Best Streaming Analytics Platforms for Amazon SageMaker Feature Store
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Compare the Top Streaming Analytics Platforms that integrate with Amazon SageMaker Feature Store as of November 2025
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This a list of Streaming Analytics platforms that integrate with Amazon SageMaker Feature Store. Use the filters on the left to add additional filters for products that have integrations with Amazon SageMaker Feature Store. View the products that work with Amazon SageMaker Feature Store in the table below.
What are Streaming Analytics Platforms for Amazon SageMaker Feature Store?
Streaming analytics platforms are software solutions that enable real-time processing and analysis of data as it is generated or streamed from various sources such as IoT devices, sensors, social media, and transactional systems. These platforms allow businesses to gain immediate insights from continuous data streams, enabling them to make faster decisions, detect anomalies, and optimize operations in real-time. Key features of streaming analytics platforms include data ingestion, real-time event processing, pattern recognition, and advanced analytics like predictive modeling and machine learning integration. They are commonly used in applications such as fraud detection, customer behavior analysis, network monitoring, and supply chain optimization. Compare and read user reviews of the best Streaming Analytics platforms for Amazon SageMaker Feature Store currently available using the table below. This list is updated regularly.
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Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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Amazon Kinesis
Amazon
Easily collect, process, and analyze video and data streams in real time. Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin. Amazon Kinesis enables you to ingest, buffer, and process streaming data in real-time, so you can derive insights in seconds or minutes instead of hours or days.
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