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Snowflake vs Rockset

Compare and contrast Snowflake and Rockset by architecture, ingestion, queries, performance, and scalability.

Snowflake vs Rockset Architecture

Architecture
Snowflake
Rockset
Deployment model
SaaS - infrastructure, software and cluster ops managed by service provider
SaaS - infrastructure, software and cluster ops managed by service provider
Use of storage hierarchy
Cloud object storage for shared data accessible from any virtual warehouse
• SSDs store shared hot data, accessible from any Virtual Instance cluster • Cloud storage for durability
Isolation of ingest and query
Yes - separate virtual warehouses for batch data loading, ELT jobs and queries
Yes - separate compute clusters (Virtual Instances) for ingest and query
Separation of compute and storage
Yes
Yes
Isolation for multiple applications
Yes - separate virtual warehouses for each workload
Yes - separate compute cluster (Virtual Instance) for each application

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

Ingestion
Snowflake
Rockset
Data sources
• Third party ETL tool to ingest data into Snowflake including Fivetran, Hevo or Striim • Bulk loading from S3, GCS, Azure Blob Storage • Sink Connector for Apache Kafka in Confluent Cloud
Managed data connectors to: • Events streams (e.g. Kafka, Kinesis) • Database CDC (e.g. MongoDB, DynamoDB, MySQL, PostgreSQL) • Data lakes (e.g. S3, Google Cloud Storage)
Semi structured data
Ingests JSON and XML as a VARIANT data type
• Ingests JSON and XML without a predefined schema • Ingests nested data
Transformations and rollups
• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts
Yes - using SQL ingest transformations

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

Performance
Snowflake
Rockset
Updates
Data warehouse with immutable storage. Updates rewrite and merge entire partitions
Documents are mutable and can be updated at the field level
Indexing
No
• Converged Index (row, columnar and inverted index) built on all data by default • All queries are resolved through the index
Query latency
Seconds to minutes on petabytes of data
50-1000ms queries on 100s of TB
Storage format
Compressed columnar format stored in cloud object storage
Converged Index, comprising a rowstore, columnstore and inverted index
Streaming ingest
• Ingests on a batch basis • Snowpipe typically ingests in minutes
• Ingests on a per-record basis • Data latency is typically 1-2 seconds

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

Queries
Snowflake
Rockset
Joins
Yes
Yes
Query language
SQL
SQL - with extensions for nested data
Developer tooling
• SQL APIs - make SQL calls to Snowflake programmatically • UDFs for Javascript, Python, Java and SQL functions • Go, JDBC, .NET, Node.js, ODBC, PHP, Python drivers
• Data APIs - saved SQL queries executed via REST endpoint • Python, Java, Node.js and Go SDKs • UDFs
Visualization tools
Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more
Integrations with Tableau, Looker, Grafana, Superset, Power BI, Retoolwhic

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

Scalability
Snowflake
Rockset
Vertical scaling
Resize virtual warehouses via web interface or using DDL commands for warehouses
Resize compute clusters (Virtual Instances) via API or in the console
Horizontal scaling
• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set
• Add or remove compute clusters (Virtual Instances) via API or in the console • Scale out compute clusters for higher concurrency • Use separate compute clusters to isolate ingest from query or for multiple isolated applications • No rebalancing required

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.