Understanding the Difference Between Data Lakes and Data Warehouses

Most often people tend to make mistakes while understanding terms like “data lakes” and “data warehouses.”

Let’s make these terms simpler for you.

Both data lakes and data warehouses help store massive chunks of data – simply said they’re used as a storehouse for data.

However, both terms are quite different from each other. Not to mention, they are not interchangeable terms.

In this article, we will walk through the definition and explain the differences between both the terms in the simplest language for you to understand.

Data Lake

A data lake is specifically used to store data of any form i.e. structured or unstructured. It also allows us to hold a large amount of raw data in its native format until it is required. The term is associated mostly with Hadoop-oriented object storage. In such a scenario, the data of the organization is first loaded to the Hadoop platform and then the business analytics. Further on, data mining tools are added to this data where it generally stays in the Hadoop’s cluster nodes of the commodity computers.

Data Warehouses

Whereas data warehouses gather data from multiple sources (internal or external), to which the data is further optimized for business purposes. In this form, the data is mostly structured and from a relational database. However, unstructured data can be gathered too, but mostly it is the structured data that gets collected.

Data Lakes Versus Data Warehouses: The Key Differences

Both use two different strategies for storing data.

One of the major differences between the both is that in data lakes there’s no particular predetermined schema. It can easily house a structured or unstructured data. Wherein this is not the case with the data warehouse. The concept of data lake started to rise only in the 2000s showcasing how data can be stored and how can you save cost at the same time.

However, a data warehouse generally composes of a determined schema and handles primary data.

Data lakes and data warehouses are efficient enough in handling unstructured data, however, they fail to do so. With the amount of data being generated, it can get expensive to store all the data. Besides this, it is time-consuming and takes rather a long process to analyze and store. One of the many reasons why data lake lakes have risen to the forefront. Wherein it can handle unstructured data most efficiently and cost-effectively.

As a data science professional, you need to know the below differences between the two terms –

History

Technologies like big data used in the data lake is a new concept, however, a concept like data warehouse has been used for decades together.

Storage

In the data lake, data can be stored despite its structure and kept in its raw form until it is needed to be used. But in the data warehouse, the data that is extracted composes of quantitative metrics wherein the data is cleaned and transformed.

Data Timeline

The data lake has the capacity to store all data. The present data and data that is needed to use in the future. And in the data warehouse, there is a specific and significant time that is spent on analyzing multiple sources.

Data Capturing

Gathers all types of data, both structured and unstructured. However, in the data warehouse, it gathers structured data and arranges them in schemas specifically designed for the data warehouse.

Storage Costs

Data stored in big data technologies is cost-efficient as compared to storing in a data warehouse, unlike a data warehouse where it is costlier and the process is time-consuming.

Users

Deep lake is crucial for users involved in deep analysis. Whereas, the data warehouse is perfect for operational users since they are well-structured and easy to use.

Tasks

Data lakes encompass every type of data and boost users to access data before it is processed and cleansed. And data warehouse provides insights into pre-defined questions for a pre-defined data type.

Data Processing

Data lake projects use the process of ELT (Extract, Load, and Transform) but in the data warehouse, they still use the traditional ETL (Extract, Transform, and Load) process.

Core Benefits

In the data lake, they have integrated multiple questions to come up with new questions since these users might not prefer using a data warehouse because they might need to go beyond their capabilities. Whereas, with the data warehouse, most of the users in the company are operational. And their core focus is only on tracking performance and reports.

In Conclusion

Before deciding which preference to go with, you need to first go through the key differences and analyze which one best suits your projects. At times, you may need to use the combination of both the storage solutions.

Which one of these solutions you would prefer today?

Here’s what you need to know. As the unstructured data keeps growing, the rise of the data lake will become popular. Yet, there will still be a need for a data warehouse. So, based on your projects, you might need to choose the best storage solution.

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