Snowflake’s query engine can power complex data pipelines and large-scale analytics through the use of compressed columnar data storage. Snowflake comes with robust SQL support and boasts high availability and reliability. Snowflake is a cloud-based data platform that can be employed for various use cases, such as data warehouses, data lakes, or even unified data stores. Redshift ML helps build machine learning models through SQL and deploy them using AWS Sagemaker. Redshift Spectrum enables querying data located in S3 or other data lakes using the processing power of your Redshift cluster. Its federated query feature allows one to query external operational databases and join their data with Redshift to derive insights. The two platforms accomplish this objective differently because of the approach in their underlying design and marketing visions.īeyond traditional warehouse features, Redshift offers many nice-to-have features. Redshift and Snowflake are cloud-based data platforms that employ massive parallel processing to help ingest and process huge volumes of data. This article will discuss the key differences and similarities between Snowflake and Redshift. Snowflake is a cloud-agnostic, serverless data platform that supports many workloads, including data lakes, data warehouses, and machine learning. Being cloud-agnostic, you can run Snowflake on any cloud – Amazon Web Services (AWS) Google Cloud Platform (GCP), and Microsoft Azure (Azure). AWS Redshift started out as a cloud-based data warehouse service and has since been moving towards becoming a data platform by adding more features, such as data lake implementation support and an independent query engine. They provide ready-to-use solutions.ĪWS Redshift and Snowflake are two popular platforms in the cloud-based data platform space. Fully managed cloud-based data platforms help organizations focus on their core business problems without getting distracted by the herculean amount of effort required to maintain the infrastructure.
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