5 Flink-as-a-Service Challenges & How Observability Solves Them

Apache Flink is a powerful, open-source stream processing framework for real-time and batch data processing. Flink-as-a-Service operations provide a framework for high throughput, low latency, stateful processing, and it powers some of the largest real-time systems in the world due to its well-proven fault tolerance and scalability.

Running Apache Flink as an Internal Service (Flink-as-a-Service)

Apache Flink requires significant resources and expertise to host and operate. Many companies choose to run it as a centralized service, benefiting from:

• Eliminating duplication of effort across teams by having a central Flink expertise hub.
• Enhanced collaboration between data teams and operations.
• Reduced infrastructure costs and setup time by pooling resources.
• Improved focus on application development instead of Flink management.
• Compliance with best practices, security policies, and observability standards.
• Pre-configured, managed Flink environments that speed up development.

What Success Looks Like – Be Ready!

Companies that implement shared Flink platforms, like Booking.com, see rapid growth:

• Hundreds of Flink applications running within a year.
• Data throughput increasing 15x, from 1GB/sec to 15GB/sec.
• Real-time data sharing across teams, accelerating innovation and efficiency.

However, growth brings challenges—outages, performance skew, and debugging delays. This is where Apache Flink Observability becomes crucial.

5 Flink-as-a-Service Scenarios that Demand Specialized Observability

1. Multi-Tenancy

Once multiple teams use a shared Flink platform, tracking activities becomes essential. The most critical observability feature for multi-tenant operators is data lineage reporting.

Data lineage allows you to trace issues back to their origin, enabling rapid resolution of:

• Noisy neighbors – When one job spikes resource consumption, degrading cluster performance.
• Schema-breaking changes – Unexpected modifications in data structures causing job failures.
• Data rebalancing issues – Optimizing sharding and parallelization without performance degradation.

2. Performance Failures

Growing platform usage increases resource demands, leading to slowdowns and outages. Traditional monitoring tools track system utilization but do not explain why problems occur.

Observability needs to include:

• Time-correlated logs and application traces to pinpoint root causes.
• State and checkpoint analysis to understand Flink job execution.
• Detailed job traces to identify performance bottlenecks.

3. Collaboration

A shared Flink platform accelerates data innovation when observability enables:

• Building and enriching data products – Teams can confidently integrate and enhance each other’s work.
• Bridging development and operations – Providing a shared understanding of system behavior for faster incident resolution.

4. Capacity Planning

As demand grows, scaling infrastructure efficiently becomes essential. Observability allows teams to:

• Monitor system performance and resource utilization.
• Predict future resource needs through historical data analysis.

5. Audits

Regulated industries and use cases like billing require precise tracking of how data is processed. Observability enables teams to:

• Reconstruct data flows to expedite audits.
• Understand how data transformations occurred inside Flink jobs.

Unlike generic observability tools, Datorios provides Apache Flink-specific insights, making audits faster and more accurate.

Observe Your Flink-as-a-Service with Datorios

Whether you’re scaling your Flink-as-a-Service platform or just starting, Apache Flink observability is key to ensuring operational quality.

Want full observability into your Apache Flink workloads? Try Datorios for free today.