Both, data lakes and data warehouses are commonly used for storing big data. However, they are not interchangeable terms.?
Defining both of these, a data lake could be considered a vast pool of raw data that doesn?t have a specific purpose for its existence.
On the other hand, a data warehouse is a repository for all kinds of structured and filtered data that has already been processed for a particular purpose.
The common thing between both these terms is that they are often confused among each other even though they are very different from each other.?
The whole difference between data warehouse and a data lake is pretty important to understand as they serve different purposes and need different resources for optimizing properly.?
What is a Data Warehouse?
Data warehouse could be defined as a blend of different kinds of technologies and components that allows a very strategic use of data.
This technique is considered the most ideal for the sake of collecting and managing data from different kinds of sources in order to provide important business insights that could be useful for different purposes.?
Data Warehousing is basically electronic storage that contains a large amount of information that?s specifically designed for the sake of queries and analysis. In nutshell, it involves a process of transforming data into information.?
What is Data Lake?
Data Lakes are the perfect hotspots to store all kinds of data in its raw form.?
The reason for this is because there?s no limit on the account size or file when it comes to storing data in data lakes. It can easily withhold a massive volume of data to increase the overall analytic performance of an organization.?
It is a modern concept and could technically be referred to as a storage repository that can store huge volumes of data. This data could be structured, semi-structured, or unstructured data.
Data Lake could be compared to a large container that?s able to contain enormous volumes of data that could be converted and used as per the need.?
- The data is stored at the leaf level in a nearly non-transformed state.
- The data is loaded from the source systems and no data is turned away.
- The data is transformed and the schema is applied for the sake of fulfilling the need for the analysis.
- Data Lake helps to store all data without considering the source of the structure. However, data warehousing stores the data in quantitative metrics along with their attributes.
- Data Lake helps in defining the schema after the data is store. However, the data warehouse defines the schema right before data is stored.
- Data Lake could be considered as a storage repository which stores data structures of all kinds while as data warehousing helps blend technologies and component that allows a very strategic use of data.
- Data Lake is considered as the most ideal option for those who are looking for an in-depth analysis. However, the data warehouse is perfect for operational users.
What?s the approach that?s perfect for me?
This might be a tricky question.
In case you already have a well-established data warehouse, there?s no need to convert all that into data lakes and start over.
However, just like a lot of other data warehouses, you might face a few issues along the way. If you face such issues, you may choose to implement such data alongside your warehouse.
In such a case, the warehouse is able to operate just like it does and you can start filling your data lake with new data sources.
The same could also be used for an archive repository for the warehouse data that you can roll off and also keep it available for the sake of providing your users with access to more data than they had before.
With the aging of the warehouse, you can consider moving it to a data lake.
And if you are just starting, you can consider both the approaches.?