Zero Data Loss at Ten Thousand Transactions per Second: An Interview with Sai Gowtham Reddi Seethi Venkata

Ask a software engineer what keeps them up at night and the answer is rarely a new feature. It is the migration. Moving live business data from one platform to another carries a category of risk that few other projects match: ITIC’s 2024 hourly downtime survey puts the cost of a single hour of downtime above $300,000 for more than 90% of mid-size and large enterprises, and 41% report figures of $1 million to $5 million or more. Companies accept the risk anyway, because the alternative is worse. The legacy modernization market, valued at $24.98 billion in 2025, is projected to reach $66.21 billion by 2031 as organizations retire the aging systems their businesses still depend on every day.

Sai Gowtham Reddi Seethi Venkata, a Lead Software Engineer at a Fortune 500 home improvement retailer, led his company’s first large-scale production database migration: 20 million records moving at 10,000 transactions per second from Couchbase to MongoDB, completed with zero data loss. The custom validation process he built for the project became the blueprint for the company’s later modernization work, and the migration saves an estimated $5 million a year. He also writes about engineering practice, most recently on building a performance-analysis AI agent for HackerNoon.

We spoke with Sai about why migrations fail so predictably, how he verified 20 million records without losing a single one, the tradeoff that nearly broke the project, and where AI agents actually belong in enterprise engineering.

Most data migrations run late, blow their budgets, or quietly lose data. Why is this still so hard?

Because most teams budget for a copy job and discover a translation problem halfway through. Couchbase and MongoDB are both document databases, and people assume that makes them interchangeable. It does not. Document shape, index behavior, and consistency guarantees all differ in ways that only surface under production load. The staffing picture makes this worse. Chainguard’s 2026 Engineering Reality Report found that engineers spend only 16% of their week building new features, with the rest consumed by maintenance and keeping existing systems alive. Migration work gets whatever capacity remains, and that is rarely the team’s best capacity.

The second failure mode is definitional. Teams declare success when the data has moved, not when the data has been proven identical. In retail, a record is never just a row. It is price state, inventory state, and promotion state, and the next transaction depends on all of it being right. If you cannot prove equivalence field by field, you have not migrated anything. You have relocated your problems to a system nobody understands yet.

You led your company’s first large-scale production migration, 20 million records from Couchbase to MongoDB at 10,000 transactions per second, with zero data loss. How did you actually pull that off?

The decision that mattered most came early: do not trust the migration pipeline, and build a second process whose only job is to catch the first one lying. I designed a custom validation batch process that swept both systems, compared records field by field, and flagged every mismatch for reconciliation. It ran alongside the migration from day one, not after it. By the time we finished, we had audited all 20 million records at full production throughput, and the loss count was zero. That was the exit criterion we set before writing any code, and we did not negotiate it down when the timeline tightened.

The business case was equally concrete. Retiring the legacy stack saves the company an estimated $5 million a year in licensing and operating costs, per our internal analysis. Just as important, the validation framework outlived the project. Other teams picked it up for their own modernization work, which changed how the company thinks about migrations in general. Proof of correctness stopped being a one-off effort and became standard practice.

That validation approach became the template for later modernization at the company. What did building it teach you?

That verification has to be treated as a product, not a phase. We instrumented the validation process the same way we instrumented production services, with reporting that a non-engineer could read. The same thinking drove a discrepancy tracker I built for the store systems work, which compared mainframe records against the new microservices and gave the business the confidence to finally retire the mainframe. The returns justify the discipline. Kyndryl’s 2025 State of Mainframe Modernization survey of 500 senior leaders found modernization programs returning between 288% and 362% on investment, but only for organizations that carry them through to completion instead of freezing halfway.

It also taught me that test automation pays for itself faster than anyone expects. For the store systems redesign, I introduced behavior-driven test automation with Allure reporting, which cut our regression effort within the development cycle by about 95%. When verification is cheap, people verify everything. When it is expensive, they verify what they have time for and hope the rest holds. Hope is not a strategy at 10,000 transactions per second.

What came closest to breaking the project, and what did you have to give up?

Honest answer: the store data, not the database. The legacy store systems had chronic latency and synchronization problems between the mainframe and the stores, and a family of barcode defects kept generating support incidents while we were mid-migration. I split focus between the migration and stabilizing the old platform, which is exactly what the textbooks tell you not to do. We resolved the barcode defect class and cut related support incidents by roughly 80%. Separately, the mainframe-to-microservices redesign brought latency and sync incidents down by about 95% across some 1,800 stores. But for a stretch of several months I was effectively running two projects, and the migration timeline absorbed the cost.

The tradeoff I still think about is validation depth versus speed. Full field-level comparison on every batch slowed our throughput noticeably, and there was real pressure to sample instead. I refused. Sampling tells you the migration is probably fine. Full comparison tells you it is fine, and at this scale “probably” is how you lose records quietly and find out during the holiday season. We lost a few weeks to that decision. Given that we finished with zero data loss, I would make the same call again, but I will admit it made for some uncomfortable status meetings.

You also completed five peer reviews for SEATNGA 2026, the International Conference on Sustainable Energy and AI Technologies for Next Generation Applications. What does reviewing research teach someone who builds production systems?

Reviewing forces a question that day-to-day engineering lets you dodge: how would this fail, and would the authors even know? When I read a submission, I look for the verification story before I look at the results. That habit transfers directly to production work. Stack Overflow’s 2025 developer survey found that 66% of developers call AI answers that are “almost right, but not quite” their top frustration with AI tools. Most failed migrations live in that same gap. The data is nearly correct, the record count is nearly equal, and nearly is where the damage hides.

Completing five reviews for the conference also reminded me how hard it is to write down what you actually did in a way a stranger can evaluate. Weak submissions make claims without evidence. Strong ones show their method clearly enough that a skeptic can follow it. I hold my own engineering documentation to the same standard, because eventually a stranger, whether a reviewer, an auditor, or the next engineer on the team, has to trust the work without trusting me.

Gartner expects 40% of enterprise applications to ship with task-specific AI agents by 2026, up from less than 5% in 2025. You built a performance-analysis agent yourself. Where do agents genuinely help, and where is the hype?

Agents earn their keep on bounded diagnostic work. The performance-analysis agent I built, and wrote about for HackerNoon, works because its job is narrow: ingest performance data, correlate the signals, and surface the probable culprit with the evidence attached. Gartner’s prediction matches what I see in practice. The successful deployments are task-specific by design. The failures I hear about are usually agents handed an open-ended mandate and no verification layer, which recreates the migration problem in a new form.

The hype sits in the judgment calls. An agent can flag a discrepancy between two systems. It cannot decide whether that discrepancy matters to the business, because that decision requires context the model does not have and accountability it cannot carry. The healthy pattern is agents proposing and humans disposing. Teams that invert that order will spend the next few years debugging decisions nobody remembers making.

What is still unsolved in this space, and what are you working toward?

Validation is still treated as a differentiator when it should be table stakes. Most organizations do not have a continuous way to prove that two systems agree. They have a project, a deadline, and a prayer. The discrepancy tracking approach we built points at where this goes: reconciliation as a permanent service, running whether or not a migration is underway. Legacy retirement, cloud adoption, agent deployment — every one of these trends multiplies the number of systems that must agree with each other, and the industry has not priced that in.

For my part, I want to keep pushing the blueprint outward, inside my company and through my writing and research in agentic AI for engineering operations. The migration taught me a lesson I keep coming back to. The goal was never to move 20 million records. It was to prove, record by record, that nothing was left behind. If that standard becomes boring and ordinary, the industry will be better for it.