Data Lake vs Data Warehouse: Difference Between Data Lake & Data Warehouse

Ever since Big Data came to the limelight, data lakes and data warehouses jumped into the scene. While both are data lakes and data warehouses are storehouses for Big Data, they are not the same. The only similarity between a data lake and a data warehouse is that they are used to store data. To understand these storage repositories’ unique purposes, it is essential to identify the difference between data lake and data warehouse. 

Data Lake vs. Data Warehouse

Data warehouse

A data warehouse is a storage repository for large volumes of data collected from multiple sources. Before data is fed into a data warehouse, you must clearly define its use case. It usually contains both historical and present data in a structured format. The data stored in a data warehouse is used by businesses to create annual and quarterly reports to measure business performance. 

Data lake

A data lake is a pool of raw data (data in its natural state) that flows like streams from data sources into the lake. Data lakes accept all data types, irrespective of whether or not it is structured or unstructured. First, the data is stored at the leaf level in an untransformed state, after which it is transformed, and schema is applied to fulfill the needs of analysis. Users can access the lake to dive in and take data samples to fuel business innovation.

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Difference Between Data Lake and Data Warehouse

When it comes to data lake vs data warehouse, here are some of the basic differences we need to consider.

Data structure

One of the biggest differences between data lake and data warehouse is the way they store data. While data lakes store raw and unprocessed data, data warehouses store organized and processed data. This is primarily the reason why data lakes require a larger storage capacity. By storing processed and structured data, data warehouses save valuable storage space and cut down costs.

The most significant benefit of data warehouses is that since they store processed data having a defined use case, businesses can readily use it for their organizational needs. Raw data also has a clear advantage – unprocessed data is highly flexible, making it ideal for ML tasks. However, since data lakes have no strict data quality and data governance measures, they can fast turn into data swamps. 

Purpose

A data lake is characterized by minimal organization and filtration. Data can flow into a data lake from any source. Generally, individual data elements in a data lake don’t have a defined or fixed purpose. On the other hand, data warehouses store processed data that will be used for specific business purposes. Thus, data warehouses never store data that has no use within an organization. 

Accessibility

The ease of accessing data from a data repository depends on the storage structure as a whole. Since data lakes have no set structure or strict limitations, you can easily access and modify the data as and when required. Contrary to this, the architecture of a data warehouse is more structured. This is beneficial since processed data is easy to interpret and understand.

User base

Raw and unstructured data is pretty tricky to manage, analyze, and interpret. Data scientists and data analysts typically deal with raw data to extract meaningful patterns from it and transform them into actionable business strategies. Thus, data lakes require much more skilled and expert users who know the nitty-gritty of dealing with raw data.

On the other hand, you can easily visualize processed data in the form of charts, tables, graphs, spreadsheets, etc. This is why data warehouses have a more extensive user base – anyone having the basic knowledge of business data can work with data warehouses. 

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Adaptability

Perhaps the biggest issue of data warehouses is that they are not flexible or adaptable. It takes a significant amount of time, resources, and effort to modify a data warehouse’s structure, mainly because the data loading process is complicated. However, as the data always remains in its raw form in a data lake, anyone can access it anytime. You can explore and experiment with the raw data in any way you desire, without any restrictions. 

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What is Data Lake vs Data Warehouse in Different Industries?

Data Lake

The use of a data lake can be seen in the following sectors:

Marketing 

A data lake allows marketing experts to acquire data about the likes and dislikes of their ideal client demography across various sources. Data lakes allow marketers to analyze data, make informed decisions, and develop data-driven initiatives.

Education

The educational sector has started to use data lakes to manage information regarding scores, attendance, and additional performance objectives so that colleges and institutions can better their financing and policies. 

Aviation 

The data lake is utilized by data scientists working for shipping and aviation companies to enhance the effectiveness of lean supply chain management by reducing costs and boosting efficiency.

Data Warehouse

The industries that extensively use data warehouses are: 

Finance

Financial institutions often use data warehouses to give all employees access to their data. A data warehouse may create accurate, safe reports, saving businesses time and money.

Enterprises

Large businesses employ high-performance enterprise data warehouse systems to manage activities by centralizing marketing, advertising, stocks, and other supply chain information.

Tools Used in Data Lake and Data Warehouse 

Some of the well-known tools used for data lake and data warehouse are as follows:

• Data Lake Tools: Qubole, AWS Lake Formation, Infor Data Lake, Azure Data Lake Storage and Intelligent Data Lake.
• Data Warehouse Tools: Snowflake, Microsoft Azure, Amazon Redshift, Amazon DynamoDB, Micro Focus Vertica and Google BigQuery. 

Conclusion

Data lakes and data warehouses serve different purposes altogether. A data lake’s primary goal is to gather Big Data from disparate sources, whereas data warehouses are best for data analytics. While a data lake may work best for one organization, a data warehouse might be the best fit for another company, whereas some companies may require both.

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