The Difference Between Data Warehouse & Data Lake Explained

by Jones David

Data or information is one of the most powerful tools these days. This is why more and more organizations are dependent on data companies to leverage big data to improve their operations. Usually, data storage is handled through a traditional database. However, for big data, it is different. Companies opt for data lakes and data warehouses for storing big data.

Data warehouses and data lakes are entirely different in terms of usage, structure, and processing. With Techfetch data warehousing jobs on the rise, it is important that we understand the meaning and key differences between data lake and data warehouse. This article helps you with just the right information. 

What Is A Data Warehouse?

Data warehousing or a data warehouse is a data management system that enables the collection of large volumes of data for business intelligence. It collects data from various sources and offers important business insights. The data collected in a data warehouse is primarily intended for querying and analysis. For decades, the data warehouse has been the foundation of the business intelligence system. With the data analysis performed by the BI system, businesses can improve their daily operations.

What Is A Data Lake?

A data lake is a system or a big centralized repository. It allows you to store structured, unstructured, and semi-structured data. Unlike a data warehouse, a data lake allows you to store raw data. There is no restriction on the file or account size. A data lake provides big amounts of data storage for better analytical performance.

Difference Between Data Warehouse And Data Lake

  1. Mode Of Operation

Data warehousing is commonly used for OLAP ( Online Analytical Processing). This includes collecting queries, running reports, and performing analysis. These operations are done once your transactions are complete. For example, suppose you want to go through transactions with a certain client. Since the data warehouse stores formatted/processed data, finding the required report will be much easier.

Data lakes are used for performing raw data analysis. All types of raw data, like pdf, XML files, and images, are collected for analysis. It is not necessary to define the schema while capturing data. This is because, when you capture the data, you might not know how to use the data in the future. Hence, you can perform detailed data analysis to derive actionable insights.

  1. Security

Data warehouses can store sensitive data. Such data, like credit card information, compensation data, and healthcare data, are used for reporting purposes. Since the companies have used data warehouses for decades, their data security is quite strong. Data warehouses enlist only authorized personnel to perform any data-related activity.

Compared to a data warehouse, a data lake is a new technology and is evolving. A data lake is made with the help of open source technologies. Hence the data security might not be as strong as the data warehouse.

  1. Processing

In a data warehouse, before loading it with data, you need to have a model. In other words, the data should have some structure or shape. Such structure and shape giving process is known as schema-on-write.

On the other hand, the data lake allows you to load raw data. However, whenever you want to use the data, you need to give a structure and shape. Such a process is called schema-on-read.

  1. Processing Time

A data warehouse provides insight into a pre-defined question from processed data. Hence, any changes to a data warehouse would need more time.

Since data lakes store raw data, users can access it before it is cleansed, transformed, and structured. Therefore, users can get the results quicker than a data warehouse.

  1. Data Quality

The data warehouse has high-quality data. Since the data is formatted before storing, it can be considered as the single source of truth. The data lake stores raw data that is not curated.

  1. Agility

The data warehouse is a structured data storage with fixed configurations. Changing the data warehouse structure might not be difficult technically. However, the process is quite time-consuming.

On the other hand, the data lake does not have any structure or shape. This agility helps the data scientists and developers to configure and reconfigure the data queries, models, and applications.

  1. Cost Of Data Storage

In the data warehouse, the data storage cost is a bit high. This is due to the expensive software used by the data warehouses. In addition to that, the data warehouse requires high maintenance. The space, cooling, power, and telecommunications require regular maintenance. Another factor to consider is that the data stored in the data warehouse has a denormalized format. Such formats take up a huge amount of disk space.

The data storage cost in the data lake is quite low. This is because the open-source software used in the data lake is less expensive. Also, since the data lake stores raw, unstructured data, it can store huge data volumes at a low cost.

  1. Users

A data warehouse is the best option for operational users as it is easy to understand and well-structured.

A data lake is a good option for those users who prefer deep analysis. Users like data scientists and developers would require advanced analytical tools with great capabilities like predictive modeling.

  1. Complaints

The main complaint regarding a data warehouse is the problem or inability that arises while making any change in it. The complaint against a data lake is that it stores data in raw format. The data can be structured only when there is a need.

Data lakes and data warehouses are major components of new data architecture. The data lake is the initial point where the company-wide raw data is captured. It is also the place where a data warehouse collects the data and structures it. If an organization incorporates the data warehouse and the data lake, it can give better insights to make wise business decisions. With both data warehouse and data lake, a company can become more agile and scalable in the industry.

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