Data Lake vs. Data Warehouse: Top 9 Key Differences

Considering these two very important tools for data intelligence namely data lake and data warehouse let’s have a brief introduction about these two technologies.

Many people are confused about the terms of Data Lake and Data Warehouse. In many cases, readers interchange them while talking about them. These two tools provide data storage solutions for both raw and processed data. While proceeding further with the blog we will learn about both of the terms and will witness a few of the most prominent pointers of difference on ‘Data Lake vs Data Warehouse’.

Before breaking the ice to catch the fish from the icy water, let’s quickly have a look at the topics that will be covered in the blog.

Topic to be covered

What is meant by Data Lake?
What is meant by Data Warehouse?
Key Differences Between a Data Lake and a Data Warehouse
Conclusion

What is Data Lake?

While talking about the very first part of the blog, Data Lake is a type of data repository that is used to store data in a central storage place. James Dixon, the CTO of Pentaho, is the author of the term. He also suggested that data in a data lake is ad hoc in nature.

Let’s move forward and understand the concept of a data lake, from the below-mentioned pointers:

AWS Data Lakes can store all forms of data namely structured data, semi-structured data, and unstructured data which makes data lakes flexible.
When data is stored in data lakes, the whole process takes place using different identifiers and metadata tags which makes the data retrieval process faster.
Data lakes in a real sense work as a lake of data, which means it provides a massive storage space, where huge volumes of data can be stored easily.
Data stored in data lakes are raw by nature, which can be used to process in such way that it produces unseen and unexpected insights, which can be useful.
This stored data in data lakes can be used to create data pipelines which, through the use of data analytics tools, can be processed for the collection of insights for organizations.

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What is Data Warehouse?

Similar to the Data Lake, the data warehouse is a form of a data repository where the highly structured data is maintained, and it is very much preferable for business organizations.

Let’s learn more about Data Warehouse, read the below-mentioned points to understand Data Warehouse better:

Data Warehouse is meant for business organizations, where highly sophisticated data is dealt with.
Data Warehouse helps organizations make strategic decisions to enhance the organization’s yield.
Important functions performed by the Data Warehouse:
- Data Extraction
- Data Cleaning
- Data Transformation
- Data Loading
- Data Refreshing
The actual data is processed so that it can be separated into several portions and stored on shelves known as data marts for a data warehouse to function.
Data retrieval is straightforward as compared to other similar data repositories, but a data warehouse is not unstructured or semi-structured data.

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Key Differences Between a Data Lake and a Data Warehouse

Below mentioned are the main differentiating pointers on which you can find the difference between a data lake and a data warehouse. Let’s have a look at them together, I bet after reading the below-mentioned points you won’t be needing any further read about them.

Pointer	Data Lake	Data Warehouse
Storage	All types of data are kept in the data lake, regardless of their origin or form. The data is still in its unprocessed form. Data is only altered when it is necessary.	A data warehouse will house data from transactional systems or data composed of quantifiable measures and their attributes.The information is cleansed and altered.
Data Capturing	The data lake captures all forms of semi-structured and unstructured data, besides the types of data and saves it in its native format right from the source systems.	Captures structured data and organizes it according to predefined schemas for data warehouse use.
Storage Cost	Big data technologies in comparison to the data warehouse, are relatively cheaper for data storage in the data lake.	Data warehouse storage is more costly and more time-consuming.
Data Timeline	All data may be stored in data lakes. This also includes the data already used and the data that will be used in the near future.Data is also preserved permanently so that it may be used for analysis in the future.	Analyzing numerous data sources takes up a substantial amount of time during the building of the data warehouse.
Processing Time	Users of data lakes are available with the data not yet converted, cleaned, or organized. Therefore, in comparison to the conventional data warehouse, it gives its customers their findings in much less time.	The pre-defined questions about the type of data were given from data warehouses. So any alteration in the data warehouse required more time.
Schema	The schema is usually created after the data is stored. This gives excellent agility and easy data access, but then has to be completed with work.	Usually, the schema is defined before data is saved. It requires work at the start of the process but gives performance, security, and integration.
Task	All forms of data exist in data lakes, giving consumers access to raw, unprocessed data before they are sorted and organized.	Data warehouses give answers to pre-determined inquiries regarding pre-determined data forms. Therefore, any data warehouse modifications required extra time.
Analyzing tools	Big data analytics, data visualization, data mining, and predictive analytics.	Data visualization, BI, data analytics.
Key Benefits	These customers are unlikely to use data warehouses since they may need to go beyond their capabilities, thus they combine diverse sorts of data to develop new queries.	Most of the users in an organization are operational. The type of users care about only reports and key performance metrics.

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Conclusion

We discussed over the main differences between a data lake and a data warehouse. There is a difference in making a data lake accessible for all kinds of data whereas a data warehouse can be accessed only for very well-structured data.Both data lakes and data warehouses are types of data repositories that are used to store data in a very sophisticated manner making it easily accessible from any part of the world connected to the internet. Both of them have different usage as a data lake covers all types of sectors whereas a data warehouse is preferably used by business organizations for better yields. I hope now you can differentiate between the two commonly used terminologies and learnt something new.