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Data lake
The key to store and analyze data
A data lake is a place where a large volume of data can be stored in its original format, without the need to organize or process it first. It is especially practical for companies that work with large volumes of information coming from various sources, in an era where its handling and storage has become very relevant for the search for solutions and decision-making. We tell you in detail what a data lake is, how it differs from traditional data storage solutions, and its benefits.
What is a data lake?
Data lakes are centralized repositories that allow large volumes of data to be stored in their original format, whether structured, semi-structured, or unstructured. Instead of processing and transforming data before storing it, as occurs in traditional systems, a data lake preserves data as it is collected, ready to be processed when needed.
In an environment such as a business, where data comes from various sources, such as applications, sensors, social media, or Internet of Things devices, being able to keep the data in its original format facilitates access and analysis by different users, from data scientists to analysts and developers.
Benefits of a data lake
Now that you know what a data lake is, do you know why they have become an attractive option for many companies? They offer a number of advantages to businesses, including the following:
Main differences between a data lake and a data warehouse
Data lake vs data warehouse: Which solution is best? Both are data storage solutions with their advantages and disadvantages, as they present significant differences.
First of all, data lake architecture makes it possible to store data in its original format, while data warehouses require data to be transformed and structured before being stored. This responds to the different purpose of each of these solutions: while data lakes are designed to analyze and summarize unstructured data sets, data warehouses are optimized for sending and receiving data at high speeds.
Furthermore, data lakes and data warehouses are more suitable for different data user profiles: data lakes are better for data scientists and technical analysts, while data warehouses are used by business analysts and IT staff. Data lakes are usually based on platforms such as Hadoop or Amazon S3, while data warehouses use systems such as Snowflake, Redshift, or Teradata.
On the other hand, data lakes are usually cheaper due to their ability to use scalable storage, while data warehouses require a larger investment in infrastructure and licenses.
Key features of data stored in data lakes
Data stored in a data lake has certain characteristics that differentiate it from data stored in other storage systems:
Repsol and data lakes
Thanks to its features and advantages, the data lake is a key tool for data-driven decision-making at Repsol, which our company uses to boost its digital transformation.
With a data lake, Repsol centralizes information from various sources, such as energy operations, Internet of Things sensors, transactions, and customer data. This facilitates advanced analysis and the use of technologies such as artificial intelligence and machine learning to optimize processes, improve operational efficiency, predict failures, and personalize services.
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