The amount of data a company needs to deal with is, in fact, too large. There is a lot of information, and increasingly, different sectors of the market are exposed to more complex data that require a powerful collection capability.
A new storage model becomes more than necessary, and that is where Data Lake comes in. This tool represents an evolution in the way data is collected, proving that standardization may not always be the solution.
In this article, we will show you the main points of this technology. Learn about it!
What is Data Lake?
One of the main purposes of creating a system such as Data Lake is to store unchanged data. That is, the conservation of information without any kind of filter, the way they are found – that is what we call Data Lake.
As we’ve known for a long time (three decades), data storage was based on the Data Warehouse system, where data was collected in a filtered, organized and processed way. Despite the advantage of standardization, it is possible that important information is lost in this process, making it impossible for analysts to find new solutions.
For the amount of data in Big Data, tools such as Data Lake present an evolution in storage. That is, it is a raw information base in which analysts can assess what can actually be used.
What are the advantages of Data Lake for business management?
There are several advantages to using Data Lake for managing a company’s information. Here we’ve listed the main ones. Check it out!
High capacity of volume and speed
Data Lake is a system capable of storing a high data load; after all, its purpose is to collect raw information. For this reason, it tends to be quite fast, since it is not necessary to go through any previous filters.
Systems such as Data Warehouses have as their standard the most restricted access, aimed only at the professionals responsible for managing the data collected. Of course, we know this is due to security issues, but that limits the possibilities that this information can bring.
With Data Lake, the process is different; the data is accessible and can be shared with different people without needing the support of an IT staff.
With a large volume of raw data collection, the analysis should obviously be deepened. After all, it is necessary to evaluate all the information by metadata, with descriptions of the origin, theme, objective, etc.
The way this information is stored in Lake requires that your analysis be advanced so that nothing is ignored, and even if something is left behind, there is always a chance to recover it.
How to build an efficient Data Lake?
There are four essential steps to building a functional Data Lake for information management. Let’s learn about each step.
Step 1 – Landing zone or raw data
The data ingestion step is where information is collected without any type of filter. This stage is separate from common IT systems. The important thing here is to not let the stored information pile up, turning it into a sort of Data Swamp.
Step 2 – Data Science Environment
At this point, the people responsible for data monitoring come on the scene. Analysts must access the Data Lake and can start carrying out experiments (creating analysis models), as well as standard evaluations.
Step 3 – Offload for Data Warehouse
The Data Lake may already have the Data Mart subdivisions, and the company may choose to store data that is not constantly used, known as cold data. However, this data will not be inactive; it can be used for later insights.
Step 4 – Critical component of data operations
At this stage, the Data Lake is already part of the company processes; it replaces the standard data storage and becomes a service for data access.
Well, we hope this text has cleared up all your questions about Data Lake. Do you want to keep increasing your knowledge? Then access our article about Big Data!