What is Apache Flink?
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Apache Flink is a tool in the Big Data Tools category of a tech stack.
Apache Flink is an open source tool with 9.1K GitHub stars and 4.8K GitHub forks. Here’s a link to Apache Flink's open source repository on GitHub
Who uses Apache Flink?
20 companies use Apache Flink in their tech stacks, including Zalando, sovrn Holdings, and BetterCloud.
21 developers use Apache Flink.
Apache Flink Integrations
Kafka, Hadoop, HBase, Apache Zeppelin, and YARN Hadoop are some of the popular tools that integrate with Apache Flink. Here's a list of all 5 tools that integrate with Apache Flink.
Why developers like Apache Flink?
Here’s a list of reasons why companies and developers use Apache Flink
Apache Flink's features
- Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
- Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
- Flexible and expressive windowing semantics for data stream programs
- Built-in program optimizer that chooses the proper runtime operations for each program
- Custom type analysis and serialization stack for high performance
Apache Flink Alternatives & Comparisons
What are some alternatives to Apache Flink?
See all alternatives
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.