IBM Event Streams

Critical within our sales process and tracking ROI for marketing advertisement campaigns. We use it to ensure our data is accurate and that the right employees can access the correct level of data to effectively do their job.

Our primary implementations covered two user cases: 1. Analytics implementation to keep eye on the traffic flow & logs coming from users to help identify the problems in shorter time and make decisions for improvements. 2. Use of event transmission between different types of technologies and stacks that are usually not easily embeddable with each other.

My organization is based on ethics and integrity, with management guided by our values and a rigorous system of corporate governance in how we conduct business and manage risk.IBM Event Streams help our organization using Apache ecosystem products and being an open source we can customize the platform as per the need of the customer and as and when the requirement changes. payment processing, fraud detection, anomaly detection, predictive maintenance, and IoT analytics all rely on immediate action on data. All of these use cases deal with data points in a continuous stream, each associated with a specific point in time.

It helps us pull reports across the finance and procurement organization. This way we are able to quickly access information without waiting on another person to provide.

IBM Event Stream solved our real-time data streaming issues. Previously, there was a lot of lag in both the website and mobile app while fetching from backend, now that lag is removed and we have happy customers.

We used IBM Event stream as a part of data analysis pipeline. We would ingest data into Event stream and then fan out to different processors. Our team really loved the product as some of us were already familiar with Kafka and it was very easy to setup. The interface is very intuitive.

Use for marketing purposes to leverage varied synergistic datapoints to enable event and data driven and triggered campaigns

We have started using IBM Event Streams to solve multiple problems in our organizations, in which most important is fraud analytics, so whenever a customer files instant claims too many times or have unusual activity we instantly send it for scrutiny, before approving the claims and procedures. We are also analyzing traffic and events on app and web to pitch the most suitable product to customer. Apart from this for APIs, we use it as messaging bus to handle peak demands during the day and ensure all payments are timely and successful.

We use IBM Event Streams at Call Copy to build smart apps for events. We also engage event streams to act as a buffer for connecting our data sources to their data pools. We have also integrated Event streams with IBM Cloud Pak to improve how we scale in the cloud.

IBM event streams help us to give an in-depth analysis of our traffic in real time. With the help of the IBM event stream, we can directly track the clicks on elements, text, and containers as well as on DOM. It helps us to manage huge amount of traffic on a daily basis and give us the option to create segments.

IBM Event Streams allows my cloud-focused team within my hosting-focused web development company to move an Apache Kafka-based pub/sub pattern from one of our pre-existing applications into the IBM cloud. It is being used within a single application at this time as a proof of concept and that application is consumed mostly by our team. The business problem that it addresses is hosting for our pub sub-app in the IBM Cloud. We had business reasons for using the IBM cloud in this case.

IBM Event Streams is being used by a department. It serves as the main messaging bus for the communication of our backend microservices. It provides high reliability and scaling when the traffic peaks. It also reduces the coupling of the microservices, so we can make changes much easier and more frequently without breaking stuff.

Our company is currently in the process of implementing a messaging system - we develop event driven Microservices and needed an event streaming system based on Kafka which can address our needs. I reviewed IBM Event Streams and it is a great option. Event streams allows us to permanently store changes to domain objects in the form of events - the changes are then made available to various other services where they are materialized into certain solutions like Analytics, AI etc.

Message Hub is used to persist and transfer the operations domain data. It's being used to replay the data from a certain point in the past. The pub/sub model allows us to empower clients to choose when to start reading the data. It's been reliable and scalable.

We have performed an evaluation of IBM Event Streams for Event Streaming & Analytics. The idea is to use the Event Streams across the enterprise as an event streaming platform. We are still in the evaluation phase and trying to solve the problems of event-based streaming from different kinds of applications across the enterprise for real-time analytics of data.