Data Lakes: almost everything you need to know and seven alternatives for your company

A data lake (also known as a Data Lake), provides companies with ample space in which to store a large part of their organization’s data, whether or not it is structured, in a way that makes it much easier for companies to understand with what information counts and what value they can extract from it to make informed business decisions.

In this type of solutions, companies have tools that make it easy to extract data from different areas of information and even more, to be able to do it transparently without the different silos in which that information may be found getting in the way. stored.

In addition, they usually offer all kinds of applications that make it easier to understand the nature of the data that is stored and how to process it quickly. In this sense, it is common to find tools for natural language processing (NLP), AI, ML, Data Mining and even predictive analysis that allows information to be offered in real time.

Although these data lakes have traditionally been kept in the companies’ CPDs, the deployment of these solutions and cloud environments has meant a before and after, since they allow companies to grow as the information they house does, without any interruption in service. Between the characteristics that this type of solutions sharewe can highlight the following:

  • Data visualization: allows users to explore and analyze large volumes of unstructured data by creating interactive visualizations to understand its content.
  • Scalability: It enables businesses with databases of all kinds to handle sudden spikes in demand without worrying about crashes or system crashes due to lack of processing power.
  • File upload/download: allows uploading and downloading of files from the cloud or from local servers in the data lake area.
  • Machine learning: It helps AI systems learn about different types of information and detect patterns automatically.
  • Integration: facilitates compatibility between multiple programs, so that organizations can use any application they choose without having to worry about incompatibility issues between them.
  • Accessibility: It ensures that any authorized user can access the necessary files without having to wait for long download or analysis times.

Among the names that have best positioned themselves in this market, we can find the main hyperscalars, but also some smaller companies that have a very outstanding value proposition. Thus, in this field, it is worth mentioning the following:


Snowflake offers a SaaS platform that provides enterprises with an all-in-one platform for all data lake needs, data warehousing, data engineering, data science and machine learning, data application, collaboration and cybersecurity.

Its most valued feature is its ability to break down barriers between databases, processing systems and storage spaces, unifying them into a single system.

With Snowflake, companies can combine structured, semi-structured, and unstructured data of any format, including from different clouds and regions, as well as data generated from Internet of Things (IoT) devices, sensors, and web data.


Cloudera’s data lake service is structured as a cloud-based Big Data processing platform that helps organizations effectively manage, process, and analyze all the information their organization generates.

The platform is designed to handle structured and unstructured data, making it ideal for a wide range of workloads such as ETL, data warehousing, machine learning, and flow analytics.

Cloudera also offers a managed service called Cloudera Data Platform (CDP)which facilitates the deployment and management of data lakes in all types of clouds or even, on premises.

Azure Data Lake

Azure Data Lake is Microsoft’s cloud data storage solution that enables users to capture data of any size, type, and ingestion speed. Azure Data Lake integrates with other Microsoft products for enterprises in areas such as identity, data management, and security.

Among its most interesting features, its tool stands out. Azure Data Lake Analyticswhich bills itself as the first cloud analytics service where you can easily build and run petabytes of data processing and transformation programs in massive parallel using U-SQL, R, Python, and .NET languages.

Google BigLake

Google BigLake is a cloud-based storage engine that unifies the data lakes and warehouses that a company may have. It allows users to store and analyze data of any size, type, or format.

The platform is scalable and easily integrates with other Google products and services. BigLake also has various security and governance controls in place to help ensure the quality and compliance with the different international regulations.

Apache Hadoop

Apache Hadoop is an open source framework for storing and processing big data. It is designed to provide a reliable and scalable environment for applications that need to process large amounts of data quickly. IBM, Cloudera, and Hortonworks are some of the leading providers of Hadoop-based software.

AWS Lake Formation

Amazon Web Services (AWS) Lake Formation is a fully managed service that makes it easy to create a data lake and securely store and analyze data.

With Lake Formation, users can quickly create a data lake, ingest data from multiple sources, and run analytics on data using the full potential of AWS’s myriad services.

In addition, Lake Formation offers built-in security and governance features to help organizations meet the requirements of compliance. Amazon Web Services also offers Elastic MapReduce, a hosted service that allows users to access your cluster without having to deal with hardware provisioning or configuration tasks.

data bricks

Databricks is a cloud-based platform that helps users prepare, manage, and analyze their data. Provides a unified platform for data scientistsengineers and the business, collaborate on data projects.

The app also integrates with apache spark Y AWS Lambdaenabling data engineers to build scalable batch or streaming applications.

Its Data Lake features provide a transactional storage layer that enables fast reads and writes for ad hoc queries and other modern analytic workloads, such as Big Data.

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