4. Kafka String Consumer. To consume data from Kafka with Flink we need to provide a topic and a Kafka address. We should also provide a group id which will be used to hold offsets so we won't always read the whole data from the beginning.
Click to see full answer
How is Flink different from Kafka?The biggest difference between the two systems with respect to distributed coordination is that Flink has a dedicated master node for coordination, while the Streams API relies on the Kafka broker for distributed coordination and fault tolerance, via the Kafka's consumer group protocol.
Where can I use Flink?
Below, we explore the most common types of applications that are powered by Flink and give pointers to real-world examples.
- Event-driven Applications.
- Data Analytics Applications.
- Data Pipeline Applications.
What industries use Flink?
Companies Currently Using Apache Flink
|Company Name||Website||Sub Level Industry|
|Apple||apple.com||General Interconnection Products & Services|
|Huntington Ingalls Industries||huntingtoningalls.com||Aerospace & Defense|
|Dell||delltechnologies.com||Computer Hardware Manufacturers|
|Shopify||shopify.com||Diversified Technology, Products & Services|
Is Flink better than Kafka?
Flink has a richer API when compared to Kafka Stream and supports batch processing, complex event processing (CEP), FlinkML, and Gelly (for graph processing).
Flink is a distributed processing engine and a scalable data analytics framework. You can use Flink to process data streams at a large scale and to deliver real-time analytical insights about your processed data with your streaming application.
Flink use cases include fraud detection, network monitoring, alert triggering, and other solutions to enhance user experience. A large variety of enterprises choose Flink as a stream processing platform due to its ability to handle scale, stateful stream processing, and event time.
The Streams API in Kafka is a library that can be embedded inside any standard Java application.
Flink vs Kafka Streams API: Major Differences.
|Apache Flink||Kafka Streams API|
|Bounded and unbounded data streams||Unbounded and Bounded||Unbounded|
Latency – No doubt Flink is much faster due to it's architecture and cluster deployment mechanism, Flink throughput in the order of tens of millions of events per second in moderate clusters, sub-second latency that can be as low as few 10s of milliseconds.
Flink is very fast and has very low latency, much better than Spark. It also has a robust fault tolerance, apps can restart exactly from the same point where they fail with exactly once deliver semantics.
Flink is a framework and distributed processing engine for batch and stream data processing. Its structure enables it to process a finite amount of data and infinite streams of data. Flink provides data source/sink connectors to read data from and write data to external systems like Kafka, HDFS, Cassandra etc.
Apache Flink is a stream processing framework that can be used easily with Java. Apache Kafka is a distributed stream processing system supporting high fault-tolerance.