Event Stream Processing Reviews and Ratings

Amazon Kinesis Data Analytics is a powerful advance analytics software powered by AWS for processing and streaming data in the real time. It is perfectly designed to run real-time processing application without the need of manual intervention or without managing underlying infrastructure.

I was impressed by the Python Library support that Confluent provides that help validate that the integrations between our existing Python libraries and Confluent worked seammlessly. The migration from self-managed Kafka to Confluent Platform allowed us to shed the operational burden and costs associated with running Kafka.

Google Cloud Dataflow is a fully managed stream and batch data processing service. It offers a lot of flexibility and scalability for building data pipelines. It provides a relatively easy-to-use platform for designing and deploying data processing pipelines that supports multiple programming languages such as Java and Python. Also, it is a fully managed service as Google takes care of infrastructure provisioning, scaling, and maintenance of managing servers. It is designed for horizontal scalability, and it can handle both batch and stream processing workloads. Google Cloud Dataflow can be seamlessly integrated with other Google Cloud services like BigQuery, Pub/Sub, and Cloud Storage, which makes it a valuable component in the Google Cloud ecosystem. Monitoring and debugging tools help to keep an eye on the health of pipelines and provide insights into job progress, performance, and resource utilization. Also, it is backed by Google, so there is strong community support and access to Google's customer support if needed.

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.

The Cribl environment as a whole was fairly straightforward to set up. We utilize a hybrid approach and it was simple to build out the on-prem infrastructure and pipe it to our cloud instance. Our entire on-premise POC to cloud only took a couple of hours to set up once we decided to move forward with the product it was only a matter of minutes to get log data following from our on-prem stream collectors to our Cribl cloud instance.

As I delved into the realm of real - time data analytics, I found Azure Stream Analytics to be a powerful tool with the Azure ecosystem. This service presented an array or flexible and dynamic capabilities that allowed me to harness the potential of streaming data for various use cases. The following patterns and scenarios showcased how Azure Stream Analytics could be seamlessly integrated. My exploration of Azure Stream Analytics revealed a powerful tool that could transform streaming data into actionable insights. The various patterns and scenarios showcased how this service seamlessly integrated into larger architecture, enabling the creation of dynamics real - time applications, visualization, and analytics. The flexibility and adaptability of Azure Stream Analytics left me excited about the possibilities it offered for building innovative and efficient data - driven solutions.

Apache spark is a unified engine software made for large scale data analytics powered by Apache Software Foundation. Its flexible option allows this software to work on multiple language and execute Data Analytics and Machine Learning tasks.

What i liked most is the topic feature in which we can produce and consume data. It is the easiest to set up and get the work done using the documentation provided. Also, with Confluent, it is very easy to handle huge amount of data.

Everything about Aiven has really been great. Personally i like the partition information about the size, offset etc. With it, it's very easy to create new clusters and enable connectivity with public cloud environments. Also, the user management is quite simple thus making it very easy to work with.

I've been a fan of MS SQL Server for more than 20 years, and it's been a great journey! The software keeps getting better with new features to tackle the latest challenges. It's always reliable, making it a wise choice of time and investment.

Red hat provides a great decision manager application mainly useful for distributed working enviourment .

Rich Developers Tools and SDKs : the availability of variety of SDKs and developers tools has made working with amazon kinesis data streams a smooth experience. Weather using python, java or other languages, i have had the flexibility to choose the tools that best suit my project. Efective monitoring and Alerting : AKDS monitoring and alerting capabilities have been invaluable in ensuring the health and performance of my data streams. Being able to setup custom alerts has allowed me to proactively address the issues. Unmatched Scalability : the scalabilty of AKDS is awe-inspiring. It feels like having an elastic data pipeline at disposal. Weather I am dealing with trickle or torrent of data, the service effortlessly accomodates my needs.

It is very user-friendly and has a great set of tools to work with Stream data in a very fast manner allowing us to meet our current batch needs and leave the door open for future streaming.

The simple pricing, professional support, and superior architecture that allows us to scale up to hundreds of thousands of concurrent connections with no problems at all makes SteamSets the best Data streaming solution for us.

There is no reason for improvement from our point of view. Very happy with the ease of use, with the way Axual behaves towards us as customer and with the professional attitude and customer centric support.

One of the key feature that attracted us is, that this product comes with machine learning tools. This product is flexible, reliable and highly scalable. It empowers various industries from retail to telecommunication. It really helped us to strengthen and in improving our CRM. One can easily take real time actions which helps in maintaining customer satisfaction and hence in increasing productivity. Customer support is too good and they have proper knowledge about the product and it's deliverables.

ideal framework for real time data processing, easy to learn

SAS is a true partner with us on our quest to deliver value to our manufacturing sites

We initially purchased Oracle Stream Analytics specifically for deduping but quickly found that it can be used for so much more. We have practically eliminated all duplicate records since we can stop users from creating a duplicate record. Also that we can build a recover file if necessary is a good feature that other tools do not give.

* Easy to deploy and understand * Good customer support when required * Good performance