Solutions Review’s listing of the best cloud data warehouse solutions is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best cloud data warehouse solutions based on each solution’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.
The editors at Solutions Review have developed this resource to assist buyers in search of the best cloud data warehouse solutions to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best cloud data warehouse solutions all in one place. We’ve also included introductory software tutorials straight from the source so you can see each solution in action.
For an even deeper evaluation of top cloud data warehouses, we recommend Qlik’s new Cloud Data Warehouse Comparison Guide which offers a look at the core differences between platforms.
Note: Companies are listed in alphabetical order.
The Best Cloud Data Warehouse Solutions
Platform: Amazon Redshift
Description: Amazon Redshift is a fully-managed cloud data warehouse that lets customers scale up from a few hundred gigabytes to a petabyte or more. The solution enables users to upload any data set and perform data analysis queries. Regardless of the size of the data set, Redshift offers fast query performance using familiar SQL-based tools and business intelligence applications. AWS also has multiple ways to do cluster management depending on user skill level.
Platform: Google BigQuery
Description: Google offers a fully-managed enterprise data warehouse for analytics via its BigQuery product. The solution is serverless and enables organizations to analyze any data by creating a logical data warehouse over managed, columnar storage, and data from object storage and spreadsheets. BigQuery captures data in real-time using a streaming ingestion feature, and it’s built atop the Google Cloud Platform. The product also provides users the ability to share insights via datasets, queries, spreadsheets and reports.
Platform: IBM Db2 Warehouse
Description: IBM Db2 is a client-managed, preconfigured data warehouse that runs in private clouds, virtual private clouds, and other container-supported infrastructures. It features built-in machine learning, automated scaling, built-in analytics, and SMP and MPP processing as well. Db2 also touts flexible deployment so users can write applications once and move them to the right location with minimal or no changes required. Other key features include, but are not limited to fast query processing, compatibility with Db2, PDA and Oracle, and an embedded Apache Spark engine.
Platform: Azure Synapse
Description: Microsoft Azure Synapse is an analytics service that includes data integration, enterprise data warehousing, and big data analytics. The solution enables users to query data using either serverless or dedicated resources. It offers a unified experience to ingest, explore, prepare, manage, and serve data for business intelligence and machine learning as well. Synapse also touts advanced security and privacy features like column and row-level security and dynamic data masking.
Platform: Oracle Autonomous Data Warehouse
Description: Oracle Autonomous Data Warehouse is a cloud data warehouse service that helps organizations secure data and develop data-driven applications. It automates provisioning, configuring, tuning, scaling, and backing up the data warehouse as well. Oracle also includes tools for self-service data loading, data transformations, business models, automatic insights, and built-in coverage for database capabilities which enable queries across multiple data types and machine learning analysis.
Description: Panoply automates data management tasks associated with running big data in the cloud. Its Smart Data Warehouse requires no schema, modeling, or configuration. Panoply features an ETL-less integration pipeline that can connect to structured and semi-structured data sources. It also offers columnar storage and automatic data backup to a redundant S3 storage framework.
Platform: SAP Data Warehouse Cloud
Description: SAP Data Warehouse Cloud is a data warehouse service built on the SAP HANA Cloud database. It connects data across multi-cloud and on-prem repositories in real-time while preserving the business context. The product also enables users to model, visualize and share data within a governed environment. It includes prebuilt data models, semantic views of SAP application data, and transformation logic that utilizes the vendor’s expertise from its ecosystem of partners as well.
Platform: Snowflake Cloud Data Platform
Description: Snowflake offers a cloud data warehouse built atop Amazon Web Services. The solution loads and optimizes data from virtually any source, both structured and unstructured, including JSON, Avro, and XML. Snowflake features broad support for standard SQL, and users can do updates, deletes, analytical functions, transactions, and complex joins as a result. The tool requires zero management and no infrastructure. The columnar database engine uses advanced optimizations to crunch data, process reports, and run analytics.
Platform: Teradata Vantage
Description: Teradata offers a broad spectrum of data management solutions that include database management, cloud data warehousing, and data warehouse appliances. The company’s product portfolio is available on its own managed cloud and on Amazon Web Services or Microsoft Azure. Teradata provides organizations the ability to run diverse queries, in-database analytics, and complex workload management.
Platform: Yellowbrick Data Warehouse
Description: Yellowbrick Data offers a data warehouse for distributed clouds that lets customers deploy in private data centers, public clouds, and the network edge. It touts a modern, MPP analytic database designed for demanding batch, real-time, interactive and mixed workloads. Yellowbrick continuously implements the latest advances in software and hardware protocols and combines them with smart thinking about database architecture. As a result, Yellowbrick’s data warehouse is quickly provisioned and easy-to-use regardless of where it is deployed.
Read Qlik’s complete Top Cloud Data Warehouses for the Enterprise guide: Amazon vs. Azure vs. Google vs. Snowflake.
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