However, there are definitely differences…. To choose the right solution for your company, you should, at the very least, compare integration, maintenance, and costs. Comparing Redshift and Snowflake: Integration I’ll do just that in the following sections, along with the side-by-side pros and cons of both the solutions. If your company is already working with AWS, then Redshift might seem like the natural choice. However, you can also find Snowflake on the AWS Marketplace with on-demand functions. If you’re already leveraging AWS services, Redshift can be integrated seamlessly. However, if you are going to use Snowflake, it’s important to note that it doesn’t have the same integrations as Redshift. This will make it challenging to integrate the data warehouse with tools such as AWS Athena. However, Snowflake makes up for this with a variety of integration options like Apache Spark. Comparing Redshift and Snowflake: Maintenance While Redshift is the more established solution, Snowflake has made some significant strides over the last few years. With Amazon’s Redshift, users compete over available resources in a cluster. This problem doesn’t exist with Snowflake since users can start different data warehouses (of various sizes) to look at the same data. When it comes to vacuuming and cleaning tables, Snowflake provides a turnkey solution. With Redshift, cleaning tables can become a problem as it can be challenging to scale up or down. Redshift Resize operations can also become expensive and lead to significant downtime. Since compute and storage are separate in Snowflake, users do not have to copy data to scale up or down but can switch data compute capacity at will. Comparing Redshift and Snowflake: Costsīoth Snowflake ETL and Redshift ETL have very different pricing models. So customers can calculate their monthly cost in a simple formula: Redshift calculates costs based on a per-hour per-node basis. Snowflake’s charges heavily depend on monthly usage patterns. Each bill is generated by the hour for each virtual data warehouse. My knowledge is mostly limited to AWS services.Storage costs on Snowflake can start at an average compressed amount at a flat rate per terabyte.īesides, data storage costs are also separate from computational costs. (My apologies for not including Google and other services as examples. If Amazon Athena is meeting your needs, then there is no need to move to anything else. The best approach is to start with something small until it no longer meets your needs. Of course, if your data needs are small, it is quite acceptable to use a traditional database (eg PostgreSQL) as a Data Warehouse. This has been addressed by the Delta Lake file format that uses a combination of Parquet files and logs files to allow data to be inserted, updated and deleted. The downside of using Query Engines is that they are not good at inserting/updating data. ![]() Data can be added by simply adding another file in the storage location.Įxamples of Query Engines are: Presto, Amazon Athena, Amazon Redshift Spectrum ![]() Rather, when a query runs, they go to the storage service, look at data stored in whatever format and then calculate the answer to the query. The main thing to understand about Query Engines is that data is not 'loaded' into them. Plus, the fact that they are cloud-native, it was easy to scale Compute as needed for short periods of time. This was not only a mind-blowing concept, but depending upon the data format (eg Snappy-compressed Parquet) could actually rival the speed of Data Warehouses. ![]() Optimized for querying, Presto could query data stored in cloud services (eg Amazon S3) without having to load the data into the database (known as a 'query engine'). The next evolution came from Presto (and can be traced back to Hadoop), which was the idea of completely separating the Compute and Storage components of databases. Examples are: Amazon Redshift, Snowflake. These systems typically use parallel infrastructure and columnar storage split across multiple storage nodes to provide fast performance. This led to a new class of Data Warehouse systems that specialise in querying tables with billions of rows and Terabytes of data. There was no capability to separate these two components because the database stored its data in a proprietary format and the Compute component was required access that data.Īs data volumes increased, traditional databases struggled to provide fast performance. Traditionally, this meant that a Database was required, and all databases (at the time) consisted of both Storage and Compute. Reporting systems need a means of running SQL against data.
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