Today’s companies work with huge amounts of data. Therefore, they are obliged to find solutions that allow them to manage all this data. They also need to process it in an appropriate way to extract as much information as possible.
For a long time, the Data Warehouse has been concerned with facilitating access to such data. Even if it originates from different sources, it has been vital to analyze the market, customers, and competition. From there, the most appropriate decisions have been made.
But in recent times, a new concept has been incorporated, and we refer to the Data Lake. This is more focused on integrating, managing, and distributing all the data in the shortest time possible.
Next, we are going to show you the differences between data lake and data warehouse. But before that, have a clear concept of both the Data Lake tool and the Data Warehouse tool.
What Is a Data Warehouse
A Data Warehouse is a data storage system designed to support data flow from operating systems to decision systems. It collects data from various sources, internal or external, and organizes it in a particular way.
This way allows optimizing its recovery for commercial purposes (extract business insights). It only contains the data for which you want to give specific use. These are usually structured (sometimes they come from relational databases) or not.
In short, it is a unified repository for all the data collected by the various systems of a company.
What Is A Data Lake?
In the 2000s, Data Lakes emerged as a more cost-effective alternative to unstructured data storage. Although this type of data could already be stored in the previous formats, the debugging and preparation processes were long and expensive.
Data Lakes store raw data without any structure, hierarchy, or organization. Data from any source, in any format.
Being unstructured, they are much more flexible than data warehouses. However, the latter, as a more mature technology, also have better security systems.
The idea is to dump all kinds of data to the Data Lakes, in case it is needed later, in the most economical and scalable way.
Difference between Data Lake and Data Warehouse
The term Data Lake refers to a place where structured or unstructured, raw, and unorganized data is stored. The main purpose of these data is to use for later analysis.
It facilitates that those who will use the data can do so in a more creative way. It is the system that gathers more data since it does not reject the information.
A Data Warehouse is an ordered warehouse prepared to be used by the company that owns it. With this technology, data can be stored in an orderly manner, and consultation and analysis are facilitated. In other words, it is a data warehouse that can be transformed into knowledge.
When deciding what type of management is the most appropriate, one of the questions to ask is who will use that data. If the person who will use the data has little knowledge at a technological level, it will be essential that they are organized and structured. In this way, you can even use them in Excel. Therefore, in this case, it would be best to use the Data Warehouse.
While if what you want is to analyze without conditioning a large variety of data. If you also don’t need it structured and an expert will do it, then it is best to use Date Lake. You will be able to squeeze out all the possibilities.
Data Lake vs Data Warehouse: Which One to Choose?
You may already have a clearer understanding of the differences between Data Lake vs Data Warehouse. But we can still go deeper into this matter. Therefore, in the following points, you will discover more aspects that will be interesting to you:
- When using the Data Lake, all data is preserved; however, it takes time to figure out the Data Warehouse.
- With the Date Lake, all data is kept, as we said, regardless of its class, and without its structure having been normalized. The information will always be in its original form and will only change when it is to be used.
- With the Data Warehouse, the data can be used by all users. This includes people who have a higher level of analysis and those who have more fundamental knowledge.
- The Data Lake adapts very well to changes, while the Data Warehouse does not. The Data Warehouse needs time to manage the information. The Data Lake does not require an initial investment of time, since the data is stored and delivered raw.
We understand the difference between Data Lake and Data Warehouse can be a little confusing. So, what is the best solution? It will depend on our problem.
As the volume of unstructured data increases, cloud Data Lakes become more popular. They are more cost-effective and easier to move when needed. However, there will always be a place for Data Bases and Data Warehouse.