VeloDB vs Redshift

Supports multiple deployment options on multi-cloud

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  SaaS and BYOC service on AWS, Azure, and GCP
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  On-premise deployment with enterprise-grade reliability

• Supports only SaaS service on AWS

Multi-compute, shared-data architecture

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  Compute and storage are fully decoupled
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  Enables resource isolation at the compute group level, and further through workload partitioning within those groups

• Lacks multi-cluster shared storage; resource isolation is only achievable via workload partitioning within a single compute group

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  High-throughput real-time data updates reach millions of records per second
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  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

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  Supports Arrow Flight for high-speed data reading

• Only supports low-speed data reading via JDBC, ODBC

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  Supports high concurrency for various analytical workloads
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  Uses hybrid row-column storage for efficient point queries

• Relies on columnar storage, which limits high-concurrency queries.

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

• Only supports skip index (Minmax Index, BloomFilter Index)

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  Cluster scaling (scale-out/in) does not impact ongoing business operations

• Cluster scaling (scale-out/in) causes downtime of minutes

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  Real-Time Analytics
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  Data Warehouse and Lakehouse
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  Logging and Observability

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