StarRocks vs Snowflake
Compare and contrast StarRocks and Snowflake by architecture, ingestion, queries, performance, and scalability.
StarRocks Architecture vs Snowflake
StarRocks is a high-performance OLAP database that can be deployed on the cloud or self managed. StarRocks does not separate compute and storage and offers limited options for resource isolation. It offers a robust set of features and high performance but requires considerable expertise to operate and scale.
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.
StarRocks Ingestion vs Snowflake
StarRocks ingests data from a variety of sources, including both batch and streaming data. StarRocks can ingest nested JSON data, but enforces type at the column level.
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.
StarRocks Queries vs Snowflake
StarRocks uses a high-performance vectorized SQL engine, a custom-built cost-based optimizer, and has support for materialized views.
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.
StarRocks Performance vs Snowflake
StarRocks was purpose-built for high-performance ingest, low-latency queries, and high concurrency. Optimized performance requires significant manual tuning.
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.
StarRocks Scalability vs Snowflake
StarRocks can scale up or out, but its tightly coupled compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling StarRocks often requires deep expertise as there are many levels of the system that need to be managed.
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.