Best Streaming Analytics Platforms for StarTree

Compare the Top Streaming Analytics Platforms that integrate with StarTree as of December 2025

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This a list of Streaming Analytics platforms that integrate with StarTree. Use the filters on the left to add additional filters for products that have integrations with StarTree. View the products that work with StarTree in the table below.

What are Streaming Analytics Platforms for StarTree?

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 StarTree currently available using the table below. This list is updated regularly.

1

Confluent

Confluent

Infinite retention for Apache Kafka® with Confluent. Be infrastructure-enabled, not infrastructure-restricted Legacy technologies require you to choose between being real-time or highly-scalable. Event streaming enables you to innovate and win - by being both real-time and highly-scalable. Ever wonder how your rideshare app analyzes massive amounts of data from multiple sources to calculate real-time ETA? Ever wonder how your credit card company analyzes millions of credit card transactions across the globe and sends fraud notifications in real-time? The answer is event streaming. Move to microservices. Enable your hybrid strategy through a persistent bridge to cloud. Break down silos to demonstrate compliance. Gain real-time, persistent event transport. The list is endless.

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2

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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3

Redpanda

Redpanda Data

Breakthrough data streaming capabilities that let you deliver customer experiences never before possible. Kafka API and ecosystem are compatible. Redpanda BulletPredictable low latencies with zero data loss. Redpanda BulletUpto 10x faster than Kafka. Redpanda BulletEnterprise-grade support and hotfixes. Redpanda BulletAutomated backups to S3/GCS. Redpanda Bullet100% freedom from routine Kafka operations. Redpanda BulletSupport for AWS and GCP. Redpanda was designed from the ground up to be easily installed to get streaming up and running quickly. After you see its power, put Redpanda to the test in production. Use the more advanced Redpanda features. We manage provisioning, monitoring, and upgrades. Without any access to your cloud credentials. Sensitive data never leaves your environment. Provisioned, operated, and maintained for you. Configurable instance types. Expand cluster as your needs grow.

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4

Apache Flink

Apache Software Foundation

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Apache Flink excels at processing unbounded and bounded data sets. Precise control of time and state enable Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed sized data sets, yielding excellent performance. Flink is designed to work well each of the previously listed resource managers.

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