Here’s the difference between Redshift and Databricks. The comparison is based on pricing, deployment, business model, and other important factors.
Amazon Redshift is a fully managed petabyte scale data warehouse service. Redshift is designed for analytic workloads and connects to standard SQL based clients and business intelligence tools. Redshift delivers fast query and I/O performance for virtually any size dataset by using columnar storage technology and parallelizing and distributing queries across multiple nodes. Most common administrative tasks associated with provisioning, configuring, monitoring, backing up, and securing a data warehouse are automated.
Databricks provides a data lakehouse that unifies your data warehousing and AI use cases on a single platform. With Databricks, you can implement a common approach to data governance across all data types and assets, and execute all of your workloads across data engineering, data warehousing, data streaming, data science, and machine learning on a single copy of the data. Built on open source and open standards, with hundreds of active partnerships, Databricks easily integrates with your modern data stack. Additionally, Databricks uses an open standards approach to data sharing to eliminate ecosystem restrictions. Finally, Databricks provides a consistent data platform across clouds to reduce the friction of multicloud environments. Today, Databricks has over 7000 customers, including Amgen, Walmart, Disney, HSBC, Shell, Grab, and Instacart.
|Categories||Data Warehouses||Data Warehouses, Data Lakes|
|Stage||Late Stage||Late Stage|
|Target Segment||Enterprise||Enterprise, Mid size|
|Pricing||Freemium||Freemium, Contact Sales|
|Location||Seattle,WA, US||San Francisco, US|
|Companies using it|