Saturday, December 4, 2021

Top Cloud Data Warehouse Companies

A cloud data warehouse is a service that collects, organizes, and often stores data that is used by organizations for different activities including data analytics and monitoring. They are abstracted for end-users that just see a large warehouse or repository of data waiting and available to be processed. The market for cloud data warehouses has grown in recent years, as organizations move to take advantage of cloud economics and reduce their own physical data center footprints. Read on to learn more about the key features of a cloud data warehouse, some of the top solutions on the market, and whether or not a cloud solution is right for your organization.

Common Cloud Data Warehouse Features

Cloud data warehouses typically include a database or pointers to a collection of databases where the production data is collected. The second core element of many modern cloud data warehouses is some form of integrated query engine that enables users to search and analyze the data. This assists with data mining.

Other key features to look for in a cloud data warehouse setup:

  • Integration or API Libraries
  • Data Quality and Compliance Tools
  • ETL Tools
  • Data Access Tools/Database Searchability
  • SQL and NoSQL Data Capabilities

How To Choose A Cloud Data Warehouse Service

When looking to choose a cloud data warehouse service, there are a number of criteria that an organization should consider.

Existing cloud deployments. Each of the major public cloud providers has its own data warehouse that provides integration with existing resources, which could make deployment and usage easier for cloud data warehouse users.

Ability to migrate data. Consider the different types of data the organization has and where it is stored. The ability to migrate data effectively into a new data warehouse is critically important.

Storage options. While data warehouse solutions can be used to store data, having the ability to access commodity cloud storage services can provide lower-cost options.

Top Data Warehouse Providers and Solutions

Amazon Redshiftaws

Value proposition for potential buyers: As Amazon’s entry into the cloud data warehouse market, Redshift is an ideal solution for those organizations that have already invested in AWS tooling and deployment.

Key Values/Differentiators:

  • A key differentiator for Redshift is that with its Spectrum feature, organizations can directly connect with data stores in the AWS S3 cloud data storage service, reducing the time and cost it takes to get started.
  • One of the benefits highlighted by users is Redshift’s performance, which benefits from AWS infrastructure and large parallel processing data warehouse architecture for distributing queries and data analysis.
  • For data that is outside of S3 or an existing data lake, Redshift can integrate with AWS Glue, which is an extract, transform, load (ETL) tool to get data into the data warehouse.
  • Data warehouse storage and operations are secured with AWS network isolation policies and tools, including virtual private cloud (VPC).

Integration Suggestions:

  • Amazon CloudWatch
  • Amazon RDS for PostgreSQL
  • Amazon Aurora PostgreSQL
  • Amazon S3
  • Amazon DynamoDB
  • Amazon EMR

Google BigQueryGoogle

Value proposition for potential buyers: BigQuery is a reasonable choice for users that are looking to use standard SQL queries to analyze large data sets in the cloud.

Key Values/Differentiators:

  • As a fully managed cloud service, the setup of the data warehouse and resource provisioning are all handled by Google, using serverless technologies.
  • The ability to easily query data with either SQL or via Open Database Connectivity (ODBC) is a key value of BigQuery, enabling users to use existing tools and skills.
  • Logical data warehousing capabilities in BigQuery let users connect with other data sources, including databases and spreadsheets to analyze data.
  • Integration with BigQuery ML is a key differentiator, bringing the worlds of data warehousing and Machine Learning (ML) together. With BigQuery ML machine learning, workloads can be trained on data in a data warehouse.

Integration Suggestions:

  • Google Data Studio
  • Tableau
  • Looker
  • Microsoft Power BI
  • Kubernetes
  • Google Compute Engine

IBM Db2 Warehouseibm

Value proposition for potential buyers: IBM Db2 Warehouse is a strong option for organizations that are handling analytics workloads that can benefit from the platform’s integrated in-memory database engine and Apache Spark analytics engine.

Key Values/Differentiators:

  • Integrates the Db2 in-memory, columnar database engine, which can be a big benefit for organizations looking for a data warehouse that includes a high-performance database.
  • Apache Spark engine is also integrated with Db2, which means that users can use SQL as well as Spark queries against the data warehouse to derive insights.
  • Db2 Warehouse benefits from IBM’s Netezza technology with advanced data lookup capabilities.
  • Cloud deployment can be done in either IBM cloud or in AWS, and there is also an on-premises version of Db2 Warehouse, which can be useful for organizations that have hybrid cloud deployment needs.

