Data lake

The Nine Most Popular and Effective Cloud Data Lake Solutions to Consider in 2022

The editors at SB Analytics developed this resource to help buyers in search of the best cloud-based data lake solution to feed the needs of our present and prospective customers.

As one of the best AI company in Bangalore, we know that choosing the right vendor and solutions has always been a tedious and complicated process. An act that needs in-depth research and often comes down to finding more than one solution because of its technical capabilities.

Hence to make your search a little easier, we have profiled the best cloud-based data lake solutions all in one place that also includes introductory software tutorials right from the source so you can view each of these solutions in action.

Note: The cloud-based data lake solutions are listed in alphabetical order.

Amazon Web Services

Description: The Amazon Web Services provides a data lake solution that can automatically configure the core AWS services that are required to search, tag, analyze, transform and govern specific subsets of data across a business establishment or with other external users.

The AWS data lake solution deploys a console that the users of the platform can access to browse and search available datasets for their day-to-day business needs. The AWS solution even includes a federated template that permits the users to a version of the solution that is ready to be integrated with Microsoft Active Directory.


Description: The Cloudera Data Platform, secures and manages the data lifecycle across all key public and private cloud services. The CDP optimizes workloads based on machine learning and analytics that enables the users to view their data lineages across cloud and transient clusters, and thereby feature a single pane of glass across hybrid and multi-cloud environments.

The Cloudera Data Platform can scale to petabytes of data and thousands of diversified users. CDP also allows the users to secure and govern their platform data and metadata with integrated interfaces.


Description: Databricks provides an Apache Spark-based and cloud-based unified analytics platform that combines data science and data engineering functionalities. Databricks is a product that leverages a plethora of open-source languages and also includes proprietary features for operationalization, performance, and real-time enablement on AWS.

A Data Science Workspace in this cloud-based data lake solution also enables its users to explore data and build models collaboratively.
Databricks even provides one-click access to preconfigured Machine Learning environments for augmented ML with popular frameworks.

Google Cloud

Description: Google provides a completely-managed enterprise data warehouse for analytics via its BigQuery product. The solution is not only serverless but it also enables businesses to analyze any data by creating a logical data warehouse over managed columnar storage, and data from object storage and spreadsheets.

The BigQuery captures real-time data using a streaming ingestion feature, and it is built on top of the Google Cloud Platform.

Google Data Lake is a product that even provides its users the capability to share insights via queries, datasets, spreadsheets, and reports.


Description: The Microsoft Azure Data Lake includes all the necessary capabilities that are needed to make it easier for data scientists, developers, and analysts to store data of any shape, size, or speed and do all types of analytics and processing across different platforms and languages.

The Microsoft Azure Data Lake can also integrate with operational stores and data warehouses so that users can extend their current data applications.

The solution proclaims enterprise-grade auditing, security, and support and is built on YARN and designed for cloud environments.


Description: Oracle Autonomous Data Warehouse is a cloud-based service that aids businesses secure data, and help organizations develop data-driven applications.

It automates configuring, provisioning, tuning, scaling as well as backing up the data warehouse.

Oracle even includes tools for self-service data loading, data transformation, business models, automatic insights, and built-in coverage for database capabilities which help to enable queries across multiple data types and machine learning analysis.


Description: Panoply automates data management tasks that are associated with running Big Data in the cloud.

Panoply’s Smart Data Warehouse needs no schema, modeling, or configurations. It features an ETL-less integration pipeline that helps connect to structured and semi-structured data.

Panoply even offers columnar storage and provides automatic data backup to a redundant S3 storage framework.


Description: Snowflake provides a cloud data warehousing service built on top of AWS. The solution loads and optimizes data from virtually any data source, which includes both structured and unstructured data, including Avro, JASON, and XML.

Snowflake features a wide range of support for standard SQL, and users can do updates, analytical functions, transactions, deletes, and complex joins as a result.

It is a tool that needs zero management and no infrastructure as Snowflake’s columnar database engine uses advanced optimizations to crunch data and thereafter process reports and run analytics for the users of the platform.


Description: Teradata provides a wide array of data management solutions that include database management, cloud data warehousing, and data warehouse appliances.

Teradata’s product portfolio is available on its own managed cloud and on Microsoft Azure or AWS.

The company provides businesses the ability to run diverse queries, in-database analytics and complex workload management.


Selecting the optimal data warehousing technology can be a daunting task.

Even though all leading cloud-based data lake providers offer seemingly similar functional services in terms of scalability, reliability, flexibility, security, and more, nevertheless, the products suit different use cases and have different cost structures.

Hence if you are still not sure either about the exact storage, processing, and analytics that your business requires and is unsure which best-fitting cloud data warehouse platform will suit your needs, SB Analytics, one of the best AI company in Bangalore is ready to help.

All you need to do is to fill out our contact form and our consultant will carefully analyze your current data environment, and strategic and tactical data analytics required to suggest an optimal cloud data warehouse platform for your specific individual needs.

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