Volt Active Data
Relational databases
Big data processing and distribution systems
Event stream processing software
Stream analytics software
Database software
Big data software
Data integration tools
Cloud data integration software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Volt Active Data
Volt Active Data is an in-memory, distributed relational database designed for high-throughput transactional workloads that also need real-time analytics. It targets teams building event-driven applications such as fraud detection, personalization, telecom/network functions, and operational analytics where low latency and continuous ingestion matter. The product combines SQL/ACID transactions with streaming ingestion and built-in features for running computations close to incoming data. It is commonly deployed in clustered environments and can be run in cloud or self-managed infrastructure depending on the edition and architecture.
Low-latency transactional processing
The platform is built around an in-memory, distributed architecture intended to keep transaction latency low under high concurrency. It supports SQL and ACID semantics, which helps teams implement transactional logic without moving to a non-relational model. This fits use cases where operational systems must respond in milliseconds while continuously ingesting events. It can reduce the need to offload hot data to separate systems for immediate decisioning.
Streaming ingestion and analytics
Volt Active Data is positioned to ingest event streams and apply computations in near real time, aligning with stream processing and stream analytics use cases. This can simplify architectures that otherwise require separate components for ingestion, processing, and serving. For operational analytics, it enables querying recent/high-velocity data with SQL while it is still in motion. This is useful for alerting, scoring, and real-time dashboards tied to transactional activity.
Distributed scale-out architecture
The database is designed to run as a cluster and scale horizontally by adding nodes, which supports growth in throughput and dataset size. It includes mechanisms for partitioning and distributing data/processing across the cluster to handle high event volumes. This can be advantageous for workloads that outgrow single-node relational deployments. It also supports high availability patterns typical of distributed databases.
Operational complexity at scale
Running a distributed, in-memory database cluster typically introduces more operational considerations than managed single-instance relational services. Teams may need to plan for capacity, memory sizing, partitioning strategy, and failure handling to meet latency goals. Observability and performance tuning can be more specialized than with general-purpose relational deployments. Organizations without strong SRE/DBA support may face a steeper path to stable production operations.
Not a general-purpose warehouse
Although it supports analytics on operational data, it is not primarily designed for large-scale historical analytics, long-running ad hoc queries, or broad BI warehousing patterns. Workloads that require deep storage, heavy batch processing, or complex multi-hour queries may fit better in systems optimized for analytical storage and query planning. Teams may still need a separate analytical platform for long-term retention and broad reporting. This can add data movement and governance requirements.
Ecosystem and portability tradeoffs
Specialized capabilities for streaming and in-memory performance can create tighter coupling to the product’s operational model and deployment patterns. Migrating from conventional relational engines may require rethinking schema design, partitioning, and application interaction patterns to achieve expected performance. Integration breadth (connectors, managed service options, and third-party tooling coverage) may vary by environment and edition. This can affect time-to-value for teams expecting drop-in replacement behavior.
Plan & Pricing
Pricing model: Pay-as-you-go / consumption-based (event-based pricing for streaming decisions)
Free tier/trial: Developer Edition — free for local/development use (limited-duration license); Stream-to-Decision Trial — guided, consumption-free trial that only charges when you go live.
Example costs:
- Volt on AWS (historical/official example): $2.85 per hour (Volt Active Data Enterprise Edition on AWS; blog post, Aug 2018).
- Volt announced an "event-based" pricing model for "Volt for Streaming Decisions" (no per-event public unit price listed on site).
Discount options / notes:
- No public list prices for standard enterprise tiers on the vendor site; the vendor promotes consumption/event-based pricing and asks customers to contact sales or use guided evaluation for production pricing.
- Partner/channel program references "preferred pricing and exclusive discounts" for partners.
Key official pages used: voltactivedata.com get-started / developer-edition pages, press releases and blog posts that reference the AWS hourly example and event-based pricing model.
Seller details
Volt Active Data, Inc.
Billerica, Massachusetts, United States
2013
Private
https://www.voltactivedata.com/
https://x.com/VoltDB
https://www.linkedin.com/company/volt-active-data
