It is undeniable how much data is a fundamental tool for the internal processes of any business, and in this context, the so-called Data Lake emerges as a great solution to add more efficiency, assertiveness, and consequently, competitiveness to companies, in an increasingly competitive market.

Proof of this was a study conducted by the Cappra Institute, back in 2021, which proved that Brazilian companies store, on average, 10 petabytes of data, and also projected a growth of 175% within the next five years.

The research reinforces how important the use of advanced methodologies is, not only for data collection, but also for an enterprise to increase its competitive capacity and ensure more prominence in the current scenario, driven by technological transformations.

Want to discover what a Data Lake is and how its modernization can benefit your business? Then you are in the right place! Read on and check out the article that we, from the CodeBlog team, have prepared for you.

Data Lake: a definition of the concept

In its literal translation, Data Lake means a "lago de dados" (lake of data), and as strange as this expression may seem, it makes perfect sense when understood.
This is because the analogy with the word "lake" conveys the idea of a repository where data flows freely and is stored in its raw state, similar to water in a lake.

In practice, the concept represents information entered into a repository without any prior processing. This process begins the moment the data is stored. Subsequently, they undergo the necessary treatments and are used in research, if needed.

Broadly speaking, Data Lakes are recognized as next-generation hybrid data management solutions capable of meeting the challenging data landscape and driving new levels of real-time analysis.

How does a Data Lake work?

A Data Lake acts as a large repository of information, but unlike a Data Warehouse, it does not require prior data processing.
This is an essential concept for dealing with the complexities of Big Data, as it allows data to be accessed in various formats and in real time. In this way, it is possible to store a large set of information from diverse sources, such as IoT (Internet of Things) based solutions, sensors, interactive logs on web pages and social networks, JSON objects, streaming data, and much more. It is important to highlight that a Data Lake is more of a concept than a technology.
After all, the ingestion and processing of data are only carried out if additional technologies are used.

Data Lake Modernization: How does it happen?

Many organizations decided, in the past, to store their raw data considering the possibility of using it in the future. And the good news is that now, these companies can make the storage process more modern and use the stored information in a more strategic and assertive way.
For this, the safest path is to migrate the data and host it in the AWS cloud, which guarantees cost reduction, agility, innovation, as well as various additional tools to simplify the analysis of information and provide valuable insights for business development.

Data Lake: the main benefits

A Data Lake offers several significant benefits.
The simple, intuitive, and highly integrated solution allows teams to work collaboratively and cohesively.
In addition, it is a low-cost, scalable, and highly available option.
Check below for a more detailed explanation of the greatest advantages of a Data Lake.

Lower costs

A Data Lake has a significantly lower cost than a Data Warehouse, for example. The reason for this is precisely its simple structure, which does not require prior data processing or constant maintenance. In other words, with a Data Lake, it is possible to avoid large investments in establishing routines. Furthermore, it is worth mentioning that implementation costs become even lower when cloud servers are used, which operate with their own infrastructure and simplify, in general, the use of the solution.

High scale

Regarding scalability, the Data Lake also presents great advantages compared to other models. This happens because of the elimination of prior data processing, which allows its scale to reach even higher levels in real time.
If cloud solutions are adopted together, this expansion can happen even faster, requiring only the acquisition of more disk space.
As a result, the search for insights is optimized according to the amount of data entered into the system, favoring a more agile and deeper analysis of the business demands, pain points, needs, and strategies.

Compatibility

In a Data Lake, data is made available in the same way it is received; therefore, it can be used and manipulated by any other tool. Thus, it is possible to meet various types of demands simultaneously through the same Data Lake.
In practice, those who need to generate reports gain more agility and productivity, as well as employees who need to perform simple or deeper analyses on the business's data science.

Speed

Within the Data Lake, data does not need to go through any prior processing.
In practice, this allows for a significant increase in speed, as information is included in the database practically at the same time it is generated. Thus, professionals can prioritize tasks and execute their work in an optimized way, without their activities being interrupted during processing.

Integration

The use of Data Lakes contributes to collaborative teamwork.
Due to the simplicity it presents, this solution allows access and use of the tool to occur even without the presence of the IT team in the company.
In other words, professionals from different sectors, such as finance, maintenance, commercial, and human resources, can rely on all the resources of the solution without difficulties.

How do AWS services, made available by CodeBit, assist in Data Lake modernization?

• Agility in responses:
  The AWS Data Lake allows you to get fast answers from all data, for all users.
  
  
  

• Ease of creation:
  AWS provides a simple way to create Data Lakes and perform all the necessary analyses.
  
  
  

• Automation:
  With AWS Lake Formation, it is much easier to automate manual tasks, reducing the time needed to build a successful Data Lake.
  
  
  

• Scalability and savings:
  AWS provides scalable and cost-effective tools to store and analyze large volumes of data.
  
  
  

• Open compatibility:
  AWS supports open file formats, such as Apache Parquet, allowing you to store data in a standard format and analyze it with various tools and techniques.

In short, dear reader, if after checking all the benefits of modernizing your Data Lake, you decided to take your company into the future, click here and learn more about all the solutions developed by the team of specialists at CodeBit, which fully supports the migration process, assisting in training, capability development, queries about AWS products, architecture, and even in studying the migration to the AWS cloud.

In the meantime, keep an eye on CodeBlog. Soon, we will have news