As more and more businesses and organizations turn to real-time data replication, storage, analytics, their interests naturally turn to solutions that allow them to keep and process huge volumes of data. Among these are data lakes and data warehouses.
At the outset, one might think that a data lake and a data warehouse are one and the same. However, there are critical differences between these two types of data repositories that can affect how you access and use your data. Do you need an enterprise-class data lake, or should you opt for a data warehouse? Here are some basic things you need to understand to help you choose.
One of the primary differences of data lakes and data warehouses is that the former stores raw data, while the latter stores structured data. This means that data within a data lake may or may not be relevant, depending on who views them, and they are only transformed only when they are ready to be used; meanwhile, all the contents of a data warehouse are pertinent to those who have access.
If the data is not required for a specific purpose, it is often excluded from a data warehouse; a data lake can retain more, if not all, data because the information is stored on lower-cost storage. However, it is up to the user to sort through and make sense of all the data within a data lake.
Data Types and Processing
Most data warehouses ignore unstructured data such as images and text; what they usually store are quantitative metrics and other processed information. On the other hand, data lakes aren’t so picky in the kind of data that they store. Apart from images and text, data lakes can also host non-traditional data types like web server logs and even social media activity.
Because the data is already prepared within a data warehouse, the data processing method is called “schema on write.” Data lakes, on the other hand, uses what is called “schema on read” because you only process the data you need when you need it.
Using a data lake or a data warehouse depends on what you want out of your data. If you already know what you’re looking for – say, a sales performance report – then you need a data warehouse. If you are looking for something more unstructured that you can pound into shape – say, customer habits when using search engines – then a data lake is more suitable.
Data warehouse users tend to need their information quickly to be able to answer questions, while those that use data lakes can spend more time with deep-dive analyses and may even come up with more questions to be answered.
The data stored in data warehouses usually have fixed configurations, due to them being already processed and more complex, and therefore take quite long and consume more resources to change or update. The data housed in data lakes, meanwhile, are more agile and accessible due to it being stored in raw form; they can be reconfigured multiple times as required.
In the end, the more important question to ask is “what do I want to do and what kind of data I need to accomplish it?” Once you have answered this, then the choice between setting up a data warehouse and a data lake becomes that much easier.