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You have a number of products to choose from when it comes to cloud-warehouse solutions. The following list looks at the most popular options:

 Amazon Redshift: A popular big data warehousing service that runs atop data sitting within the Amazon Cloud, it is most notable for the incredible speed at which it can handle data analytics and business intelligence workloads. Because it runs on the AWS platform, Redshift’s fully managed data warehousing service has the incredible capacity to support petabyte-scale cloud storage requirements. If your company is already using other AWS services — like Amazon EMR, Amazon Athena, or Amazon Kinesis — Redshift is the natural choice to integrate nicely with your existing technology. Redshift offers both pay-as-you-go as well as on-demand pricing structures that you’ll want to explore further on its website: https://aws.amazon.com/redshift Parallel processing refers to a powerful framework where data is processed very quickly because the work required to process the data is distributed across multiple nodes in a system. This configuration allows for the simultaneous processing of multiple tasks across different nodes in the system.

 Snowflake: This SaaS solution provides powerful, parallel-processing analytics capabilities for both structured and semistructured data stored in the cloud on Snowflake’s servers. Snowflake provides the ultimate 3-in-1 with its cost-effective big data storage, analytical processing capabilities, and all the built-in cloud services you might need. Snowflake integrates well with analytics tools like Tableau and Qlik, as well as with traditional big data technologies like Apache Spark, Pentaho, and Apache Kafka, but it wouldn’t make sense if you’re already relying mostly on Amazon services. Pricing for the Snowflake service is based on the amount of data you store as well as on the execution time for compute resources you consume on the platform.

 Google BigQuery: Touted as a serverless data warehouse solution, BigQuery is a relatively cost-effective solution for generating analytics from big data sources stored in the Google Cloud. Similar to Snowflake and Redshift, BigQuery provides fully managed cloud services that make it fast and simple for data scientists and analytics professionals to use the tool without the need for assistance from in-house data engineers. Analytics can be generated on petabyte-scale data. BigQuery integrates with Google Data Studio, Power BI, Looker, and Tableau for ease of use when it comes to post-analysis data storytelling. Pricing for Google BigQuery is based on the amount of data you store as well as on the compute resources you consume on the platform, as represented by the amount of data your queries return from the platform.