Determining whether a data lake or a data warehouse is the best fit for your organization’s data is likely one of the first in a long line of data-driven decisions you’ll make in your data governance journey. We’ve outlined four key differences between data lakes and data warehouses and explained factors that may impact your decision.
By definition, a data lake is a place where data can be stored in a raw and unstructured format. This data is accessible whenever and by whomever- by data scientists or line of business execs. On the other hand, a data warehouse stores structured data that has been organized and processed and allows the user to view data in digestible formats based on predefined data goals. Due to their nature, there are a few key differentiators between these two data storage options.
1) Data Format
First, the format in which data can be viewed after import varies between data lakes and data warehouses.
A data warehouse requires data to be processed and formatted upon import, which requires more work on the front end, but allows for more organized and digestible data to be viewed at any point in the data’s lifecycle after defining the schema.
Data lakes allows you to store data in its native or raw format the entire time the data is housed within the lake. This allows for a quick and scalable import process and allows for your organization to store a lot of data in one place and access the raw form at any point.
The way in which data is processed is a critical differentiator between a data lake and a data warehouse.
Data warehouses use a process called schema-on-write and data lakes use a process called schema-on-read. A schema within data governance is a collection of objects within the database, such as, tables, views, and indexes.
Schema-on-write, what is used in data warehouses, allows the data scientist to develop the schema when writing, or importing, the data, so that the database objects, including tables and indexes can be viewed in a concise and digestible way once imported.
On the other hand, schema-on-read allows execs to forego developing the schema when importing the data into the data lake but will require you to develop the schema when accessing the data later down the road.
The benefit of schema-on-read is allowing the schema to be created on a case-by-case basis to benefit the dataset. Many who opt to store their data in a data lake prefer the flexibility that schema-on-read allows for each unique data set.
Alternatively, schema-on-write interprets all imported data equally and does not allow for variance once imported.
Finally, accessibility and user control may be the deciding factor for how and where your company stores data.
A data lake is more accessible by day-to-day business execs and makes it easy to add new raw data to your lake. A data lake is traditionally less expensive due to the nature of the format, and because you likely won’t need additional manpower to import and maintain your data within the lake.
A data warehouse likely will only be accessible and able to be updated by data engineers within your organization. It is more complicated to update and may be more costly because of the manpower required to produce changes.
It's important to note that you can have a data warehouse without a data lake, but a data lake is not a direct replacement for a data warehouse and is often used to complement a data warehouse. Many companies who use a data lake will also have a data warehouse.
Regardless of where you store your data, you’ll need to set up access rules to govern and protect it. Implementing a cloud data security solution has never been easier. ALTR’s data governance solution for Snowflake is not only free but it’s also designed to help you easily share data securely to get value as quickly as possible. Get started today!