Break Your Bottlenecks: Don’t Let Kafka Challenges Hold You Back

In their bid to quickly adjust to dynamic market needs, more businesses are embracing open source software and technologies.  A new report from Red Hat found 95% of IT leaders see open source as important to their organization’s overall infrastructure.

While reducing IT infrastructure costs is a key focus for businesses today, along with modernizing data infrastructure, meeting or exceeding service-level objectives (SLOs) and accelerating time to market,  implementing open source software seems appealing. Yet running it at scale adds to costs and operational complexity. Here’s how fully managed services can help.

Take Apache Kafka, an open source distributed data streaming platform. With companies realizing the value of prioritizing data as the foundation for digital transformation and differentiation, Apache Kafka has quickly become the cornerstone of modern data infrastructure.

Today, Kafka is being used by more than 70% of the Fortune 500 as their real-time data streaming platform of choice — and helping drive business growth and unparalleled customer experiences.

Despite the many benefits of open source software, the costs associated with deployment and maintenance can manifest themselves in several ways.

For instance, while Kafka is free to download, modify, use and redistribute, self-managing a complex distributed system at scale still comes with substantial operational and development costs, risks of costly downtime and security breaches, and adds to enterprise expenses. Despite the many benefits of open source software, the costs associated with management and a lack of elasticity can manifest themselves in several ways.

These costs can be bucketed into several categories:

• Infrastructure cost: Organizations have to pay for the underlying infrastructure as well as its management before the Kafka software development and operations costs. Over-provisioning is required to support fluctuating demand, plus, there’s the inability to scale storage without growing compute resources.
• Scalability cost: The ability to scale at reasonable costs is another challenge. As usage increases, organizations incur exorbitant costs to scale up due to manual processes.
• FTE and operational cost: includes allocating engineering resources for managing Kafka, in addition to requiring engineers to build components and tools for a more complete data streaming architecture, rather than building data streaming applications or other high-value projects for the business. Plus, it’s increasingly difficult to hire and retain Kafka talent.
• Downtime cost: includes costs associated with unexpected cluster failures and maintenance as Kafka spans more use cases, data systems, teams and environments. Valuable resources are then diverted to address unplanned downtime and breaches. While these costs are difficult to quantify, their significance becomes abruptly apparent when an incident does occur.

Elasticity, one of the cornerstones of cloud native computing, allows for allocating varying infrastructural resources to meet the demands of a business. It lets businesses operate more efficiently with lower infrastructure cost by letting them use and pay for only what they need when they need it.

Simply put, you want to scale your cloud workloads in a way that does not exponentially increase your costs. And elasticity helps fit workloads in a way that optimizes your cost curve.

More importantly, elasticity isn’t just about faster scaling, it’s also about the ability to shrink infrastructure resources when needed to ensure you’re not overpaying for infrastructure—a key requirement during these uncertain times.

When it comes to building elasticity into streaming infrastructure, horizontal scaling of individual Kafka clusters has its limitations. As the number of brokers grows, the number of connections per broker required to manage replication becomes difficult and impacts performance. And it adds to operational costs. Scaling vertically brings challenges too. Vertical scaling of storage increases the time to recover from a failure.

How to Solve the Kafka Elasticity and Cost Problem

The latest evolution in open source offerings is in the area of managed services. They provide businesses with even more ways to benefit from the open source community by making configuration, monitoring and management of open source software simpler and more reliable, while saving on costs and resources.

With a fully managed service for a popular open source project like Apache Kafka, your organization can shift key resources to higher-value tasks. They guarantee zero operational burden by ensuring that clusters are provisioned instantly and maintenance is seamlessly managed. Plus, you can also skip the part where you spend six to nine months hiring and training people and instead focus on product development right away.

What’s more, it doesn’t involve capacity planning, data rebalancing or any other typical operational burden that goes into scaling data infrastructure, ensuring your business runs faster and more efficiently with lower infrastructure cost, maintenance and downtime risks.

In fact, offloading Kafka infrastructure and operations to a fully managed cloud native data-streaming service can minimize the technology’s burden and risks, keep your best people focused on their critical projects and improve the total cost of ownership in a number of ways, including:

• Avoiding over-provisioning: Apache Kafka best practices traditionally recommend provisioning your cluster for peak usage. This often comes months or even quarters ahead of the traffic. Leveraging a fully managed service helps reduce this to mere hours, so you can scale right before the traffic hits and not waste weeks of unused capacity.
• Increasing resiliency and availability: Building in a production-grade service-level agreement (SLA) takes a lot of effort and resources, including engineer time. And the smallest difference in SLA means exponentially more downtime for your customers. When broker failures happen, fully managed services ensure that you are able to automatically and quickly repair or bring on new brokers, which is critical for making sure a cluster maintains its SLAs and provides the user with the appropriate amount of capacity.
• Deploying with one click: With fully managed services, scaling can be automated or done through a click of a button. That means you don’t need to spend time on capacity planning, network setup, load balancing or any of that. Instead, your highly compensated full-time equivalents (FTEs) could focus on innovation and building mission-critical applications.

Building scalability into infrastructure has never been more important, and leveraging cloud elasticity has become a powerful way for businesses to ensure that they’re ready for anything.

A key differentiator of Confluent Cloud, a fully managed cloud native service for Apache Kafka, is that elastic scaling applies when sizing both up and down. You can shrink clusters just as fast as you can expand them. The result? You avoid overpaying for any excess capacity when traffic slows down.

Want to learn more about how Confluent took Apache Kafka’s horizontal scalability to the next level and made Confluent Cloud 10x more elastic? Check out our video overview and customer stories now.