Data lake: 10 advantages of large and flexible storage

Data lake is an essential data repository for carrying out the best analyzes and making the right decisions in your business. Even more so when big data is one of the main resources for analysis and decisions in your company .

If you are interested in knowing how to make your organization grow using data, continue reading. Let's explain ten advantages and differences that a data lake can offer you.

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What is a data lake?

Imagine that you go fishing in a large lake filled with the most varied types of fish and seafood. After fishing, you will need to clean and prepare everything before serving, right?

So the same happens in a data lake .

Data lake is an essential data repository for carrying out the best analyzes and making the right decisions in your business. Even more so when big data is one of the main resources for analysis and decisions in your company .

We also say that it is a data lake that serves as a repository of large storage capacity and is responsible for aggregating data of all types , created and used by and for the company.

The information is available both in its raw state and in its processed version . Therefore, data stored in data lakes can be used for various types of analysis, such as visualization in dashboards , machine learning and big data processes .

This flexibility makes ETL and ELT processes even less rigid , especially compared to those used in a data warehouse (DW) .

You may be wondering: but doesn't a DW already do all of this?

Yes do! But you will see that there is a big difference anyway.

Data lake x Data warehouse

Present on the market for almost 30 years, the data warehouse has the function of storing processed data , ready for analysis and use. This allows companies and analysts to have this organized information available whenever they need it.

It seems perfectly practical, doesn't it? But have you ever thought about what would happen if the need arose to use other data that was not previously available in the data warehouse?!

That's where the data lake comes in , offering increasingly larger volumes of data and different models. By storing raw data, time and effort are also saved that would otherwise be spent processing, structuring and organizing this information.

It is important to mention that these systems are complementary . It is not necessarily necessary to replace one with another. 😉

Ideally, you know when and how to make use of these repositories , and what types of data will be needed for analysis.

Next, learn about some genres and examples of data that are stored in a data lake .

Structured data

Data is standardized and formatted in rigid, well- defined structures , so that reading is easier when viewed in a group. This type of data guarantees companies greater control and ease of use as they are better prepared.

See some examples of structured data:

• databases
• electronic spreadsheets
• CSV files

In the end, structured data ends up functioning as organized labels that make work more simplified, in addition to helping with information retrieval.

Unstructured data

This is information presented in its raw form , without any type of treatment or organization. Therefore, they end up demonstrating greater flexibility of use , in addition to being bulky and having many more format options .

The main examples of unstructured data are:

• text files
• images
• video files
• social media data

In short, unstructured data does not have all of its metadata filled in , which makes automation difficult. It is difficult, for example, to classify all the words in a text file.

Semi-structured data

Despite not being stored in databases or even tables, semi-structured data still has some type of organization. To do this, they rely on metadata or semantic tags that help keep them in a hierarchical order even if there is some inconsistency.

Among the types of semi-structured data, we have:

• HTML codes
• e-mail
• XML files
• JSON files

Now that you know what a data lake is and the types of data that are stored in it, it's time to learn about the ten main advantages .

Data lake: 10 advantages for your company

Compared to the data warehouse , in addition to (1) the speed of adding data and having (2) lower implementation costs, a data lake (3) does not require the structuring and organization of data and, therefore, allows (4 ) real-time analysis .

The combination of these 4 advantages above allows analytics teams to dedicate themselves even more to analysis, not other activities.

But it's not over!

A data lake also has these 5 more advantages :

• (5) greater scalability;
• (6) accesses without IT support;
• (7) synchronization with more data science tools ;
• (8) availability of data at any time;
• (9) simultaneous accesses.

And the 10th advantage?

It's this: (10) a data lake can also be very useful in BI projects , especially due to the “ in-data-lake BI ” process, which gives institutions a better chance of reacting to market dynamism.

“Does my company need a data lake?”

If it generates value from data , yes!

According to this research by Aberdeen , companies that use data lakes tend to outperform the competition by growing around 9% of revenue organically. 🚀

This data appears due to the possibilities of new types of analysis that data lakes offer in situations that were not foreseen . This allows leaders to make assertive and quick decisions, according to the opportunities that arise.

This agility generates business growth and increased productivity through customer attraction and retention.

Want to know where to start implementing a data lake?

Indicium offers hands-on consultancy services , that is, it analyzes your business and is also responsible for making the necessary changes .

Get in touch now and start gaining a competitive advantage over the competition!