Overview
- Understand the meaning of data lake and data warehouse
- We will see what are the key differences between Data Warehouse and Data Lake
- Understand which one is best for the organization.
Introduction
From processing to storage, all aspects of data have become important to an organization just because of the sheer volume of data we produce in this age. When it comes to storing big data, it is possible that you have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for saving big data.
Having been in the data industry for a long time, I can attest to the fact that a data warehouse and a data lake are two different things. Despite this, I see a lot of people who use them interchangeably. As a data engineer, understanding the data lake and data warehouse along with their differences and usage is very important, since only then will you understand if the data lake fits your organization or data warehouse.
Then, in this post, satisfy your curiosity by explaining what data lake and storage are and highlight the difference between them.
Table of Contents
- What is a data lake?
- What is a data warehouse?
- What are the differences between Data Lake and Data Warehouse?
- Data lake or data warehouse: Which one to use?
What is a data lake?
A data lake is a common repository that is capable of storing a large amount of data without maintaining any specific data structure.. You can store data whose purpose may or may not yet be established. Its purposes include creating dashboards, machine learning or real-time analytics.
Now, when you store a large amount of data in one place from multiple sources, it is essential that it is in a usable form. You must have some rules and regulations to maintain the security and accessibility of the data.
Opposite case, only the team that designed the data lake knows how to access a particular type of data. Without the proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. Therefore, it is essential that your data lake does not become a data swamp.
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What is a data warehouse?
A data warehouse is another database that only stores the pre-processed data. Here, the data structure is well established, optimized for SQL queries and ready to use for analytical purposes. Some of the other names of the data warehouse are Business Intelligence Solution and Decision Support System.
What are the differences between Data Lake and Data Warehouse?
| Data lake | Data warehouse | |
| Data storage and quality | The data lake captures all kinds of data as structure, unstructured in their raw form. It contains the data that could be useful in a current use case and also that is likely to be used in the future. | Contains only high-quality data that is already pre-processed and ready to be used by the team. |
| Target | The purpose of the Data Lake is not fixed. Sometimes, institutions have a future use case in mind. Its general uses include data discovery, user profiling and machine learning. | The data warehouse has data that has already been designed for some use case. Its uses include business intelligence, Batch visualizations and reporting. |
| Users | Data scientists use lakes of data to uncover patterns and useful information that can contribute to companies. | Business analysts use data warehouses to create visualizations and reports. |
| Prices | It's comparatively low-cost storage, since we don't pay much attention to storage in the structured format. | Data storage is a bit more expensive and also a time-consuming procedure. |
Data lake or data warehouse: Which one to use?
We have seen what the differences are between a data lake and a data warehouse. Now, we will see which one we should use.
If your organization handles healthcare or social media, most of the data you capture will be unstructured (documents, images). The volume of structured data is much lower. Then, here, data lake is a good option, since it can handle both types of data and will provide more flexibility for analysis.
If your online business is divided into multiple pillars, apparently you want to get summary dashboards of all of them. Data warehouses will be useful in this case to make informed decisions. Will maintain quality, consistency and accuracy of data.
Most of the time, institutions use a combination of both. They perform data exploration and analysis across the data lake and move the rich data to data warehouses for fast and advanced reporting.
Final notes
In this post, we have seen the differences between data lake and data warehouse based on data storage, the purpose of use and which one to use. Understanding this concept will help the big data engineer select the correct data storage mechanism and thus make the most of the cost and processes of the organization..
The following are some additional data engineering resources that I highly recommend you check out:
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