Aiven for Apache Flink
Fully managed Apache Flink service by Aiven.
Apache Flink
Unified engine for high-throughput, low-latency stream and batch processing
Apache Flink delivers a streaming-first runtime that handles both real-time and batch workloads with exactly-once guarantees, flexible windowing, and native integration with Hadoop ecosystem components.
Overview
Overview
Apache Flink is a streaming-first framework that also supports batch workloads, providing a single runtime for diverse data processing needs. It offers high throughput while maintaining low event latency, and guarantees exactly-once fault tolerance through checkpointing and natural back‑pressure handling.
Deployment & Integration
Flink runs on Unix‑like systems and integrates seamlessly with the Hadoop ecosystem—YARN, HDFS, HBase, and more. Developers can use fluent Java APIs (or Scala) and leverage built‑in libraries for graph processing, machine learning, and complex event processing. The project ships with externalized connectors for Kafka, JDBC, Elasticsearch, and many other sources, making it adaptable to modern data pipelines.
Audience
Ideal for engineers building real‑time analytics, ETL pipelines, or stateful stream applications that require precise time semantics and robust scalability.
Highlights
Streaming-first runtime with unified batch support
Exactly-once fault tolerance and natural back-pressure
Rich APIs and libraries for graph, ML, and CEP
Seamless integration with Hadoop ecosystem (YARN, HDFS, HBase)
Pros
- High throughput with low latency
- Event-time and out-of-order processing
- Flexible windowing and custom triggers
- Strong ecosystem and connector library
Considerations
- Steep learning curve for advanced APIs
- Requires Java/Scala expertise
- Resource-intensive for small workloads
- Operational complexity in large clusters
Managed products teams compare with
When teams consider Apache Flink, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Real-time analytics on high-velocity data streams
- Batch ETL jobs needing low-latency results
- Complex event processing with stateful operators
- Organizations already using Hadoop/YARN
Not ideal when
- Simple CRUD applications without streaming needs
- Environments lacking Java 11+ runtime
- Teams preferring low-maintenance serverless services
- Very small datasets where overhead outweighs benefits
How teams use it
Fraud detection in financial transactions
Detect anomalies within seconds using event-time windows and stateful processing.
Clickstream analysis for e‑commerce
Aggregate user actions in real time to power personalized recommendations.
Batch log processing
Generate daily reports from massive log files with exactly-once guarantees.
IoT sensor data aggregation
Combine time-based windows across millions of devices with low latency.
Tech snapshot
Java87%
Scala8%
Python3%
Shell1%
TypeScript1%
HiveQL1%
Frequently asked questions
Which programming languages are supported?
Primary APIs are available in Java and Scala; Python is supported via PyFlink.
Can Flink run on existing Hadoop clusters?
Yes, Flink integrates with YARN, HDFS, HBase, and other Hadoop components.
What fault‑tolerance guarantees does Flink provide?
Flink offers exactly-once processing guarantees through distributed snapshots.
Is there support for out-of-order event handling?
Flink’s DataStream API supports event-time semantics and out-of-order processing.
How are connectors managed?
Most connectors are externalized into separate Apache projects, such as Kafka, JDBC, and Elasticsearch.
Project at a glance
Active- Stars
- 25,725
- Watchers
- 25,725
- Forks
- 13,836
LicenseApache-2.0
Repo age11 years old
Last commit2 days ago
Primary languageJava
Last synced 2 days ago