Why Confluent Kubernetes is a data-in-motion play

Open source is all about data, obviously… but we’ve seen these last few months really amplify the notion of data-in-motion.

It wasn’t so long ago that Confluent for Kubernetes arrived, a platform purpose-built to bring cloud-native capabilities to data streams in private infrastructures — and, logically, data streams means data-in-motion, right?

To make it easier and faster to harness the value of data in motion (Ed: phew, you dropped the hyphens at last, praise be!) across an entire organisation, Confluent drew on its expertise managing Apache Kafka clusters in its own Confluent Cloud to offer the same cloud-native experience for on-premises environments.

The company insists that these ‘cloud-native capabilities are key’ to running modern data platforms — and says that they are now available wherever an organisation’s applications and data reside.

Within minutes, it’s now possible to build a private cloud Kafka service that powers applications.

According to Ganesh Srinivasan in his role as chief product and engineering officer at Confluent, to win in today’s global economy, organisations must deliver exceptional customer experiences, which stem from data-driven, backend operations. This requires the ability to react, respond, and adapt to a continuous, ever-changing flow of data from across an organisation in real time.

Srinivasan and team argue that this (above) reality has driven the rapid rise of Kafka as a key enabler to helping companies cut across silos and process the flow of business events as they happen.

Let’s remind ourselves that Apache Kafka is a distributed publish-subscribe messaging system that receives data from disparate source systems and makes the data available to target systems in real time. Kafka is written in Scala and Java and is often associated with real-time event stream processing for big data.

Self-managing Kafka is difficult

However, despite its rise, given the high level of technical complexity, need for constant monitoring, and time-consuming custom tooling, self-managing Kafka is difficult and often takes large teams of specialised professionals. These challenges can result in costly project delays and inefficient resource investments, holding organisations back as they compete to innovate and win in a digital-first world.

“With the launch of Confluent for Kubernetes, Confluent brings together the cloud-native advantages learned from Confluent Cloud with the control and customisation from self-managing their private infrastructure in on-premises environments,” said Srinivasan.

He argues that whether an organisation is transitioning to the cloud or needs to keep workloads on premises, they can take full advantage of Confluent for Kubernetes’ cloud-native capabilities, including a declarative API to deploy and operate Confluent.

The platform also makes moving applications to the public cloud easier by migrating workloads to wherever your business needs them with the ability to connect and share data with Confluent Cloud.

 

 

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