Apache Flink
Apache Flink is an open-source stream processing framework that performs stateful computations over unbounded and bounded data streams. It offers high throughput, low latency, accurate results, and fault tolerance.
opensource
stream-processing
realtime
distributed
scalable
Apache Flink: Open-Source Stream Processing Framework
Apache Flink is an open-source stream processing framework that performs stateful computations over unbounded and bounded data streams. It offers high throughput, low latency, accurate results, and fault tolerance.
What is Apache Flink?
Apache Flink is an open-source stream processing framework developed by the Apache Software Foundation. It is designed to perform high-throughput and low-latency data processing over unbounded and bounded data streams.
Some key capabilities and features of Apache Flink include:
- Stateful stream processing - Flink maintains state across stream events and windows, allowing it to perform sophisticated streaming analytics.
- Event time processing - Flink includes special support for event time semantics when processing streams.
- Fault tolerance - Flink uses checkpointing and distributed data storage to provide accurate results even in the event of failures.
- SQL support - Flink includes a SQL interface for querying streaming data.
- Python/Java/Scala APIs - Flink provides APIs in multiple languages for developing streaming applications.
- High performance - Flink is designed to run distributed across clusters while maintaining high throughput and low latency.
Use cases for Apache Flink include real-time analytics, data pipelines, event-driven applications, and more. It handles out-of-order data streams and lets you process historic, archived streams along with real-time streams.
Apache Flink Features
Features
- Distributed stream data processing
- Event time and out-of-order stream processing
- Fault tolerance with checkpointing and exactly-once semantics
- High throughput and low latency
- SQL support
- Python, Java, Scala APIs
- Integration with Kubernetes
Pricing
- Open Source
- Pay-As-You-Go
Pros
High performance and scalability
Flexible deployment options
Fault tolerance
Exactly-once event processing semantics
Rich APIs for Java, Python, SQL
Can process bounded and unbounded data streams
Cons
Steep learning curve
Less out-of-the-box machine learning capabilities than Spark
Requires more infrastructure management than fully managed services
Official Links
The Best Apache Flink Alternatives
Top Development and Big Data & Analytics and other similar apps like Apache Flink
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