Integration Suggestions:

  • Oracle Cloud Lift
  • IBM Common SQL Engine
  • Podman (Linux)
  • Microsoft Power BI
  • IBM Netezza tools

Azure Synapse Analytics


Value proposition for potential buyers: Azure Synapse Analytics, previously known as Azure SQL Data Warehouse, is well suited for organizations of any size looking for an easy on-ramp into cloud-based data warehouse technology, thanks to integration with Microsoft SQL Server.

Key Values/Differentiators:

  • Microsoft released a major update for Azure SQL Data Warehouse in July 2019 with the Gen2 update, providing more SQL Server features and advanced security options.
  • Dynamic Data Masking (DDM) provides a very granular level of security control, enabling sensitive data to be hidden on the fly as queries are made.
  • Existing Microsoft users will likely find the most benefit from Azure SQL Data Warehouse, with multiple integrations across the Microsoft Azure public cloud and more importantly, SQL Server for database.
  • In contrast to simply running SQL Server on-premises, Microsoft has built on a massive parallel processing architecture that can enable users to run over a hundred concurrent queries.

Integration Suggestions:

  • Apache Spark
  • Azure Data Share
  • Microsoft Power BI
  • Azure Private Link

Oracle Autonomous Data Warehouseoracle

Value proposition for potential buyers: For existing users of the Oracle database, the Oracle Autonomous Data Warehouse might be the easiest choice, offering a connected onramp into the cloud.

Key Values/Differentiators:

  • A key differentiator for Oracle is that it runs the Autonomous Data Warehouse in an optimized cloud service with Oracle’s Exadata hardware systems, which have been purpose-built for Oracle database.
  • The service integrates a web-based notebook and reporting services to share data analysis and enable easy collaboration.
  • While Oracle’s own namesake database is supported, users can also migrate data from other databases and clouds, including Amazon Redshift, as well as on-premises object data stores.
  • Oracle’s SQL Developer feature is another key feature, which integrates a data loading wizard as well as a database development environment.

Integration Suggestions:

SAP Data Warehouse CloudSAP

Value proposition for potential buyers: SAP’s new Data Warehouse Cloud might be a good fit for organizations looking for more of a turnkey approach to getting the full benefit of a data warehouse, thanks to pre-built templates.

Key Values/Differentiators:

  • Data Warehouse Cloud is a relatively new entrant in the space and was first announced at the 2019 SAPPHIRE NOW conference in May.
  • SAP’s HANA cloud services and database are at the core of Data Warehouse Cloud, supplemented by best practices for data governance and integrated with a SQL query engine.
  • A key differentiator for the platform is the integration of pre-built business templates that can help solve common data warehouse and analytics use-cases for specific industries and lines of business.
  • For existing SAP users, the integration with other SAP applications means easier access to on-premises as well as cloud data sets.

Integration Suggestions:

  • SAP Data Intelligence
  • SAP Analytics Cloud


Value proposition for potential buyers. Snowflake is a great option for organizations in any industry that want a choice of different public cloud providers for data warehouse capabilities

Key Values/Differentiators:

  • A key differentiator is Snowflake’s columnar database engine capability that can handle both structured and semi-structured data, such as JSON and XML.
  • The decoupled Snowflake architecture allows for compute and storage to scale separately, with data storage provided on the user’s cloud provider of choice.
  • The system creates what Snowflake refers to as a virtual data warehouse, where different workloads share the same data but can run independently.
  • Queries are made via standard SQL for analytics, with integration with both the R and Python programming languages.

Integration Suggestions:

  • Alteryx
  • Salesforce
  • Qlik
  • Tableau
  • ThoughtSpot
  • Talend
  • Domo
  • Sisense
  • SAS

Read Next: Top 10 Benefits of a Data Warehouse

Data Warehouse Platform Comparison


Key Differentiator

Amazon Redshift

High-performance and massively parallel processing capabilities.

Network isolation security.

Direct integration with S3 cloud storage.

Google BigQuery

Part of Google Cloud.

Full SQL query support.

Integration with BigQuery ML for machine learning workloads.

IBM Db2 Warehouse

Includes an in-memory columnar database.

Cloud deployment options include both IBM Cloud as well as AWS.

Integrated support for Apache Spark data analytics.

Azure Synapse Analytics

Data masking security capabilities.

Integrated with broader Azure cloud services.

Inclusion of Microsoft SQL Server support.

Oracle Autonomous Data Warehouse

Based on the latest Oracle Autonomous Database release.

Migration support for other databases and cloud data warehouse services.

Delivered by purpose-built Oracle Exadata hardware.

SAP Data Warehouse Cloud

Pre-built business templates.

Integration with existing SAP apps and services.

Based on SAP HANA database.


SQL-based queries for analytics.

Support for JSON and XML as well as structured data.

Multi-cloud deployment options.

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