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DirectoryData & AnalyticsStreaming & Real-Time AnalyticsPravega

Pravega

Open Source

About Pravega

Pravega provides a storage abstraction called a stream for continuously generated and unbounded data. A Pravega stream stores unbounded parallel sequences of bytes in a durable, elastic and consistent manner while tiering data to scale-out storage. It provides a cloud-native streaming infrastructure. Pravega streams natively support long-term data retention. It handles both small events as in IoT and larger data such as video. Pravega also enables replicating application state and storing key-value pairs.

Used for

  • Detecting predictive maintenance needs for IoT devices such as wind turbines
  • Processing billable events for real-time billing
  • Analyzing streaming data for real-time cybersecurity threat detection
  • Using Pravega Transactions to ensure a set of events are written to a stream atomically
  • Building pipelines of data processing combining batch, real-time and other applications

From Pravega'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 Pravega. 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

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

cncf.pravega.io
CategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics

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