mall. It contains discrete "shops" that store structured data — bits that have been pre-sorted into formats compatible with database software.

In comparison, a data lake resembles an unkempt flea market. It has "stalls," but it's not entirely clear where one ends and the next begins. Data lakes, in contrast to data warehouses, can contain both structured and unstructured data. As the name implies, unstructured data refers to "messy" digital information such as audio, images, and video.

The "data marketplace" complicates matters further. In contrast to the first two concepts, this is not architecture but rather an interface to a data lake that enables access to its contents by those outside the IT team, such as business analysts. It enables users to fish what they require from the lake via a search function. Consider data marketplaces to be personal tour guides at flea markets, directing shoppers to the best bargains.

Inside the Data Warehouse and Data Lake

A data warehouse is an excellent option for a business that wishes to analyse large but structured data sets. Indeed, if a business is only interested in descriptive analytics — the process of summarising existing data — a data warehouse may suffice.

Consider the following scenario: company leaders want to examine sales figures over a specified period, the number of product inquiries, or the number of views on various marketing videos. A data warehouse is ideal for these applications, as all associated figures are stored as structured data.

However, structured data is only a portion of the story for the majority of businesses embarking on big data initiatives. Businesses generate an enormous amount of unstructured data each year. Indeed, according to 451 Research and Western Digital, 63% of enterprises and service providers retain at least 25 petabytes of unstructured data. For those businesses, data lakes are appealing options due to their capacity to store massive amounts of such data.

To top it all off, data lakes give analysts the freedom to move beyond descriptive analytics and into the more exciting and lucrative world of predictive or prescriptive analytics. For businesses, predictive analysis is the process of analysing current data to predict future trends, such as revenue growth for the following year.

To go a step further, predictive analytics uses artificial intelligence to provide recommendations based on predictions. A data lake is essential for both predictive and prescriptive analytics. Analytics software like Apache Hadoop is frequently used by leaders to manage data lakes.

Consider who will conduct data analyses and what type of data they will require before investing in either a data lake or a data warehouse. While data warehouses are frequently only accessible to IT teams, data lakes can be configured to allow access to analysts and business personnel throughout the organisation.

For instance, a healthcare organisation with which my company recently worked requested a data warehouse solution. Eventually, however, it became clear that the firm would require a data lake. It was not only interested in predictive modelling, but also in incorporating unstructured data, such as handwritten doctor's notes.

Analysts at a healthcare organisation may extract treatment data from a data lake to forecast patient outcomes. They may then add a prescriptive layer to recommend the optimal course of treatment for each patient's needs — one that minimises cost and risk while maintaining the highest possible quality of care.

Utilizing the Data Lake to Its Full Potential

Given their capacity to store both types of data and their suitability for future analytics requirements, data lakes may appear to be the obvious solution. However, due to their amorphous structure, they are frequently referred to as data "swamps" rather than lakes.

Indeed, Adam Wray, CEO and president of NoSQL database Basho, characterised them as "evil due to their unruliness" and "extremely expensive." According to Basho, "the value extracted [from data lakes] is infinitesimal in comparison to the value promised."

But one shouldn’t count data lakes out just yet. Data marketplaces can rescue the promise of data lakes by organizing them for the end-user. Just as the internet was much more difficult to navigate before Google, data marketplaces unlock the powerful data lake architecture. In the analytics world, there’s no one-size-fits-all system. Data warehouses can give even smaller companies a taste of data analytics, while data lakes (when combined with data marketplaces) can enable enterprises to dive headfirst into big data. These systems aren’t mutually exclusive, either. If its analytics needs change, a company that chooses a warehouse can later add a lake and a marketplace.

What's critical is that you begin the journey toward a more data-driven business. Many executives will recall that data was not even discussed outside of IT teams a decade ago. Now, with a plethora of analytics requirements and tools available, it is up to executives to drive the conversation.