What is a data lake?

Data lake is a large centralized repository of data, essential for the growth of your business.

If you have already realized the need to have centralized and assertive information available, perhaps a data lake can help.

In this post, we explain in a very clear and didactic way what a data lake is, some points that differentiate it from a data warehouse and even what its architecture is like.

Everything so you can understand why and if your company needs a data lake to grow your business.

Enjoy and have a great read!

After all, what is a data lake?

Data lake is a large centralized repository of data, essential for the growth of your business.

The biggest advantage in using it is the possibility of storing structured and unstructured data in one place, regardless of scale. This enables different types of analysis, such as business intelligence (BI) visualizations and dashboards, or even big data processing, machine learning and real-time analytics.

This versatility in storage brings more options in the range of analyzes for data scientists, because the accessibility to information is much greater. With a DL, it is possible, for example, to access the raw data when necessary, and you can explore the content however you want without needing the help of another system.

It is important to point out that, due to the large stock capacity of this repository, good governance is essential for its proper functioning. Otherwise, there is a risk of data becoming electronic waste.

Another important detail: do not confuse data lake with data warehouse (DW)!

Data lake and data warehouse, what's the difference?

It's easy to get confused here, but despite the similarity (both are big data repositories), data lakes and data warehouses have different purposes and serve more specific cases.

We have already seen that a DL stores raw, unstructured data without requiring this information to have a pre-defined objective. A DW, on the other hand, requires an entire process of cleaning, structuring and organizing data for reporting.

In other words, while the data warehouse requires the work of refining data before storing it (which can take months or even years), the data lake offers instantaneous collection of information, allowing analysts to discover only later a practical purpose for they.

Check out other differences between data lake and data warehouse below.

Data lake:

• non-relational and relational data
• schema recorded at the time of analysis
• any data types, selected or raw
• used by data scientists, analysts and developers
• allows various types of analysis

Data warehouse:

• relational data from transactional systems
• schema defined before implementing DW
• rigorously selected data
• used by business analysts
• focused on generating reports, BI and visualizations

The architecture of the data lake is another aspect that also draws attention due to its singularities.

What is the architecture of a data lake like?

Because it has the power to store raw data mixed with structured data, the data lake has a very simple architecture, making it possible to host it in the cloud or on-premise.

The massive scalability of this architecture can reach exabytes, which is advantageous when you do not know in advance the volume of data that will be stored. Therefore, data lake architecture is excellent for data scientists who explore and extract data across the company in search of new insights.

But, despite housing so many different types of information, you need to keep in mind that a data lake is not messy!

Governance, that is, control, needs to be much more rigorous to prevent the DL from becoming a data swamp (electronic waste). Good practice is to tag all content in the data lake with metadata , and to do this before even placing it in the repository.

And now, the burning question…

Why does your company need a data lake?

Well, as you may have noticed throughout this text, you can do a lot with a data lake! Especially because it houses and works with a generous amount of data, thus opening up a world of analytical possibilities.

In short, with a data lake your company can leverage more data (from more sources) in less time, in addition to enabling more users to collaborate and analyze information in different ways, leading to more assertive and faster decision-making.

It does not stop there. With a data lake, you:

• combines CRM data to improve customer interactions;
• innovates using hypothesis testing;
• Analyzes IoT data to increase operational efficiency.

So: do you think your company or project needs a data lake ?

Indicium can help you

We have international recognition as a B2B service provider in Brazil and New York City, in addition to the trust of large customers.

Our data science services rely on professionals and cutting-edge tools to deliver the best analytics results for your business.

Contact us, let's talk about your project. 🚀

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