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 typically flows into data warehouses from multiple sources, and typically on a regular and consistent cadence. Once the data is collected in the warehouse, it is sorted based on pre-determined schemas that your data team sets.
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. Data lakes typically are optimized to store massive amounts of data from multiple sources, allowing your data to be unstructured, semi-structured, or structured.
2) Processing
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 way once imported. This may mean more work on the front end writing SQL code and determining the objectives of your data warehouse, but will allow for a more digestible view of your data 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. Schema-on-read is what allows your data to be stored in unstructured, semi-structured, or structured formats within your data lake.
3) Flexibility
The benefit of schema-on-read is allowing the schema to be created on a case-by-case basis to benefit the data set. 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. The benefit of flexibility in a data warehouses is the ability to immediately see the impact of your data within the warehouse after import – you’ve already done the front end work of determining the schema and your data will be immediately accessible and readable for you.
4) Users
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. The nature of a data lake is such that data can regularly be added in its original format and the end outcome of the data can be determined down the road, at any point in the data’s lifecycle.
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. When setting up your data warehouse, your data team will likely need context of what your data needs to do in order to correctly write the SQL code that will make your warehouse successful.
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.