Snowflake vs Rockset
Compare and contrast Snowflake and Rockset by architecture, ingestion, queries, performance, and scalability.
Snowflake vs Rockset Architecture
Snowflake is the data warehouse built for the cloud. Snowflake is well-known for separating storage and compute for better price performance. With Snowflake, multiple virtual warehouses can be spun up or down for batch data loading, transformations and queries all on the same shared data.
Rockset is built to be a cloud-only database and does not have a self-managed option. It disaggregates compute from both hot storage and cloud storage, allowing multiple isolated compute clusters to run on the same shared data.
Snowflake vs Rockset Ingestion
Snowflake is an immutable data warehouse that is built for batch ingestion and relies heavily on the modern data stack ecosystem for data connectors and transformations. Snowflake has a number of integrations to ETL and ELT solutions including Fivetran, Hevo, Striim and dbt. While Snowflake does have support for semi-structured data in the form of a VARIANT type, it is best to structure the data for optimal query performance.
Rockset has built-in connectors that manage streaming ingestion from common data sources. It has native support for semi-structured data, so that nested JSON and XML can be ingested and queried as is.
Snowflake vs Rockset Performance
Snowflake is designed for batch analytics with analysts and data scientists infrequently accessing large-scale data for trend analysis. Snowflake, like many data warehouses, is immutable and does not support frequently changing data efficiently. Snowflake uses a columnar store to return aggregations and metrics efficiently, often with query response times in the seconds to minutes on petabytes of data.
Rockset is designed to make streaming data queryable as quickly as possible by avoiding the need to batch data. It also updates documents efficiently by only reindexing fields that are part of an update request. Rockset indexes all data by default, which results in storage amplification but also enables low-latency queries that require less compute.
Snowflake vs Rockset Queries
Snowflake supports SQL as its native query language and can perform SQL joins. Snowflake for developers introduced a number of developer tools including SQL APIs, UDFs and drivers to support application development. As Snowflake was originally built for business intelligence workloads, it integrates with a number of visualization tools for trend analysis.
Rockset supports SQL as its native query language and can perform SQL joins. Users can create data APIs by storing SQL queries in Rockset that are executed from dedicated REST endpoints. Rockset integrates with some common visualization tools, but BI is not Rockset’s primary use case.
Snowflake vs Rockset Scalability
Snowflake virtual warehouses can be scaled up for faster queries or scaled out using multi-cluster warehouses to support higher concurrency workloads. Snowflake has shared blob storage that scales automatically and independently.
Rockset Virtual Instances are distributed compute clusters that can be scaled up for faster queries or scaled out for practically unlimited concurrency or if compute isolation is needed. Rockset has shared storage that scales automatically and independently, so no rebalancing is required.