June 10, 2021

If you work in business intelligence, then you’re probably familiar with the ongoing data lake vs data warehouse debate. You may even have your own strong opinion! What you may not know, however, is that one data platform really isn’t necessarily better than the other. Both storage methods have their own uses, and which method is right for you mostly depends on your business and needs.

Specifically, which data platform you’ll benefit from more ultimately comes down to what you need to use your data for. Both lakes and warehouses collect, store, and surface data in different ways. To understand which platform is right for you, you’ll have to figure out what kind of data you need, what you need it for, and how you want to look at it. Luckily, by learning more about each of these platforms, you’ll be able to figure out quite a bit about what you need a lake or warehouse for in the process. Here’s everything you should know about the pros and cons of both platforms to help you understand which is right for you.

What is a data warehouse?

data warehouses: advantages and disadvantages

A traditional data warehouse stores large amounts of data from across a company’s functions. They periodically pull information from transactional systems, line of business applications and more.

After pulling data, these warehouses transform it into a standardized schema that matches info already stored in their database. This data model is called “schema on write,” because the platform “writes” the schema before implementing it. After implementation, the platform categorizes information contained inside it according to different pre-defined files and folders.

“Schema on write” and pre-organization both help make data as easy for analytics tools to use as possible. Users determine how the warehouse formats, organizes and pulls it.

Data Warehouse Advantages

Cloud data warehouses define everything they manage in advance in a process called “database optimization.” This makes management very simple. You can store all data required for reporting under a single category, even if you need to combine it from multiple sources.

The platform defines, cleans, standardizes and structures data according to what you need it for. For instance, if you’re reporting, the warehouse can structure your numbers in a specific way to make them especially useful for reporting.

Data warehousing is the ideal way to produce an updated “single source of truth” for specific analysis tasks. After setting up a data warehouse to pull financial reporting information (for example), the platform will do so whenever you need it. Warehouses save data engineers tons of time by allowing them to access the specific types of information they need.

Data Warehouse Disadvantages

Data warehouses are great at organizing data to answer specific “questions,” but they aren’t as useful for accessing data OUTSIDE of those questions. If the info you’re looking for doesn’t fit within the warehouse’s schema, then it may be excluded.

Alternatively, your warehouse may contain the data you’re looking for, but it may be transformed into a context that doesn’t suit what you need. Meanwhile, any unstructured information is completely excluded. Before the warehouse can pull data sets, it needs to know how it’s formatting the information.

Consequently, warehouses can be overly rigid and difficult to use outside of their pre-defined use cases. Companies that constantly seek out new ways to utilize their existing information may spend too much time repeatedly reworking their warehouse instead of spending on actual analysis and value-adding activities.

What is a data lake?

Like a data warehouse, a data lake is also a single, central repository for collecting large amounts of data. The major difference is data lakes store raw data, including structured, semi structured and unstructured varieties, all without reformatting.

Warehouses use “schema on write” when information is added, while lakes use “schema on read.” In schema on read, information is only formatted when it’s “read,” or queried in real time. Lakes tend to be most useful for professionals such as data scientists or analysts with experience organizing and evaluating data according to custom and business-specific needs.

data lake advantages and disadvantages

Data Lake Advantages

Data lakes allow users to store massive amounts of data in its native format without organizing or defining it beforehand. This allows greater flexibility for analyzing things like syndicated, POS, and Big Data, where structural consistencies from different sources become problematic for a warehouse. With a lake, users can access all information much more easily and in real time.

Keeping information in its original format is a big advantage for several reasons. First, your team doesn’t need to specify what you’ll be using it for. Instead, they can begin uploading as soon as the lake is ready. They’ll also be able to upload any information directly from any source system.

Lakes support many users and use cases more easily than warehouses. With the proper tools or support, users can answer more questions and analyze more information. Lakes are particularly useful for professional business analysts diving deep into a company’s many data sources. Analysts can use lakes to gain big picture insights, understand intricate causalities driven by external factors, and more.

Data Lake Disadvantages

Data lakes store data in its native format. Different sources may come into the lake in non-standard formats and need to be reformatted manually. The lake also can’t curate and arrange data for a specific purpose the way warehouses can.

Ultimately, you’ll probably need either data scientists and/or high-quality tools, such as EBM Catalyst, to make the most of a Data Lake. With the right set up, Lakes are a tremendously useful way to quickly query and structure it for useful analysis. Without these things, however, it’s possible your team will spend more time simply trying to organize and make sense of their information than you would prefer. Think of it this way: Lakes simply collect native data in a single, central repository. How it comes out of that repository is up to you and your ability to organize and analyze it… or your ability to find the right tool to help you do those things.

data lake vs data warehouse: which to use and why

Data Lake vs Data Warehouse: which should I use and when?

Clearly, these data platform models aren’t necessarily “better” or “worse” than each other. Instead, each is more effective at different functions and for different experts. Warehouses are ideal for organizing data required for pre-defined purposes such as reporting, which makes them great for traditional finance and data storing business functions. Meanwhile, lakes are better for collecting large quantities of data for insights and strategic questions, which makes them more effective for customized data analysis and the kind of value building business optimization practices CFOs pursue.

Data lakes are a younger technology than warehouses, and new technologies improve them all the time. One of these technologies is EBM Catalyst. Catalyst greatly simplifies the processes required to derive insight from the data lake. With the help of the EBM Catalyst tools, you can pull and interpret your Lake’s data with the efficiency and confidence of an expert – no matter your background.

The Best of Both: EBM Catalyst

Catalyst FP&A Cloudprovides a “best of both worlds” solution to the endless data lake vs data warehouse debate. The tool allows users to build structured warehouses inside their unstructured lake. This allows users to benefit from the organizational capabilities of warehouses without losing the flexibility, formatting options, and breadth of data a Lake allows them to access. Meanwhile, Catalyst’s Query Tool, which we jokingly refer to as “SQL for Dummies,” lets users query, structure and marry data from different sources within the warehouses and lakes for analysis. Query makes it easy and intuitive to quickly locate and analyze the data you want, regardless of where it’s housed within your lake.

For example: Want to marry weather and sales data to see how many more umbrellas you sell when it rains? Easy. How about stitching together your POS data with shipment and inventory data? Catalyst can do it in a few mouse clicks.

Want to dive even deeper and examine your data from multiple angles? Catalyst has a full array of reports, OLAP and Tabular cubes, dashboards and visualization tools (with seamless Power BI™ integration) to help. Whether the data is structured or unstructured, Catalyst lets you transform it into game-changing insights faster.

Data lakes are not necessarily more useful than warehouses, and warehouses are not necessarily more organized than lakes. Both simply handle different needs well, and both continue to have a place in business and data storage. Either way, EBM is finding ways to help our clients see the best of both worlds.

With Catalyst, we can make your data work for you. Whether you’re interested in Data Warehouses or Data Lakes, EBM has the right solution for your business.