Event Streaming

IntVerse.io recommends Micro services and event streaming architectural patterns that can be used together to build modern, scalable, and resilient software systems.

Micro services architecture is an approach to building software applications as a set of independently deployable services that can communicate with each other over a network. Each service is responsible for a specific task or business capability and can be developed, deployed, and scaled independently of other services.

Event streaming, on the other hand, is a pattern that involves capturing and processing events in real time to enable applications to respond quickly to changes in data. In this pattern, events are generated by various sources and are sent to a central event streaming platform where they can be processed, analyzed, and acted upon by other applications and services in real-time.

At IntVerse.io, we provide services for Micro Services and event streaming implementation, including expertise and guidance, customization, integration, optimization, and training and support. Our team has expertise, experience, and track record of successful event streaming implementations for a variety of industries and use cases.
Key Event Streaming we supports, but not limited

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Apache Kafka

Apache Kafka is an open-source event streaming platform that enables organizations to build real-time data pipelines and stream data across various applications and systems.


Confluent provides a commercial version of Apache Kafka and offers additional enterprise-grade features such as management tools, security, and monitoring.


Redpanda is an open-source event streaming platform that was created by the team at Vectorized. It is designed to be a modern, high-performance, and scalable alternative to Apache Kafka, and it is built using C++ and Rust.


Solace is an event streaming and messaging platform that enables real time data movement across different applications, cloud services, and devices. The Solace platform offers a variety of messaging and event streaming capabilities, including messaging-as-a-service, event distribution, streaming analytics, and microservice integration.

AWS Kinesis

AWS Kinesis is a cloud-based event streaming platform from Amazon Web Services that enables organizations to process real-time data at scale.

Google Cloud Pub/Sub

Google Cloud Pub/Sub is a cloud-based event streaming platform from Google Cloud Platform that enables organizations to process real- time data at scale.

IBM Event Streams

IBM Event Streams is a cloud-based event streaming platform that enables organizations to build real-time data pipelines and stream data across various applications and systems.

Microsoft Azure Event Hubs

Microsoft Azure Event Hubs is a cloud-based event streaming platform that enables organizations to ingest and process large volumes of real-time data.

Apache Flink

Apache Flink is an open-source event streaming platform that enables organizations to process real-time data streams and build event-driven applications.
Exploring the Top Features of Event Streaming

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Stream Processing

Stream processing is the core component of event streaming, and it involves the continuous processing of real time data streams, including filtering, transformation, and data aggregation.

Event-Driven Architecture

Event-driven architecture is a design pattern that focuses on the production, detection, and reaction to events. In an event-driven architecture, the system responds to real time events rather than waiting for batch processing.


Event streaming is highly scalable, allowing organizations to handle massive volumes of data in real time. It can handle large numbers of data sources, processing them in parallel and scaling resources up or down as needed.

Fault Tolerance

Event streaming systems are designed to be fault-tolerant. They use techniques such as redundancy and replication to ensure that the system can continue to operate even if one or more components fail.

Real-Time Analytics

Event streaming allows organizations to perform real time analytics, detecting and reacting to trends and changes in real time.

Key benefits of Event Streaming

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Real-time data processing

Event streaming enables organizations to process data in real-time, which can help them make faster decisions and respond to changing circumstances quickly.


Event streaming platforms can scale to handle large volumes of data, making them suitable for organizations with high data processing requirements.


Event streaming platforms can handle a variety of data formats and types, including structured and unstructured data.

Data integration

Event streaming platforms can integrate data from various sources, including databases, sensors, and IoT devices, enabling organizations to gain insights from multiple data sources.

Event-driven architecture

Event streaming can help organizations move towards an event-driven architecture, where applications and systems are designed to respond to real time events, enabling faster and more flexible data processing.

Reduced latency

Event streaming can help reduce data latency by processing data as soon as it is generated, enabling organizations to take immediate action on the data.


Event streaming platforms can be cost-effective as they can process large volumes of data using a distributed architecture, reducing the need for expensive hardware and infrastructure.

Case Study

One of the biggest challenges in the insurance industry is fraud detection. Fraudulent activities can result in significant financial losses for insurance companies. Therefore, it is essential for insurers to identify quickly and investigate fraudulent claims.

To tackle this challenge, a leading insurance company adopted a real-time fraud detection system that leveraged event streaming technology from Confluent. The insurance company has partnered with IntVerse.io team to implement Confluent Platform, which is a distributed event streaming platform based on Apache Kafka. Confluent Platform allowed the insurer to stream and process data from various sources, such as insurance claims, customer transactions, and other related events, in real time.

The platform also enabled the insurer to analyze the data and detect any fraudulent activities as soon as they occurred. The insurer used machine learning algorithms to process the data and identify patterns that were indicative of fraud. By leveraging IntVerse.io team's expertise and Confluent Platform, the insurance company was able to improve its fraud detection capabilities significantly. The system helped to reduce false positives, resulting in improved accuracy and faster detection of fraudulent activities.

Moreover, the event streaming solution allowed the insurer to quickly respond to any detected fraud by automating the claim review process. The insurer could quickly investigate suspicious claims, validate the authenticity of the claim, and take necessary actions.

Overall, the adoption of Confluent Platform helped the insurance company to improve its fraud detection capabilities and reduce financial losses due to fraudulent activities.