Enterprise applications are moving to using big data and multiple data points to personalize application services. To get the most out of this large volumes of data, one needs to build complex data pipelines capable of handling over a million transactions per minute. Product teams can benefit from using Kafka to handle complex data pipelines and real-time data streams, along with a multidimensional data observability tool.

Kafka can handle real-time data pipeline with high throughput, low latency, and guaranteed reliability without tying up computing resources. Scale is critical for all enterprise data teams, and Kafka can help process complex data pipelines at scale quickly and cost-effectively.

Benefits of using Kafka:

Kafka can handle large data feeds in real-time within a few milliseconds:

Think of Kafka as a huge data conveyor belt that moves your data to where you need it, in real-time. And because it handles data as unbounded sets, it can help enterprises process big streams of incoming data in real-time, without any significant time lags.

Kafka, a streaming platform, can handle the real-time data streaming needs of enterprises. Instead of batches, Kafka can process and present the data continuously in unbounded streams. So, it can handle huge data feeds within milliseconds to meet the real-time data processing and analysis needs of enterprise applications.

Kafka can plug in to meet a wide range of real-time use cases. For example, it can help businesses provide users with real-time notifications, personalized recommendations and can give the ability to verify and analyze transactions at scale in real-time.

Using Kafka for real-time notifications

Reduce production loads:

Kafka can be coupled with microservices to handle complex data pipelines that process millions of transactions per second. It reduces production loads and costs by simultaneously streaming data to different targets.

As soon as data pipelines start to handle millions of transactions every minute, their complexity increases. At a larger scale, they need to work with microservices, or else they break down. Secondly it also reduces production loads by simultaneously serving data streams to different targets. For example, it can stream a transaction simultaneously to both an end-user as well as a machine learning algorithm.

Kafka can connect with various database sources, including NoSQL, Object-Oriented, and distributed databases. This helps product teams to create customized solutions for enterprise clients. HTTP and REST APIs are also supported.

Meeting the modern data needs of businesses can get out of hand quickly. If not monitored properly, it can lead to additional infrastructure costs and unforeseen expenses. So, along with real-time data streaming, it is equally important to monitor and manage data pipelines effectively.


Enterprise applications can work with Kafka pipelines to ingest, validate and transform data streams in real-time. It can enable you to make more effective data-driven decisions in real-time.

‍If you are looking for experts to work with your Kafka pipelines, to meet your data streaming needs in real-time; our software specialists can work with your CTOs and product teams to build your products at scale.


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