Comparisons

VeloDB vs Snowflake

VeloDB is designed for real-time data analytics with strong scalability. Snowflake is a cloud-based data warehouse and analytics platform. When it comes to real-time analytics, VeloDB shows higher concurrency, faster queries at a fraction of the cost.

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SecondsMintues

Real-Time Data Latency

4-5x

Faster Queries

10x

Higher Concurrency

3-5x

Cost Efficiency

Why Choose VeloDB

	VeloDB	Snowflake
Deployment	Supports three deployment options: Cloud-native SaaS service on AWS, Azure, and GCP Cloud-native BYOC service on AWS, Azure, and GCP On-premise deployment with enterprise-grade reliability	Supports only Cloud SaaS
Real-Time Updates	High-throughput real-time data updates reach millions of records per second Consume data from sources like Flink, Kafka, and APIs in real-time, with data visibility in seconds	Not ideal for frequent data updates Batch data ingestion
Data API	Supports Arrow Flight for high-speed data reading	Only supports low-speed data reading via JDBC, ODBC
Rich Indexes	Skip Index: Minmax Index, BloomFilter Index Point Query Index: Prefix Index, Inverted Index	Only supports skip index (Minmax Index, BloomFilter Index)
Materialized View	Supports synchronous materialize view, real-time data refreshing Supports asynchronous materialized view for multi-table	No support real-time data refreshing No support for asynchronous materialized view
Use Cases	Real-Time Analytics Data Warehouse and Lakehouse Logging and Observability	Supports data warehouse and lakehouse, yet not ideal for real-time analytics

VeloDB

Deployment
Supports three deployment options:

Cloud-native SaaS service on AWS, Azure, and GCP
Cloud-native BYOC service on AWS, Azure, and GCP
On-premise deployment with enterprise-grade reliability
Real-Time Updates

High-throughput real-time data updates reach millions of records per second
Consume data from sources like Flink, Kafka, and APIs in real-time, with data visibility in seconds
Data API

Supports Arrow Flight for high-speed data reading
Rich Indexes

Skip Index: Minmax Index, BloomFilter Index
Point Query Index: Prefix Index, Inverted Index
Materialized View

Supports synchronous materialize view, real-time data refreshing
Supports asynchronous materialized view for multi-table
Use Cases

Real-Time Analytics
Data Warehouse and Lakehouse
Logging and Observability

Snowflake

Deployment

Supports only Cloud SaaS
Real-Time Updates

Not ideal for frequent data updates
Batch data ingestion
Data API

Only supports low-speed data reading via JDBC, ODBC
Rich Indexes

Only supports skip index (Minmax Index, BloomFilter Index)
Materialized View

No support real-time data refreshing
No support for asynchronous materialized view
Use Cases

Supports data warehouse and lakehouse, yet not ideal for real-time analytics

By making the switch to VeloDB for our real-time analytics platform, we've seen query speeds boost 3-10x faster and costs dropped nearly 50% compared to Snowflake. Plus, VeloDB's strong support for diverse analytical workloads—like complex searches, multi-table joins, and a variety of aggregations—makes it ideal for analytics that need to be both swift and adaptable.

Performance Comparison

TPC-H SF100 Benchmark

The TPC-H benchmark with a scale factor of 100 (SF100) is a widely used standard for evaluating database performance. It includes a set of complex SQL queries designed to simulate real-world business intelligence workloads.

For comparison of query performance, models with similar costs were selected:

Snowflake Standard (Gen2),Large, Enterprise
VeloDB Cloud with 192C configuration

TPC-DS SF1000 Benchmark

The TPC-DS SF1000 Benchmark evaluates data warehouse performance using a 1TB dataset with 6.35 billion records across 24 tables.

It includes 99 complex queries to test joins, aggregations, and subqueries. Based on a snowflake schema, it simulates real-world sales scenarios. The 1TB scale is challenging due to query complexity.

ClickBench

ClickBench is a benchmarking tool to evaluate the performance of analytical databases. It focuses on testing the performance of large, flat tables rather than complex multi-table joins. It uses real-world data from a major web analytics platform, covering typical scenarios such as clickstream analysis and structured logs.