Rockset vs. Snowflake
for Real-Time Analytics

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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.

35%

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.

65%

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.

80%

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.

Whatnot logo
Rockset offers ultimate flexibility for us to quickly experiment and build features.

Xin Xia, Marketplace and Discovery

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Command Alkon logo
We absolutely love Rockset. It’s a game changer for us.

Doug Moore, VP of Cloud

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Compute and Storage
Snowflake only separates compute from storage.
Snowflake separates compute from storage, making it possible to run multiple workloads on a single dataset. This eliminates the need to overprovision storage.
Compute Costs
Snowflake is storage optimized
Snowflake transforms data into its compressed, columnar format, which minimizes storage footprint. Querying data in this format requires computationally intensive scans, making it expensive to run sub-second queries on fresh data.
Query Speed
Snowflake does full scans
As a columnar database, Snowflake has to scan through large amounts of data to run each query. This process is slow, especially as data size or query complexity grows. Plus, each query requires at least 100ms of start-up time.
Data Latency
Snowflake data is stale
Snowflake loads data in batches, resulting in a delay before new data can be queried. Its immutable columnar format uses copy-on-write, which is compute intensive. Snowpipe supports data latency with a p95 of one minute, but is costly to run.
Data Model
Snowflake has poor support for semi-structured data
While there is “native” support for JSON, users must specify type to query fields inside an object. Performance is poor and advanced features are not supported.

Resources



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Introducing Compute-Compute Separation for Real-Time Analytics

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Real-Time Data Ingestion: Snowflake, Snowpipe and Rockset

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Can BigQuery, Snowflake, and Redshift Handle Real-Time Data Analytics?

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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:

Converged Indexing™

Creation of search, columnar and row indexes at ingest time

Mutability

Efficient inserts, updates and deletes

Cloud-native architecture

Independent scaling of storage-compute and compute-compute

Continuous Ingest Transformations and Rollups

Pre-aggregate and transform data at ingest time, using SQL.