Redshift offers a concurrency scaling feature, but it comes at an additional cost and is not as robust as Snowflake’s auto-scale function. It can take anywhere from several minutes to hours since you have to add and remove individual nodes manually. The only way to get larger nodes in Snowflake is to purchase larger virtual warehouses, which inevitably drives up compute costs and makes it inefficient to run specific queries.Īutoscaling in Amazon Redshift is limited. While you can resize clusters and virtual warehouses in a single click, Snowflake does not give you any ability to resize nodes. Snowflake has a unique auto-scaling feature that allows you to automatically spin up more computing resources to handle any query load and an auto-suspend feature that helps you start and stop your virtual warehouses. Storage within Redshift is duplicated from S3, so you can compress and store data in a columnar format, just like Snowflake. Redshift runs on PostgresSQL 8, which is older and less user-friendly than Snowflake and the platform only runs on AWS, which means it’s not accessible if you’re currently using a different cloud. All nodes within a cluster are automatically partitioned into slices, each representing an allocated portion of an individual node’s disk and memory space. However, Redshift offers several node types, so you have much more control and flexibility over how you configure clusters. The standard version of AWS Redshift is not serverless, and storage and compute are tightly coupled. There's even an auto-complete feature where Snowflake automatically makes recommendations when you enter your script in the query editor. Snowflake takes advantage of Massive Parallel Processing (MPP) to process queries, and since the platform is based on ANSI SQL, the barrier to entry is really low. Everything from file sizing, compression, structure, metadata, statistics, and every other data object that is not directly visible to you is automatically handled by Snowflake. Snowflake stores a portion of every dataset locally within these clusters and uses micro partitions to optimize and compress all your data into a columnar storage format. Snowflake warehouses do not share compute resources with other virtual data warehouses, which means the platform provides near-unlimited query concurrency and robust computing power. Compute nodes and clusters within Snowflake are known as individual warehouses. You never have to manage or maintain any hardware, and storage and compute are completely decoupled to optimize for performance and query concurrency. Snowflake is entirely serverless, eliminating the need for dedicated resources. Like Snowflake, Redshift lets you query data using SQL for various analytics and engineering-related use cases. AWS Redshift was one of the first cloud data warehouses to become available on the market, officially launching in 2013. However, whereas Snowflake is a SaaS offering, Redshift is a PaaS (Platform-as-a-Service) solution. Redshift is also a traditional data warehouse designed to tackle Business Intelligence use cases, among other things. What is Redshift?įunny enough, Amazon Redshift was strategically named as a deliberate dig at Oracle since all of Oracle's branding is red. These workloads include data warehousing, data lakes, data engineering, application development, data sharing, and business intelligence. What is Snowflake?įounded in 2012, Snowflake is a SaaS-based data platform that can run on any of the major cloud providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP.) In its simplest form, Snowflake helps you consolidate and aggregate your data into a single, centralized platform to tackle analytics use cases. Cloud services and data warehouses have changed drastically over the years, and there are now quite a few options available in the market, but Amazon Redshift and Snowflake still dominate most of the market, and choosing between the two can be challenging since there are so many technical and architectural differences you need to consider.
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