The differences between data lakes and data warehouses: a brief explanation
When comparing data lake vs. data warehouse, it's important to know that these two things actually serve quite different roles. They manage data differently and serve their own types of functions.
The market for data warehouses is booming. One study forecasts that the market will be worth $23.8 billion by 2030. Demand is growing at an annual pace of 29%.
While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes.
Both data warehouses and data lakes are used when storing big data. On the other hand, they are not the same. A data warehouse is a storage area for filtered, structured data that has been processed already for a particular use, while Data Lake is a massive pool of raw data and the aim is still unknown.
Many people are confused about these two, but the only similarity between them is the high-level principle of data storing. It is vital to know the difference between the two as they serve different principles and need diverse sets of eyes to be adequately optimized. However, a data lake functions for one specific company, the data warehouse, on the other hand, is fitted for another.
This blog will reveal or show the difference between the data warehouse and the data lake. Below are their notable differences.
- Type of Data: structured and unstructured from different sources of data
- Purpose: Cost-efficient big data storage
- Users: Engineers and scientists
- Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning
- Sizes: Store data which might be utilized
- Data Type: Historical which has been structured in order to suit the relational database diagram
- Purpose: Business decision analytics
- Users: Business analysts and data analysts
- Tasks: Read-only queries for summarizing and aggregating data
- Size: Just stores data pertinent to the analysis
Data cleaning is a vital data skill as data comes in imperfect and messy types. Raw data that has not been cleared is known as unstructured data; this includes chat logs, pictures, and PDF files. Unstructured data that has been cleared to suit a plan, sort out into tables, and defined by relationships and types, is known as structured data. This is a vital disparity between data warehouses and data lakes.
Data warehouses contain historical information that has been cleared to suit a relational plan. On the other hand, data lakes store from an extensive array of sources like real-time social media streams, Internet of Things devices, web app transactions, and user data. This data is often structured, but most of the time, it is messy as it is being ingested from the data source.
When it comes to principles and functions, Data Lake is utilized for cost-efficient storage of significant amounts of data from various sources. Letting data of whichever structure decreases cost as it is flexible as well as scalable and does not have to suit a particular plan or program. On the other hand, it is easy to analyze structured data as it is cleaner. It also has the same plan to query from. A data warehouse is very useful for historical data examination for particular data decisions by limiting data to a plan or program.
You might see that both set off each other when it comes to the workflow of the data. The ingested organization will be stored right away into Data Lake. Once a particular organization concern arises, a part of the data considered relevant is taken out from the lake, cleared as well as exported.
Each one has different applications, but both are very valuable for diverse users. Business analysts and data analysts out there often work in a data warehouse that has openly and plainly relevant data which has been processed for the job. Data warehouse needs a lower level of knowledge or skill in data science and programming to use.
Engineers set up and maintained data lakes, and they include them into the data pipeline. Data scientists also work closely with data lakes because they have information on a broader as well as current scope.
Engineers make use of data lakes in storing incoming data. On the other hand, data lakes are not just restricted to storage. Keep in mind that unstructured data is scalable and flexible, which is better and ideal for data analytics. A big data analytic can work on data lakes with the use of Apache Spark as well as Hadoop. This is true when it comes to deep learning that needs scalability in the growing number of training information.
Usually, data warehouses are set to read-only for users, most especially those who are first and foremost reading as well as collective data for insights. The fact that information or data is already clean as well as archival, usually there is no need to update or even insert data.
When it comes to size, Data Lake is much bigger than a data warehouse. This is because of the fact that Data Lake keeps hold of all information that may be pertinent to a business or organization. Frequently, data lakes are petabytes, which is 1,000 terabytes. On the other hand, the data warehouse is more selective or choosy on what information is stored.
Understand the Significance of Data Warehouses and Data Lakes
If you are settling between data warehouse or data lake, you need to review the categories mentioned above to determine one that will meet your needs and fit your case. In case you are interested in a thorough dive into the disparities or knowing how to make data warehouses, you can partake in some lessons offered online.
Always keep in mind that sometimes you want a combination of these two storage solutions, most especially if developing data pipelines.
Author: Liraz Postan
Source: Smart Data Collective