Snowflake was the first data warehouse designed for the cloud with compute-storage separation. Snowflake was designed for batch analytics supporting business intelligence and data science use cases, not the speed and scale of real-time analytical applications. Rockset delivers millisecond-latency analytics on real-time, streaming data in the cloud. Rockset redefines what it means to be cloud-native with both compute-storage and compute-compute for real-time analytics.
Faster Query Performance
Rockset delivers sub-second query latency by automatically indexing all your data in multiple ways. Snowflake relies on time-consuming scans and has significant query planning overhead that adds 100ms of latency to each query.
Lower Cost Per Query
For low latency, high concurrency data apps, cost per query matters more than cost per GB as your app scales. Rockset enables compute-efficient analytics by using indexes instead of scans.
Lower Data Latency
Snowflake loads data in “micro-batches” via Snowpipe, making it available to users within minutes. Rockset has native, built-in data connectors that ensure data can be queried within 2 seconds of being generated.
Solutions For Slow Snowflake Query Performance
Diagnosing Slow Snowflake Query Performance
Introducing Compute-Compute Separation for Real-Time Analytics
Space-Time Tradeoff: Examining Snowflake's Compute Cost
Real-Time Data Ingestion: Snowflake, Snowpipe and Rockset
Can BigQuery, Snowflake, and Redshift Handle Real-Time Data Analytics?
Rockset is purpose built for real-time analytics in the cloud, offering unparalleled speed and efficiency.
Here are four reasons to use Rockset for real-time analytics:
Creation of search, columnar and row indexes at ingest time
Efficient inserts, updates and deletes
Independent scaling of storage-compute and compute-compute
Continuous Ingest Transformations and Rollups
Pre-aggregate and transform data at ingest time, using SQL.