Benefits of Using Kafka to Handle Real-time Data Streams

Enterprises collect and use more data than ever before. To make the most out of all this data, they build complex data pipelines, most of which are capable of handling over a million transactions per minute. To meet this demand, enterprises need to reliably handle high throughput data feeds with low latencies. This is because at this scale, even small mistakes, can cost millions of dollars in avoidable additional expenses.

Kafka can handle your 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 Kafka handles data as unbounded sets, it can help enterprises process big streams of incoming data in real-time, without any significant time lags.

Kafka can handle continuously changing data with minimal resources

Kafka uses change data capture (CDC) methods such as triggers, queries, and logs to keep track of only your most recent data changes. So, when there is a change in data, Kafka doesn’t transform or load all the data once again, which means computing resources don’t get locked up with changes.

Kafka can handle complex data pipelines and reduce production loads

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

Kafka and multidimensional data observability

As businesses continue to undergo digital transformation, data becomes more mission-critical. Data is intertwined with operations at every level, so not having a comprehensive data observability solution can increase the risk of unexpected data problems and outages.

Thoughts and trends on data observability