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Apache Storm – distributed realtime computation system.Apache Samza – A distributed stream processing framework.Apache Apex – Enterprise-grade unified stream and batch processing engine.It executes pipelines on multiple execution environments. Apache Beam – a workflow manager for batch and streaming data processing jobs that run on any execution engine.It provides a fault tolerant operator based model for streaming and computation rather than the micro-batch model of Apache Spark. Apache Flink – considered one of the best Apache Spark alternatives, Apache Flink is an open source platform for stream as well as the batch processing at scale.Solace – A complete event streaming and management platform for the real-time enterprise.Īnd here are five open source alternatives to Spark Streaming:.
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StreamSets – Brings continuous data to every part of your business, delivering speed, flexibility, resilience and reliability to analytics.Lenses – The data streaming platform that simplifies your streams with Kafka and Kubernetes.Spring Cloud Data Flow – Tools to create complex topologies for streaming and batch data pipelines.Striim – a streaming data integration and operational intelligence platform designed to enable continuous query and processing and streaming analytics.IBM Event Streams – A high-throughput, fault-tolerant, event streaming platform.Aiven for Apache Kafka – A fully managed streaming platform, deployable in the cloud of your choice.Confluent – The leading streaming data platform.Google Cloud Dataflow – a fully-managed service for transforming and enriching streaming and batch data.See also Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis – Collect, process, and analyze real-time, streaming data such as video, audio, application logs, website clickstreams, and IoT telemetry.With that in mind let’s look at ten closed-source alternatives to Spark Streaming first: Are you processing streaming data or batch data? Do you prefer an open or closed source/proprietary alternative? Do you need SQL support? When researching Spark alternatives it really depends on your use case.