Apache Kafka on Confluent Cloud enables organizations to perform real-time data streaming and processing, integrating user data, and managing large transaction volumes efficiently.
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Organizations leverage Apache Kafka on Confluent Cloud for several applications such as capturing change data, handling IoT workloads, and fostering data migration and microservices. It is instrumental in publishing and managing data across multiple platforms, ensuring both high throughput and seamless log management. Confluent Cloud's features like scalability, efficient transaction management, and cost-effectiveness, combined with seamless integration with multiple data tools, make it a preferred choice for real-time data processing tasks.
What are the key features?
What benefits and ROI should you look for?
In specific industries, such as finance, real-time data processing is crucial for handling high volumes of transactions and location tracking. In retail, it integrates user data and manages inventory systems efficiently. IoT applications benefit from Apache Kafka on Confluent Cloud by capturing and processing massive data streams from connected devices in real-time.
We use Apache Kafka with Confluent Cloud for specific real-time transaction use cases, both on-premise and in the cloud. We have been using Confluent Cloud for about five years.
We initially used it for data reputation, then expanded to microservices integration and Kubernetes, focusing on improving data quality and enabling real-time location tracking.
We configure it for data transactions across various topics and partitions, depending on the specific use case and required throughput.
From an IT perspective, I've used this product across all domains: system development, operations, data management, and system quality.
We have experience using Kafka on Confluent Cloud for data pipelines. We've implemented several techniques to optimize topic usage, integrated it with microservices, and even utilized change data capture (CDC) components.
We leverage topic configurations and partitions extensively. We simulate various use cases with different partition numbers, like high throughput scenarios with 45 partitions or high transaction environments with other configurations.
In our microservices architecture running on Kubernetes, Confluent Cloud helps us manage transactions effectively. Additionally, it integrates seamlessly with our data analysis tools like DataStage, Big Data, and Teradata, providing a smooth flow for large data volumes.
The overall integration with other tools and efficient transaction management are the key benefits I experience with Confluent Cloud for large-scale data streams.
I saw an interesting improvement related to the analytics environment.
We have been using this solution since 2018.
We have a well-defined process and platform for scaling big data solutions. When multiple providers propose their options, we configure a custom platform based on our current use cases.
However, we're planning to migrate to a new big data platform within the next fifteen months. This timeframe is due to our internal process for evaluating and deploying new platforms.
In terms of configuring the product, specifically Confluent, understanding the design and configuring values for various parameters is something only I am familiar with. The initial setup, including the initial Non-Disclosure Agreement (NDA) and progress in implementation, is quite difficult.
We primarily use on-premises Kafka for high-transaction scenarios. If something crashes there, we handle data processing manually. It might not be the most efficient, but we haven't considered it a major concern.
For other use cases, we also prefer on-premises.
The implementation took us one year. It involved configuring the platform over a year. The time required for configuring or implementing use cases varies; some take longer, while others might also take up to a year.
I attempted the deployment myself. However, there were three of us involved in these tasks within this analytical environment.
My role revolves around deploying use cases in analytics. I also operate within Architect areas, focusing on data architecture.
For maintenance, the same three people take care of it. We might need two more, but for now, three is sufficient.
The platform and container operations themselves provided significant value.
