Spark Streaming
Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
Spark Streaming Alternatives
The best Spark Streaming alternatives based on verified products, community votes, reviews and other factors.
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Confluent offers a real-time data platform built around Apache Kafka.
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Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
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The only 8-in-1 AI content detector platform in the world. We integrate with leading AI content detectors to give unparalleled confidence that your content appear to be written by a human.
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Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
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Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
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Leo enables teams to innovate faster by providing visibility and control for data streams.
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Striim provides an end-to-end, real-time data integration and streaming analytics platform.
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Discover our high quality range of over 40 interchangeable camera lenses including A-mount and E-mount lenses crafted for a range of shooting situations.
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Your data. Where you need it. HVR is the leading independent real-time data replication solution that offers efficient data integration for cloud and more.
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The StreamSets Data Collector (SDC) is used to build, test and execute dataflow pipelines for data lake and multi-cloud data movement plus cybersecurity, IoT and customer 360 applications.
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Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
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Spring Cloud Data Flow is a platform capable of stream and batch data pipelines having the tools to create delicate topologies.
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Analyze, continuously query, and act on IoT and other streaming data at lightning fast speeds. Take real-time operations and analytics to the next level with...
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Learn how Informatica's intelligent data security TDM solution allows automated provisioning of masked and synthetically generated data to meet the needs of test, development, and QA teams.