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Data Lake and Data Warehouse: What's the Difference?
Data Lake and Data Warehouse: What's the Difference?

October 17, 2024

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Data lakes and data warehouses are excellent options for storing big data. However, choosing the right one depends on understanding their differences and how they fit your business needs. Let's break down each to help you make an informed decision.

Data Lakes

Data lakes are scalable storage repositories that hold large amounts of raw data in the natural format until required. Data lakes store data from various sources in all formats, i.e. structured, unstructured, and semi-structured. Think of a data lake as an actual lake where data flows in from different streams and stays there until it is required.

Data Warehouses

On the other hand, data warehouses combine different technologies to organize and use data strategically. Before storing, the data is cleaned and transformed, making it ready for analysis and reporting.

Data Lakes vs Data Warehouses

Data Retention:

Data lakes retain all data types regardless of source, usage, or format. However, in data warehouses, considerable time is spent understanding business processes and analyzing and structuring data before storing it.

Data Type Support:

Data lakes support all data types (traditional and non-traditional) and store them in raw form until processing. This approach is known as 'schema on read.' Data warehouses use a 'schema on write' approach to store cleaned data extracted from transactional systems and structure it with quantifiable metrics and attributes. 

User Support:

Data lakes are ideal for data scientists who need advanced analytics tools for data analysis. On the other hand, data warehouses are more suitable for operational users who need easy-to-use, well-structured data.

Storage Cost:

Data lakes are generally cheaper than data warehouses due to the lower efforts and time required for processing data.

Data Processing:

Data lakes use the Extract Load Transform (ELT) process, while data warehouses use the Extract Transform Load (ETL) process.

Wrapping up!

We suggest analyzing both data storage approaches before making any decision for your business.  Depending on specific requirements, organizations can choose between a data lake and a data warehouse. Often, the best approach is to use both, leveraging the strengths of each. For instance, an organization with a fully-fledged data warehouse can adopt and implement a data lake with its existing data warehouse to reap the advantages of both approaches. 

 

The article was first published on CSM Blog Named: Data Lake and Data Warehouse: What's the Difference?


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