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Alternatives to Apache Flink

If you are looking for alternatives to Apache Flink, our experts have your back. Here are some of the top technologies available for real-time data processing and stream analytics.

Apache Spark Streaming

Apache Spark is a leading big data processing framework. It has a unified analytics engine that caters to both batch and streaming needs. It is popular for its high performance driven by advanced components like the DAG scheduler and query optimizer.

Furthermore, Spark facilitates rapid insight extraction from large datasets. With over 80 high-level operators, the framework offers versatility in creating parallel applications effortlessly.

Apache Storm

Apache Storm is a freely available open-source distributed real-time computing system. It simplifies the processing of unbounded streams and data in a reliable manner, similar to Hadoop.

Additionally, it can be seamlessly integrated with any programming language. This versatile tool finds applications in various domains, including real-time analytics and online machine learning.

Kafka Streams

Kafka Streams is a key component of the Apache Kafka ecosystem. It acts as a stream processing library helping developers to construct applications that seamlessly process data directly within Kafka.

Hence, Kafka Streams is the best choice for cases where data is already stored in Kafka topics, and real-time processing is essential within the Kafka ecosystem.

Amazon Kinesis Data Analytics

Amazon Kinesis makes collecting, processing, and analyzing video and data streams an easy task. It lets us process streaming data at any scale cost-effectively. Additionally, we can select the tools that best fit our application’s requirements.

Google Cloud Dataflow

Google Cloud Dataflow stands out as a fully managed data processing service. It caters to both stream and batch processing needs. With support for multiple programming languages, it presents a user-friendly platform for designing and deploying data processing workflows.

Google Cloud Dataflow’s fully managed service eliminates the need for infrastructure provisioning, scaling, and server maintenance. Additionally, its tools ensure efficient pipeline management.

StreamSets Data Collector

StreamSets Data Collector is a powerful open-source tool for designing and executing data integration pipelines. Also, it simplifies the process of ingesting, processing, and delivering data across various sources and destinations.

With a drag-and-drop interface, users can create data flows without extensive coding. Additionally, StreamSets supports real-time and batch data processing. Overall, StreamSets Data Collector lets organizations efficiently manage their data integration tasks.

Confluent Platform (Kafka Streams + ksqlDB)

Built on Apache Kafka, the Confluent Platform is an event streaming platform made up of Kafka Streams for stream processing and ksqlDB for interactive SQL-based stream processing. Furthermore, it helps with constructing real-time data pipelines and developing event-driven applications.

Apache Gobblin

Apache Gobblin is an open-source distributed data integration framework. It simplifies data integration tasks like ingestion, replication, and lifecycle management in large-scale data environments. Additionally, we can use it for streaming and batch data ecosystems.

Compatible with the Hadoop ecosystem, Gobblin can run as a standalone program, support embedded mode, and operate as a MapReduce application across multiple Hadoop versions.

KsqlDB

KsqlDB is made on Apache Kafka and Kafka Streams. It seamlessly integrates potent stream processing capabilities with a relational database model via SQL syntax. Hence, KsqlDB is a powerful tool for creating real-time applications that demand the quick processing and analysis of streaming data.

Let us know which one of the above alternatives worked well in your favor.

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Conclusion

In brief, our Support Experts introduced us to different alternatives to Apache Flink.