Siddhi
About Siddhi
Siddhi can run as an embedded Java or Python library, as a microservice, or natively in Kubernetes. It integrates with messaging systems, databases, services, file systems, and email.
Used for
- Retrieving and publishing data from various enterprise systems.
- Calculating aggregations over time, length, and session windows.
- Generating alerts based on static and dynamic thresholds.
- Processing static rules via predefined and database based rules.
- Analyzing event occurrence patterns and trends over time.
From Siddhi's own website, read 2026-10-24. How we build listings.
How to evaluate Streaming & Real-Time Analytics
This is how the CIOPages Research Team evaluates this category. It is not an assessment of Siddhi. It comes from our Streaming Data & Event Processing buyer guide.
25%
Throughput, Latency & Durability
Sustained throughput and tail latency at your partition count, ordering and exactly-once / at-least-once delivery guarantees, replication and multi-AZ durability, retention limits, and behavior under broker failure and rebalancing
20%
Operating Model & Architecture
Self-managed vs. fully managed vs. serverless vs. bring-your-own-cloud, KRaft (post-ZooKeeper) operations, diskless / object-storage-backed options, autoscaling, partition rebalancing, and upgrade and patching burden on your team
20%
Kafka-API Compatibility & Portability
Fidelity of the Kafka protocol (consumer groups, transactions, compaction), reuse of existing clients, connectors and tooling, lock-in to a single cloud, and the realistic effort to migrate in or out
15%
Stream Processing & Ecosystem
Native or integrated processing (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB), connector catalog (CDC, sinks, sources), schema registry and governance, and direct landing into Iceberg / Delta lakehouse tables
10%
Security, Governance & Compliance
Encryption in transit and at rest, mTLS / SASL / OAuth and RBAC, audit logging, private networking, data residency and BYOC data-boundary control, and SOC 2 / ISO 27001 / GDPR coverage
10%
Cost Model & Economics at Scale
Pricing unit (capacity / throughput / consumption), inter-AZ and egress charges, storage and retention cost, processing billed separately, and how the bill behaves as volume, partitions, and connectors grow
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Quick Facts
siddhi.ioCategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics
DeploymentKubernetes, Docker, On-premises/bare metal, Embedded (Java/Python library)
PricingOpen source
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