UNDERSTAND DATA LAKE AND THE 3 ADVANTAGES OF THIS TYPE OF DATA ARCHITECTURE - Quesoft Tech

 UNDERSTAND DATA LAKE AND THE 3 ADVANTAGES OF THIS TYPE OF DATA ARCHITECTURE

For companies, Data Lake can be useful in extracting datasets. In this repository, it is possible to find a multitude of data, both structured and semi-structured and unstructured. Therefore, it is a valuable source in obtaining business intelligence.

Because it has this structure, it is necessary to perform periodic maintenance on the Data Lake, as well as apply governance. The intention is to prevent the company from having in this repository data that will never be used, or even, if used one day, not be useful to the business.

Continue reading until the end and understand everything about Data Lake!

How is the structure of the Data Lake?

Because it does not need any previous treatment, the structure of a Data Lake is considered simple. In practice, the records can be stored both locally and in the cloud, and several different business sources can feed this repository.

Because of having heterogeneous data, the data scientist can try to correlate seemingly unconnected records. This can make the business detect a deficiency in its internal processes. Still, it may reveal some flaw that prevents the company from selling more to its customers, among several other possibilities. Efficient processes are one of the pillars of digital transformation. 

How is the Data Lake built?

Due to its very large storage capacity, the Data Lake allows storing data of different sizes. Spreadsheets, databases and business management systems are some sources that can feed the repository. Other possibilities, in this sense, are data from:

• Internet of Things;
• streaming ;
• sensors, something that can occur in segments such as industry;
• videos;
• social media;
• data and events from websites, such as clicks on pages;

More practically, the Data Lake does not require records to be organized into tables. In it, it is possible that, for example, both program log files and text documents coexist. The only procedure that needs to be done internally in the Data Lake is data categorization.

It serves to avoid the Data Swamp problem, or data swamp , which occurs when the analytical company stores a large amount of data that will never be used. Data scientists and other professionals need to know in advance which records they can find in the Data Lake, as this will make further analysis and treatment more effective. 

How is Data Lake an extension of data architecture?

Good data architecture allows the company to have more information security in its processes, in addition to compliance with the General Law for the Protection of Personal Data (LGPD). In addition, it defines how data will transit between various systems, thus allowing integration and interoperability.

That said, the Data Lake, as much as it has records without any prior refinement, requires monitoring and governance. Taking this precaution ensures that this repository will not, at any time, deviate from what was designed in terms of data architecture. In this way, professionals such as data scientists and engineers will have an easier time, respectively, when analyzing and executing data pipelines from the Data Lake. 

What is the main purpose of the Data Lake?

Companies always need to look for ways to increase their competitiveness and productivity. In this way, the chances of attracting more customers increase, allowing for revenue that promotes the scalable growth of operations.

The best way to identify and correct failures is through data analysis and treatment. Some companies use the Data Warehouse — a repository whose data has undergone some prior refinement. Others prefer the Data Lake, with the intention of having a more generalized set of records. 

Therefore, having a Data Lake means that the company believes in the potential of Big Data and Data Science to help it obtain knowledge and business intelligence . Even though it is a bit risky to keep so much heterogeneous data, which may never be used, the idea of ​​having this repository is to extract a set of records from it and, only later, define what will be done with them. The fact of promoting governance and categorization to the Data Lake records increases the chances of obtaining business knowledge. 

In practice, how does the Data Lake work?

The process of extracting a set of data from a data lake for analysis is called schema-to-read. Whoever is going to do this does not need to export the records to another system, since everything can be done directly through the repository. Getting reports into a data lake is often ad-hoc. It is important to highlight that it is also possible, through a scheme, to automate the copying of this report.

Ingesting, extracting, cleaning, consistency and data usage

These are the five steps of how to use Data Lake. The first is very intuitive, consisting of ingesting the repository from various sources, including structured, semi-structured and unstructured data. In addition, at this stage, ports and firewalls are opened. In data extraction, the idea is to obtain useful information in order to reduce the volume of data. Cleansing and consistency are the two phases where data transformation actually takes place.

Finally, the usage stage consists of employing an Artificial Intelligence or Machine Learning model, it is important to note that these require structured records. For this, the schema for reading is used. 

What are the benefits of Data Lake?

The first benefit of the Data Lake is that it allows input of any type of data. An important point to be emphasized is the following: in a company, systems tend to delete their log files after a certain time, due to the fact that they take up a lot of space in memory. 

Such systems, in theory, no longer need these files. However, once inserted in the Data Lake, they can be of great use, considering that the company has data scientists or other professionals with expertise to extract some business knowledge from there.

1. Unique interface

Another extremely important advantage is that professionals work in a single interface. In practice, it is enough to use the Data Lake when analyzing and treating a set of records, thus dispensing with the adoption of several different systems to access the data that professionals need, as well as several different storage technologies. 

2. Scalability

Depending on it, it is possible to greatly expand the storage in a Data Lake. Such a structure is suitable, for example, when a data scientist extracts a set of records, with the intention of obtaining some specific knowledge that will become business intelligence. 

3. Low costs

At first glance, one might think that it is expensive to obtain and maintain a Data Lake. However, as much as it is capable of storing a large volume of data, its hardware is not usually costly for companies. 

Also, if the company prefers and finds it more convenient, it can use the cloud repository. In other words, the physical equipment for storing heterogeneous data does not require a high degree of specificity, and can be of the common type. The fact of having reduced costs also contributes to the scalability of the Data Lake. 

What type of architecture does the Data Lake fit into?

The data architecture type of Data Lake is called Data Mesh. This means that it is a decentralized arrangement, organizing records by business domains. In this way, professionals start to develop APIs capable of being used by other employees in the company. Creating these APIs can, among other things, promote faster access to specific information.

Another architecture that can be used in conjunction with Data Mesh is Data Fabric. Basically, it consists of automating data integration, with the help, among other things, of data mining and Machine Learning. The purpose of Data Fabric is the same as Data Mesh (to integrate data), with the difference being a slightly more recent technology.