Comparative guide to the best cloud data lakes 2024
Posted: Tue Jan 21, 2025 5:49 am
Cloud data lakes allow you to configure and adapt data storage to meet the analytical demands of companies in a changing context like the current one. There are a large number of platforms available on the market, in this article we will help you find the most suitable one for your business.
Why modernize your data lake architecture?
To manage large volumes of data from very diverse sources, data lakes become the perfect tool since they are fast, scalable and cost-effective. And when they work in the cloud, they offer greater agility and efficiency.
Businesses require an architecture that, in addition to allowing users to generate knowledge themselves, adapts to an unpredictable and constantly changing data landscape.
The focus should be on the ability to sustain and generate bangladesh phone number lead data in motion and analyze the most recent data in order to always be able to react in real time to what is happening in the business.
To get there, organizations need a cloud-based data analytics architecture to focus on getting the most out of their data and avoid high upfront costs of creation and maintenance.
Now, what is a cloud data lake ? It is the architecture that allows you to:
Extract data from various sources and load it into a specific catalog.
Store large volumes of information in a wide variety of formats.
Process data by running transformation routines and algorithms on raw data.
Analyze processed data for different use cases.
Have the guarantee of full availability, ease of use and data integrity ready to be governed.
Components, features and functions of the best data lakes 2024
Among the wide variety of cloud data lake providers out there, many organizations tend to wonder which solution is best suited for their business.
That's why Qlik has compiled the key features and functionalities of 6 popular cloud platforms to help you navigate your search.
Amazon Web Services (AWS) data lakes
AWS offers a variety of services to help you build secure, flexible, and cost-effective data lakes.
Its core services are Amazon Simple Storage Service (S3), which provides general-purpose storage, and Amazon Elastic MapReduce (EMR), an open-source processing engine that automates batch and streaming data processing.
Additionally, to help you easily create a data lake, Amazon offers AWS Lake Formation, a fully managed service designed to automate configuration and creation on S3.
Google Cloud Platform (GCP)
GCP offers a data lake to securely ingest, store, and analyze large volumes of diverse data. It integrates with other GCP services and includes the following key elements:
Google Cloud Storage (GCS), a general-purpose storage service that offers a low-cost option for businesses of all sizes.
Google Dataproc, a fully managed service based on open source tools that processes and analyzes data sets at cloud scale.
Google BigQuery, Google's serverless data warehouse service that enables native queries on GCS data, similar to data lakes . In addition to offering SQL users high-performance native query capabilities for data stored in GCS, Google BigQuery is an ideal complement to Google Data Lake.
Microsoft Azure Data Lake
Azure Data Lake, built on the Microsoft Azure cloud platform, provides scalable storage, processing, and analytics across platforms and programming languages.
Additionally, it includes disaster recovery features and integrates with other Azure services to provide role-based access controls and single sign-on capabilities.
Why modernize your data lake architecture?
To manage large volumes of data from very diverse sources, data lakes become the perfect tool since they are fast, scalable and cost-effective. And when they work in the cloud, they offer greater agility and efficiency.
Businesses require an architecture that, in addition to allowing users to generate knowledge themselves, adapts to an unpredictable and constantly changing data landscape.
The focus should be on the ability to sustain and generate bangladesh phone number lead data in motion and analyze the most recent data in order to always be able to react in real time to what is happening in the business.
To get there, organizations need a cloud-based data analytics architecture to focus on getting the most out of their data and avoid high upfront costs of creation and maintenance.
Now, what is a cloud data lake ? It is the architecture that allows you to:
Extract data from various sources and load it into a specific catalog.
Store large volumes of information in a wide variety of formats.
Process data by running transformation routines and algorithms on raw data.
Analyze processed data for different use cases.
Have the guarantee of full availability, ease of use and data integrity ready to be governed.
Components, features and functions of the best data lakes 2024
Among the wide variety of cloud data lake providers out there, many organizations tend to wonder which solution is best suited for their business.
That's why Qlik has compiled the key features and functionalities of 6 popular cloud platforms to help you navigate your search.
Amazon Web Services (AWS) data lakes
AWS offers a variety of services to help you build secure, flexible, and cost-effective data lakes.
Its core services are Amazon Simple Storage Service (S3), which provides general-purpose storage, and Amazon Elastic MapReduce (EMR), an open-source processing engine that automates batch and streaming data processing.
Additionally, to help you easily create a data lake, Amazon offers AWS Lake Formation, a fully managed service designed to automate configuration and creation on S3.
Google Cloud Platform (GCP)
GCP offers a data lake to securely ingest, store, and analyze large volumes of diverse data. It integrates with other GCP services and includes the following key elements:
Google Cloud Storage (GCS), a general-purpose storage service that offers a low-cost option for businesses of all sizes.
Google Dataproc, a fully managed service based on open source tools that processes and analyzes data sets at cloud scale.
Google BigQuery, Google's serverless data warehouse service that enables native queries on GCS data, similar to data lakes . In addition to offering SQL users high-performance native query capabilities for data stored in GCS, Google BigQuery is an ideal complement to Google Data Lake.
Microsoft Azure Data Lake
Azure Data Lake, built on the Microsoft Azure cloud platform, provides scalable storage, processing, and analytics across platforms and programming languages.
Additionally, it includes disaster recovery features and integrates with other Azure services to provide role-based access controls and single sign-on capabilities.