What Is Change Data Capture (CDC)?

Fingers danced across the keyboard, but the clock ticked louder with every passing second. The backups were failing, and the logs were filled with chaos. I kept staring at the output, each line a reminder of what was lost. The first hint was always the same: dfsmsdss-first. But that signal was just one part of the puzzle, tainted by the noise of other systems clamoring for attention.

I scrolled through the abend listings, trying to find the local cause, but the mix of errors was overwhelming. It felt like trying to solve a riddle with missing pieces. Kubernetes retries were flooding the logs, complicating the already murky waters. The team was fixing symptoms but not addressing the underlying issue. It was a mess, and I could feel the pressure mounting.

I have watched the same conversation in dfsmsdss-first reviews where every fix changes the shape of the failure. It’s a losing game of whack-a-mole, where the quieter logs trick us into thinking we’re on the path to resolution when, in reality, we're just hiding the clues that matter.

As the team debated the next steps, I could feel the tension rising. It was more than just the failed backups; it was the looming deadlines that felt like a storm cloud hanging over us. Each passing hour without a solution chipped away at our confidence. The pressure to deliver reliable backups was palpable, and I knew we had to dig deeper. We needed to understand how multiple systems interacted, not just fix the symptoms that were in front of us. Only then could we hope to turn this ship around.

Step One — The Wrong Assumption

Misdiagnosing the Real Problem

"Change Data Capture is about tracking changes in databases. It’s not our issue here."

This initial instinct assumes that Change Data Capture (CDC) is solely about capturing changes in database records. The thinking is that if we’re seeing backup job failures, it must be a local z/OS issue related to how CDC is implemented or configured.

However, this perspective is dangerously narrow. While CDC does track changes, the issues we’re facing are more complex. They can stem from upstream systems' configurations or even the way data is being fed into z/OS. Ignoring the broader landscape leads to misdiagnosing the root of backup failures. The reality is that CDC is just one piece of a larger puzzle, one that requires a holistic view to truly understand the interplay of systems.

Step Two — The Partial Signal

Signals That Seem Normal

In the chaos of backup jobs failing, three signals appear normal: first, the dfsmsdss-first listings seem consistent; second, the system logs appear to indicate a routine operation; and third, the Kubernetes batch calls are functioning without errors. At first glance, everything looks fine.

But the fourth signal is where the trap lies. The missing piece is the interaction with other systems, which aren’t being monitored closely enough. While the initial three signals give a false sense of security, the lack of visibility into upstream processes means critical changes are overlooked. We often fail to connect the dots between these signals, leading us down a path of misguided troubleshooting.

When backup jobs fail, it’s often the interplay of these signals that reveals the real problem. The team needs to dig deeper, looking beyond the apparent normalcy of the first three signals to uncover the unseen issues lurking beneath. This deeper investigation is crucial to prevent making assumptions based on incomplete information that could lead to further failures down the line.

Step Three — The Failed Fix

Attempted Fixes That Backfired

The team jumped at the chance to implement a local fix, convinced it would stabilize the backup jobs. The fix involved adjusting the backup parameters and re-evaluating the job schedules. We thought we had nailed it; the backups would now run smoothly. However, when the next cycle failed, we were left scratching our heads.

What we didn’t consider was how that local fix changed the visibility of the failure. Instead of addressing the core issue, it masked the symptoms, allowing the underlying problems to fester. This left us in a worse position, as we were now chasing a shadow of a problem that was never fully understood. It was a classic case of treating the symptom without addressing the disease, and the repercussions were starting to show.

In our desperation to fix the immediate failures, we overlooked the upstream signals that would have guided us. The team’s focus on the local fix became a blind spot, complicating the recovery process further instead of simplifying it. We had to confront the reality that our approach was flawed and that the only way forward was to revisit our assumptions and seek a more comprehensive solution.

Step Four — The Real Failure

Understanding the True Failure

The real failure lies in lifecycle management, ownership gaps, and how contracts are structured between systems. The backup jobs were failing not because of CDC itself, but due to a lack of clarity in how data was flowing from upstream systems into z/OS. The integration points were frayed, and no one had ownership of the end-to-end process. This kind of oversight is all too common in complex environments where multiple systems interact.

This oversight meant that when issues arose, there was no one to hold accountable. The systems weren’t integrated in a way that allowed for seamless data flow, leading to misalignment. As a backup admin, this is a familiar struggle—missing a critical signal can mean the difference between a successful backup and a catastrophic failure. Without a clear understanding of how all the pieces fit together, we were left vulnerable to breakdowns.

In my experience, it’s the gaps in ownership and lifecycle management that often lead to these failures. Understanding how data changes affect the entire ecosystem is crucial for ensuring reliable backups. We need to establish clear protocols for accountability and visibility to prevent these issues from derailing our operations in the future.

Step Five — The Definition

Now the definition lands.

Change Data Capture is a methodology for tracking and capturing changes in database records to enable efficient data integration and replication. It helps ensure that data remains consistent across systems by capturing real-time updates.

Unlike textbook definitions, which often focus purely on the technical aspects, the lived experience of CDC reveals its operational complexities. It’s not just about capturing changes; it’s about understanding the implications those changes have on the data lifecycle and integration processes. This understanding is critical, as it shapes how we respond to changes and manage data flows across different systems.

CDC isn't a standalone solution; it requires careful management and a clear understanding of how data flows through various systems. The integration landscape is fraught with potential pitfalls, where a lack of visibility can lead to significant issues, as I’ve seen firsthand. Our approach to CDC must be proactive, anticipating potential problems before they arise.

What Solix Enforces

Operational visibility in data capture processes

What Solix's archival and governance platform enforces in this category is the operational visibility that is critical for effective Change Data Capture. The platform captures changes at the boundary of the source system, ensuring that the data’s lineage and context are preserved from the moment of capture, not just during the replication phase. This is essential for maintaining data integrity and trust.

This approach ensures that any changes made to data are reflected across all integrated systems in real-time, allowing for better decision-making and data integrity. By having a clear audit trail and a well-defined data lifecycle, organizations can avoid the failures that come from poor visibility and ownership of data processes. The real value of CDC is realized when organizations can fully leverage the insights it provides, driving better outcomes and reducing risks.

Three things to do this week

• Audit your data flow processes Conduct a thorough audit of how data flows between systems. Identify upstream dependencies and clarify ownership roles. This visibility is critical to prevent future failures related to backup jobs.
• Trace all signals in the system Map out all signals that contribute to backup job performance. Ensure that no signal is considered in isolation; instead, understand how they interact to provide a more comprehensive view.
• Register clear ownership for data lifecycle Establish clear ownership for each part of the data lifecycle, from capture through integration to backup. This accountability will help prevent gaps that lead to system failures.

References

• Forrester — Forrester report: The Forrester Wave™: Integration Platform as a Service Q3 2025 (RES184850). Relevant insights on integration platforms that include Change Data Capture.
• Forrester — Forrester report: Market Overview Big Data Integration (RES117834). Provides context on the importance of data integration strategies.
• IDC (my.idc.com) — IDC research document US53001625. Research insights into data capture methodologies.