What Devs Should Know When Starting an Apache Kafka Journey
If you’ve worked with Apache Kafka, or are considering it, you’ve likely encountered its steep learning curve and operational challenges.
From complex configurations and performance tuning to time-consuming integrations and handling data inconsistencies, many teams struggle to unlock its full potential. In my experience, most of the common Kafka trials can be distilled down into a couple of root causes: Kafka can be hard to learn, and companies have a fragmented data strategy. Both can turn a potentially paradigm-shifting technology into a costly headache.
These challenges often help expose and solve foundational challenges holding you and your organization back. Let’s explore the speed bumps and share some actionable advice on how to turn them into opportunities.
Complexity Is Commonplace
Welcome to the world of distributed computing in the age of AI, a world where massive volumes of data require high-availability and low-latency processing. By checking those boxes (and others), Apache Kafka has become the de facto standard for event streaming use cases. Some common examples include:
- Activity tracking: Businesses use Kafka for real-time tracking of user interactions to power personalized experiences, such as ad impressions, clicks or social media engagement.
- IT architecture modernization: Kafka helps organizations connect legacy systems with cloud native architectures or migrating on-premises workloads to the cloud, enabling modernization without major disruptions
- Stateful stream processing: Companies in e-commerce, media and entertainment use Kafka to power real-time recommendation engines, where user behavior informs personalized content suggestions, sales and marketing offers, and in-app notifications.
Distributed Systems and Data Pipelines Have Challenges
Distributed systems such as Apache Kafka provide immense potential for building a robust data infrastructure. But that doesn’t come without operational challenges, and that’s nothing new to anyone already building and managing data pipelines.
I once worked for an advertising startup that used batch processing to reconcile ad spending for advertisers with impressions, clicks, and other events for publishers. In other words, it was how we got paid. Our team wasted so much time repeatedly fixing the same brittle ETL (extract, transform, load) pipelines, but the company wasn’t motivated to make a change until the combination of hardware costs and data volume from our ad server made our C-level execs frustrated.
Not only was the batch processing-based architecture slow and expensive, it also couldn’t provide real-time insights. As we were making our foray into real-time bidding (RTB), closing the feedback loop on those insights was the key factor of success. After a “messaging system bake-off,” we landed on Kafka as the backbone of our data pipelines.
Our developers and DevOps engineers spent many hours troubleshooting Kafka operations, but the company’s ad bidding became more efficient, and the underlying data pipelines became more reliable and less expensive to maintain over time.
Kafka’s Benefits Won’t Fill the Gaps in Your Data Strategy
Here are some common challenges teams face early in the journey with Kafka:
- Lack of data governance: Engineers often see governance as a “four-letter word,” a burden that stifles progress rather than an essential part of any platform. Without proper data contracts and schema management, you’re left with poor data accessibility, discoverability and quality.
- Overseeing scaling and capacity planning: One of the hardest aspects of implementing Kafka at scale is ensuring that you have the right resources to manage it. Kafka isn’t a magic bullet — it requires dedicated personnel with a solid understanding of partitioning, replication and your data volume.
- Understanding ownership and expertise: I’ve worked at organizations where this conversation happens about a lot of newly adopted tech: “This seems like a great idea. Who is gonna manage it? Who is gonna pay for it? If it’s a shared resource, how do we allocate the usage to the cost centers?” Not having that conversation early enough stifles many potentially game-changing innovations.
These problems aren’t unique to Kafka projects, but they certainly make operating it more difficult because they undermine the impact of its scalability and performance benefits across your organization.
Once you have the right resources and approach, Kafka can be a powerful tool that helps you manage real-time data and unlock new capabilities for your business.
Tips for Getting Started With Kafka
When I started using Kafka, my team focused on simple use cases, things like streaming logs and basic event processing. From there, we gradually moved to more complex use cases, like real-time analytics and stateful stream processing. Along the way, we all learned not just about Kafka, but also about building a resilient, scalable data architecture.
Here are some tips to get you started:
- Start small: Don’t start trying to “boil the ocean.” Consider a scope of low-risk, simple use cases that help you master the core concepts of Kafka and event streaming patterns. Use cases like log streaming or basic event processing can give you a solid foundation. These early wins will help improve buy-in from interested parties.
- Focus on the basics: Learning key concepts like data governance, developer education and having an established approach to the software delivery life cycle are essential building blocks for being successful with Kafka.
- Define events thoughtfully: Kafka can exponentially benefit an organization by allowing multiple teams to independently use the same source data. To make this work for you, carefully plan the data you’ll share with other teams. Design schemas for those events and use industry-standard serialization formats such as Avro or Protobuf. For bonus points, embrace the broader principles of data contracts.
- Key design and partitioning, learn it and live it: For each Kafka-based use case, take great care in planning your key strategy. Concentrate on factors like expected data volume. Design the keys with the goal of distributing the data as evenly as possible across the cluster. Take into account your processing SLAs (service-level agreements) to help determine the number of partitions as you create topics.
- Infrastructure as Code: Your Kafka infrastructure configuration will evolve as you refine and expand its use. Apply established DevOps principles such as IaC with tools like Terraform from an early stage. Your future self will thank you.
- Avoid the premature optimization trap: Knowing how to optimize and tune your cluster (and your client code) is almost as important as knowing when to do it. Use performance testing and observability tools to understand which knobs and levers to tune.
- Consult the experts: If you’re using data streaming, I encourage you to bookmark the Confluent Developer website for all things Kafka and other data streaming information. This website features blogs, free courses and thought leadership from our team of streaming professionals.
Your organization is not the first one that’s trying to figure out how to get started with Apache Kafka. But there’s a reason why eight out of 10 of the Fortune 500 trust Kafka as their event streaming platform of choice. Managed solutions can help companies easily use this powerful technology and overcome some of these key challenges.
Whether you choose open source Kafka or a managed solution, on-premises or a cloud service provider, the benefits of an event-driven design can fundamentally change your architecture and give your organization a competitive advantage.
