Oracle Stream Analytics Alternatives

Event Stream Processing

Considering alternatives to Oracle Stream Analytics? See what this market Oracle Stream Analytics users also considered in their purchasing decision. When evaluating different solutions, potential buyers compare competencies in categories such as evaluation and contracting, integration and deployment, service and support, and specific product capabilities.

Check out real reviews verified by Gartner to see how Oracle Stream Analytics compares to its competitors and find the best software or service for your organization.

Reviewed in Last 12 Months

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1. Amazon Kinesis Data Analytics

By Amazon Web Services (AWS)

4.4

My experience with Amazon Kinesis Data Analystics has been really good and positive, thanks to the community of amazon that help me with the setup Read all insights and reviews for Amazon Kinesis Data Analytics

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2. Confluent Platform

4.5

It offers a leading-enterprise-grade event streaming platform built around the industry's de-facto standard Apache Kafka. It offers a robust set of tools for real-time data streaming, integration, and processing at scale, and in the latest versions it has incorporated data quality and data contracts with a rule engine to ease the data quality processes.Read all insights and reviews for Confluent Platform

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3. Google Cloud Dataflow

4.6

Google Cloud Dataflow offers a scalable and cost-efficient way to build data pipelines and run them in real-time, and also offers data processing. The platform automates data cluster management and data infrastructure provisioning. It is a great tool for work rebalancing and data autoscaling.Read all insights and reviews for Google Cloud Dataflow

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4. Apache Storm

By Apache Software Foundation

4.5

Apache Storm is an one of the best opensource, real-time processing and distributed computation system available. It comes with best fault tolerant and stateless in nature, increases the performance.Read all insights and reviews for Apache Storm

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5. Cribl Stream

4.6

I handled the PoC and implementation on my own, and it was smooth process. The platform is very efficient and I really appreciate that we can perform upgrades and push configurations with zero downtime. It gives us control over data pipeline without so much of hurdles. Read all insights and reviews for Cribl Stream

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6. Azure Stream Analytics

4.3

A powerful and versatile service that stands out a robust solution for real time data analysis and event processing. Provides seamless data integration which can ingest data from variety of sources, including IoT devices, social media, sensors, and applications. Its real time data processing ensures that businesses can make quick and informed decisions based on fresh and actionable insights. Its scalability and integration allows organization to adapt to changing data volumes effortlessly also enabling users to leverage the power of Azure ecosystem for more advanced analytics and machine learning. Its extensive connectivity offers a vast library of connectors to various data sources and sinks. Read all insights and reviews for Azure Stream Analytics

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7. Apache Spark

By Apache Software Foundation

4.4

I used this via a python module. It's great for handling large amounts of data with efficiency. The module also provides easy coding options and optimization.Read all insights and reviews for Apache Spark

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8. Confluent Cloud

4.5

Confluent Cloud is efficient and provides immense data computing power and it has also done well integrating with our different services. Having the effective and easily accessible operational support in the background of this tool truly makes it work for our needs.Read all insights and reviews for Confluent Cloud

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