Data Warehouse Vs Data Lake: Understanding The Essential Differences
In a world full of technologies, storing numerous data become so light nowadays. And it is true, that many business organizations indeed prefer to use data warehouses and data lakes to reserve their huge company's data effortlessly.
Saving a company's data history for many years was tough in the old times. But today's technologies made this heavy thing very easy. Now, any company can store its significant data via a data warehouse and data lake.
But the thing is, a data warehouse and a data lake are different. There are many differences among them. In simple language, a data lake is raw and unorganized data. And on the other hand, a data warehouse is organized and specific data.
Let's Take An Example To Understand It More Clearly.
A water lake can be used to describe the data lake. So, a water lake contains water from anywhere in the world. That water doesn't come from one source. But it comes from a variety of sources. And all of the water got stored in one lake. This process is similar to the data lake.
Because in a data lake, multiple data come from different-different sources and are stored in one data lake. Alternatively, multiples of data from the data lake get organized and filtered, then stored in one specific place called a data warehouse.
If the data lake is compared to a water lake, then a data warehouse can be compared to a house's water tank. The reason for that is the water in a lake comes from various sources and is stored without being filtered.
However, many individuals took the lake's water and stored it in their house's water tank, filtering it so they could use it whenever needed.
Data Warehouse VS Date Lake - Differences
Structure Of The Data
All data is refined and organized in the data warehouse. And all these data come from a data lake. Additionally, data in a data warehouse is usually normalized, which indicates that duplicate information is decreased to ensure uniformity and reduce storage requirements.
As an alternative, all the data is raw and unclear in the data lake because it comes from various sources.
Storage Capacity
The data lakes always contain big data. That's the reason it has a large storage capacity for data. The data stored in data lakes are unorganized and unstructured. That's why it provides flexible and scalable solutions to reserving large volumes of data.
But for data warehouses, the storage capacity is not that large. That's why it contains flexible and perfectly analyzed data. Also, the storage capacity of a data warehouse depends on its hardware. And many data warehouses are designed to manage terabytes to petabytes of data.
Approaches/Processing
The approach used by data warehouses is a schema-on-write approach for processing the data. It is a form of organizing and reserving data where the structure and schema of data are determined and implemented before writing the data into the storage system.
On the other hand, the data lakes used a schema-on-read approach to processing their data. In this approach, the data stored are semi-structured, raw, and without format form. While reading or querying data, this approach implements the schema or data structure.
Cost
The cost of a data warehouse is high. Because it provides its users with powerful hardware and infrastructure. Currently, many business organizations prefer to use the cloud-based data warehouse for storing and preserving their company's data effortlessly. The expense of cloud-based data warehouses varies according to the usage and cloud provider.
Apart from that, the data lake cost is lower and reasonable. The reason behind it data lakes always store data in a raw and unorganized form. And they are compatible with a wide range of data processing tools and frameworks. That is why the data lake cost is lower than the data warehouse.
Objective
The objective or purpose of using the data of a data warehouse is specific. And the best advantage of a data warehouse is its storage capacity. Because a data warehouse's storage space is not wasted on data that will never be used in the coming future.
A data lake is a storage for raw data that may or may not have a defined future use. As a result, data in the data lake is less ordered and filtered.
Users
The users of data warehouses are IT sectors or many business professionals. Because as an IT company, they have to use specific and organized data for their services. That is the main reason they always prefer to use the data warehouse for saving and storing the salient data of their company.
Aside from that, the users of data lakes are data scientists and engineers. These people prefer to use data lakes for storing and reserving their data because they contain data from multiple sources. These data include raw, semi-structured, and unorganized data.
Accessibility
The data warehouse structure is perfectly designed by the developer. As a result, it is tough to exaggerate and manipulate. However, the users of a data warehouse can excess their data by using SQL (Structured Query Language). Moreover, Once data has been entered into a data warehouse, it is usually not directly altered or changed.
In contrast, the data lake has few constraints and is simple to access and modify. It is one of the most significant data lake advantages. As compared to a standard data warehouse, where data is often deemed read-only once imported, data in a data lake can be modified or changed.
So, What To Choose?
Choosing whether a business organization should utilize a data warehouse or a data lake to store data is the simplest option for anyone who understands the difference between a data warehouse and a data lake.
Furthermore, the data warehouse and data lake are great for storing valuable data for a business organization. And many companies prefer to use both data solutions in their business for smooth storage.
So, this article will help business owners to know the difference between them and selecting the perfect storing place for all their significant data.
Thanks for reading!!!
