Kubernetes and Kafka: The Combo for DataOps Success?

Apache Kafka with Kubernetes together can massively increase the agility and efficiency of building real-time data applications

Thanks to companies such as Uber, Instacart and Amazon, customer expectations for online services have raised the bar for how people want to work. Why should working with data be any different? To deliver on those expectations, organizations—especially enterprise organizations—must rethink their approach and transform the way they operate. In today’s world, every digital transformation needs a data transformation.

To survive and thrive in this new world, organizations must build great data products. This means accessing data, whether it is streaming across applications or sitting statically within your organization, socializing it across teams and processing it to unlock new revenue opportunities.

However, this shift won’t be without challenges:

  • The need for highly skilled and rare talent in data technologies.
  • Data privacy.
  • Visibility across vast landscapes and sets of data.
  • The need to get into production fast.
  • The capacity to process ever-increasing amounts of data.

If you’re ahead of the curve, you’re probably already up and running a real-time data platform and taking your first steps to deploy real-time applications on it.

When it comes to data technology, open source has become the standard for building data infrastructure and tooling. It underpins everything from Netflix’s streaming services to your engineering team’s internal tools—and it forms the basis for every other software you may take for granted.

It’s the not-so-silent hero.

In recent years, one open source project has stood out from the pack, becoming the go-to platform for organizations’ software deployment: Kubernetes (often abbreviated to K8s).

In short, the Google-authored container orchestration system provides a platform for automating deployment, scaling and operations of applications.

But why should you care about Kubernetes? And more importantly, how does deploying your apps on Kubernetes help you and your organization’s efforts in becoming more data-driven?

Kubernetes, Kafka and Microservices: Data Success Without Reinventing the Wheel

One of the hottest trends among those running real-time applications is breaking down applications and pipelines into microservices.

Implementing a microservice strategy allows teams within your business to take ownership of a small part of the data pipeline and work autonomously. This enhances your team’s efficiency and increases scalability to process more data.

Kubernetes is a great technology to support your microservice strategy. It takes care of your deployment and application management as well as providing you the availability of service you require (even during software rollouts and other maintenance tasks) thanks to its built-in high availability and failover.

Managing those small parts of your real-time data application within containers and deploying them on Kubernetes accelerates your code to production and helps you scale as your microservices gain adoption.

Coupled together, Apache Kafka with Kubernetes can massively increase the agility and efficiency of building real-time data applications. With Kafka as your data streaming platform and applications deployed on Kubernetes, you can deploy and manage distributed real-time applications like never before—allowing you to speed up the time from idea to market by significantly cutting the time (and risk) typically involved in taking a new app or service to production.

Just the Start of Your DataOps Journey

However great Kafka and Kubernetes are, unfortunately, they alone do not promote DataOps.

DataOps centers around aligning the way you manage your data with the goals you have for that data. It’s bringing together your data, teams, applications and technologies and aligning them with your business’ objectives.

You might think of it as DevOps but for data. DataOps is at a higher level of abstraction and ultimately depends on good DevOps practices. It focuses on removing friction from data engineering practices, lowering the bar of entry (skills) needed to operate data, promoting data observability and higher levels of transparency.

How can organizations move forward on their journey toward a DataOps-driven enterprise?

  • Make sharing data (or data socialization) part of the mission of every product and deliverable, encouraging teams to broadcast activities to the organization.
  • Choose a platform and a mesh of preferred best-of-breed data technologies to make all data projects, applications and business logic accessible, addressable and understandable.
  • Seek out accessible solutions that require a low knowledge threshold, such as SQL, so that a wider range of people can utilize the platform and technologies.

Recent research survey data indicates that the adoption of DataOps is growing rapidly, with 72% of respondents agreeing that their organization is investing in DataOps. That figure rises to 85% at those companies for which nearly all strategic decisions are data-driven.

Kafka and Kubernetes start you on your journey. Technology is an enabler for building data intensity.

Kafka and Kubernetes have huge potential, but to realize that fully (both individually and as a combo) your engineering teams need to open them up and make them accessible to everyone in your organization. After all, when stakeholders with business domain expertise are able to access the data platform and build with data, they drive business outcomes.

Andrew Stevenson

Andrew Stevenson is the Chief Technology Officer and co-founder of Lenses.io. He leads the company’s world-class engineering team and technical strategy. With more than 20 years of experience with real time data, Andrew started as a C++ developer before leading and architecting big data projects in the banking, retail and energy sectors including Clearstream, Eneco, Barclays, ING and IMC. He is an experienced Fast Data Solution architect and highly-respected Open Source contributor with extensive data warehousing knowledge. His areas of expertise include DataOps, Apache Kafka, GitOps and Kubernetes and the delivery of data driven applications and big data stacks. Andrew has a BEng with Honors in Civil Engineering with Construction Management from University of Leeds, England and an Masters of Science in Computer Based Information Systems from University of Sunderland, England.

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