What are the best data lakes in the cloud?

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Cloud data lakes are designed to overcome the limitations of traditional data warehouses, offering greater scalability and cost-effectiveness to manage large and varied volumes of data across a variety of analytics initiatives. It's time for a change in your business.

What a cloud data lake entails
Cloud data lakes are platforms designed to replace traditional data repositories that have become obsolete and inefficient in the face of the large amounts of data generated by digital transformation.

By using it, companies can manage a large volume of varied data for their analytics initiatives (Artificial Intelligence, BI, machine learning, etc.) and achieve the desired scalability, speed and economic profitability.

Today, we are facing a growing migration of data lakes south korea phone number lead to the cloud. These provide great advantages to companies, such as the initial savings involved in creating and maintaining data lakes and the focus on getting the most out of data in an agile manner.

There are various platforms with specific features, functions and capabilities, but — in general — cloud data lakes meet key components that are similar:

Data Ingestion : Extract data from various sources and load it into the cloud data lake.
Analytics : Allows analysis of processed data for different use cases.
Storage : Stores large amounts of data in various formats.
Data Processing : Runs transformation routines and algorithms on raw data.
Security and governance : Ensures data availability, usability, and integrity.
Unlike on-premises data lakes, cloud data lakes provide multiple advantages in data governance : decoupled storage and compute, built-in security and encryption, transparent scalability, flexible on-demand infrastructure, and pay-as-you-go pricing.

6 cloud data lakes that stand out in the market
As businesses choose to move their data to the cloud, more and more vendors are offering solutions that have been designed to meet today's data and organizational demands.

Regardless of which cloud data lake you choose, it is key to consider platforms with robust data integration so that data reaches its destination properly.

This is essential for ingesting and migrating all types of data from different sources, as well as for processing and adjusting the data to make it available for all analytics use cases.

With a wide variety of cloud data lake providers , it can often be difficult to choose the right solution for your business. Below, you can learn about the 6 cloud data lakes considered to be the best options for organizations .

Data Lake Amazon Web Services (AWS)
This platform offers multiple services for building secure, flexible, and cost-effective data lakes. It provides different web services such as Kinesis Stream, Kinesis Firehose, and Database Migration Service [DMS], as well as partner solutions that help ingest and migrate data from cloud and on-premises sources to S3.

Additionally, AWS provides several fully managed analytics services, such as Elasticsearch and Athena, that enable you to analyze log data and perform interactive queries.

The main services that make up AWS-based data lakes are the following:

Amazon Simple Storage Service (S3), which provides general-purpose storage. In some cases, Amazon DynamoDB, a NoSQL database, is also used to store low-latency data, such as clickstream or IoT data.
Amazon Elastic MapReduce (EMR), a processing engine based on open source tools (such as Apache Spark, Apache Hive, or Presto) that automates batch and streaming data processing.
Azure Data Lake
This solution is part of the Microsoft Azure cloud platform that provides scalable storage and enables all types of processing and analytics across multiple platforms and programming languages.

Its key components are:

Azure Data Lake Storage (ADLS) Gen 2, which combines ADLS Gen 1 file system storage with binary large object (BLOB) storage to improve scalability, analytics workload performance, and cost.
Azure HDInsight, a managed service based on open source tools, and Azure Synapse, which combines SQL queries with large-scale data processing based on Apache-Spark.
Azure Data Lake Analytics, an on-demand platform that allows you to develop your own code and offers support for multiple languages, such as U-SQL, R, Python or .NET.
Additionally, it includes disaster recovery capabilities and integrates with other Azure services to provide role-based access controls and single sign-on capabilities.