What is a data lake and why is it important?

Data lake is a repository already seen as indispensable in this reality in which big data is one of the main resources for analysis and business decisions .

It is a resource that brings countless advantages to organizations in their data-driven decision making and in the automation of these intelligent decisions as well.

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Revolutionizing the way we structure and use data.

Follow along to understand what the data lake is , its importance, its main elements and challenges, and how to apply it in your company.

What is a data lake?

Data lake is a repository already seen as indispensable in this reality in which big data is one of the main resources for analysis and business decisions .

Although data lake is not a new term, it is an extremely current technology that follows the need for companies to generate more and more business value .

However, there is a great difficulty to be overcome: more and more data is generated, from more different sources , at greater speed .

How to handle all of this?

To stay up to date , your business needs to have an architecture capable of handling this enormous volume of data , allowing it to be used when, how and in whatever way your company needs it, be it:

• structured, unstructured or hybrid.
• coming from texts, spreadsheets, images, databases, etc.

This architecture , much broader than that of a data warehouse, is called a data lake .

This is a resource that allows the storage and manipulation of a company's data in a single location , where it can be saved as is without the need for structuring.

In other words, it is a centralized repository that allows you to store, on a large scale , all your structured and unstructured data .

What is the importance of a data lake?

The rapid growth in data volume and complexity causes several problems for companies, which use data in their decisions, since building large databases is a major technical challenge .

This is why you need a data lake .

After all, organizations (and your company too!) need to be agile, flexible, digitalized and capable of dealing with unpredictable situations if they want to remain competitive and go further . And this is exactly what rigid, slow technological structures designed to meet specific needs are incapable of accomplishing.

Therefore, the data lake becomes a fundamental resource - due to the architecture that allows the generation of relevant insights about the business, and necessary - for the collection, storage, organization and interpretation of this complex data on a large scale .

What are the main elements of a data lake?

Now that you understand what data lakes are and how important they are, it's time to learn about some key resources that you can explore when applying them to your company.

• Secure storage: Allows you to store relational data, such as operational databases and line-of-business applications, and non-relational data, such as mobile applications, IoT devices, and social media.

• Data cataloging: makes it possible to understand what data is in the data lake, through tracking, cataloging and indexing data.

• Analytics: Enables various roles in your organization, such as data scientists, data developers, and business analysts, to access data using tools and analytical frameworks of your choice.

• Machine learning: Allows organizations to generate different types of insights, including reporting on historical data and machine learning, where models are built to predict possible outcomes and suggest prescriptive actions.

• Data movement: allows you to import any quantity, collect from multiple sources and move data in its original format saving time, as there is no need to move your data to a separate analysis system.

In fact, we have good news ! Even using a data lake, you can continue to count on the support of your data warehouse .

Let's better understand the difference between data lake and data warehouse and how they complement each other ?

Data lake x data warehouse: what are the differences?

First of all, it is necessary to recognize that both the data warehouse and the data lake are viable solutions for implementing big data projects .

However, they must be evaluated according to the needs of each company , as they present technical and conceptual differences in terms of architecture and purpose.

Secondly, it is important to highlight that dala lake is not a type of data warehouse 2.0 , despite supporting greater amounts and varieties of data. They are complementary tools that generate a lot of value when they work in sync .

The issue is that in traditional data warehouses it is very difficult to deal with unstructured data , something that is increasingly important for business. For this reason, it became necessary to adopt a more flexible system to deal with market transformations, the data lake .

Both solutions have pros and cons, therefore, between one option and another, the decision will depend on each company, such as: its size, its limitations and the objectives of its big data projects.

Want to know more about the differences between data lake and data warehouse ? Access our complete content on this .

What are the main challenges of a data lake?

As you may have already understood, the data lake solves many problems that modern companies face. Still, like any technology, it has its own challenges . Shall we meet some?

Data reliability

Without the appropriate tools, data reliability issues can occur in data lakes, hindering the analyzes of data scientists and analysts. These problems can be caused by the difficulty of combining batch and streaming data in the data lake, data corruption , among other factors.

Query performance

The data lake still has some bottlenecks such as metadata management , improper data partitioning, etc. Therefore, as the size of data in a data lake increases, the performance of traditional query engines slows down.

Data swamp

Due to the ease and low cost of storing a data lake , the tendency is to include any and all data generated by the company within it. When this happens, the data lake can turn into a place full of disorganized and “useless” data. In this case, it becomes what we call a data swamp and loses its original functionality .

How to build a data lake?

Make no mistake, the data lake is not a magic solution just because it has a high degree of data customization for each business, sector and business strategy.

Both data lakes and data warehouses are complex structures that must be designed and implemented by professionals with experience in the subject.

Here at Indicium , we work with the most advanced tools on the market and offer the most complete portfolio of services to build your data lake in the cloud and analyze all your data.

Come and talk about your project . Get in touch today by clicking here.