Imagine this: you’re finally ready to stream the season finale of your favorite series. You press play—only to be greeted by an error message. Frustrated, you immediately switch to another app. You’re not the only one. Whenever a streaming service crashes, millions of viewers do exactly the same thing.
According to Gartner’s research, every minute of downtime costs companies an average of $5,600. For streaming giants with millions of viewers, that number soars. If a platform is down for just an hour, companies can lose hundreds of thousands—or more—not only in revenue, but also in angry subscribers and disappointed content partners. These days, 99.999% uptime isn’t something to brag about; it’s the bare minimum if you want people to stick around.
This was the recurring nightmare that Aniruddha Vyawahare, an engineer working in streaming data analysis, kept facing. Most companies just waited for things to break. When an outage happened, panic followed. Teams scrambled, but with millions staring at error screens, the old “fix it on the fly” method just wasn’t enough anymore.
The Engineer at the Intersection of Streaming, Big Data, and Reliability
Aniruddha Vyawahare has spent over ten years right where streaming tech, big data, and keeping things running smoothly all collide. He’s led teams through chaos—think millions of people streaming at once, no glitches allowed—and turned shaky systems into rock-solid platforms trusted across the industry.
With more than a decade spent deep in the trenches of streaming technology, Aniruddha Vyawahare developed a rare blend of expertise: large-scale data processing, video analytics, device integrations, and emergency-level troubleshooting. After earning his Master’s in Electrical Engineering from San José State University, he joined Conviva, where he began as a hands-on TAC engineer and steadily rose to lead global product support.
Along the way, he built teams from scratch, redesigned escalation processes, and worked side-by-side with engineering and executive teams to deliver flawless service levels. His technical toolkit—Spark, Databricks, AWS, Tableau, Python, SQL—was only part of the equation. What set him apart was his ability to translate complex engineering challenges into business-critical improvements.
He’s not just a tech guy; Aniruddha knows the whole streaming game. Video analytics, device integrations, crunching mountains of data, jumping in when things catch fire—he’s done it all.
Aniruddha’s toolkit? It’s stacked—Spark, Databricks, AWS, Tableau, Python, SQL. He moves easily between engineering, product, and exec teams, translating tech speak into business results and back again.
When Outages Became Too Big to Ignore
The real turning point came after a series of serious outages that left millions in the dark. Most teams didn’t even know there was an issue until social media exploded. Once they found out, chaos took over—everyone tried different fixes, people talked past each other, and no one was clear on who should do what. The end result? Hours wasted, reputations damaged, and a lot of money lost.
Aniruddha watched this chaos happen over and over. For years, companies could get away with scrambling behind the scenes. But with millions now expecting instant streaming, that approach fell apart. The “usual” process? Someone sees a flood of angry tweets or calls, so teams start throwing out fixes—usually without coordinating. By the time they find the real issue, the harm is done.
So, what made the difference? A new way to handle disasters: the Structured Incident Management Framework. Suddenly, there was order. Every type of crash had its own plan. Teams knew what to do, who to call, and how to check if they were actually improving.
The Breakthrough: A Structured Incident Management Framework
It all came down to two things: how quickly teams noticed a problem, and how fast they fixed it. No more guessing whether things were working—just check the numbers. The numbers told the truth.
To make that happen, teams mapped out every possible way things could go wrong. They wrote detailed step-by-step instructions, set up clear chains of command, and built tools to spot issues before anyone watching caught on. The old days of everyone running around, trying to fix things without a plan? Over. Now, automated systems alerted the right people right away, and everyone knew their role, so no one got in each other’s way.
This shift completely changed how streaming companies viewed reliability. Outages stopped being random disasters. They became patterns to spot, plan for, and eliminate. With the right tools and plans, many issues were fixed before anyone saw an error. If something did go wrong, teams knew exactly what to do, who to contact, and how to make sure it didn’t happen again.
Building this system wasn’t easy. Teams spent months mapping out every possible failure, running drills, and stress-testing their plans. But it paid off. The new system handled emergencies calmly, reduced downtime, and made the experience smoother for everyone.
And the results? They spoke for themselves. The platform reached 99.99% uptime—viewers noticed, and so did the company’s bottom line. In a crowded market, it’s not enough to have great shows. People expect their shows to play, every time. The new system delivered that. Major outages dropped dramatically. Problems that used to last hours were now fixed in minutes, often before viewers even knew there was an issue. Teams cut response times from hours to minutes, and the confusion, wasted effort, and finger-pointing that once slowed everything down all but disappeared.
Once other companies saw what was happening, they joined in. Partners and operations teams noticed the improvements in reliability and the cost savings, so they began adopting the same approach. Aniruddha Vyawahare summed it up: “We went from crashes lasting hours and affecting everyone to fixing most problems in minutes, often before anyone even noticed.” It wasn’t only about solving technical issues. The whole attitude changed, and suddenly everyone cared much more about keeping everything running smoothly.
The framework quickly became the go-to solution for tackling major problems throughout the company. Teams stopped waiting for things to break—they started preventing issues before they happened. People even began using the same organized method for customer support and content delivery. Everything just ran better.
But it didn’t end there. Other streaming services noticed as well. They saw that keeping things running smoothly was just as important as creating great shows. That was what kept viewers coming back. The approach started guiding bigger decisions—how to design core infrastructure, how to collaborate with creators, even how to shape business strategy. Reliability was no longer just an IT issue. It became a main focus.
You can see the change across streaming now. The industry has shifted from reacting to crashes to actually preventing them. That’s significant. Viewers aren’t patient with interruptions, and streaming services have to keep things running for millions of people, on every kind of device and connection, all over the world.
This organized system gave streaming platforms a real advantage. Companies that kept their services running while others stumbled didn’t just keep their viewers—they built stronger ties with content partners and protected their reputations. The influence kept spreading, raising standards for reliability everywhere. Both viewers and creators benefited—they could trust the service to work.
What made the system successful? It wasn’t complex. It replaced panic with preparation. Even the hardest problems stopped feeling overwhelming when there was a plan. And it wasn’t only for the big companies—start-ups could do it too. Teams focused on results they could measure, and that’s how they made real, lasting progress.
Now, as streaming becomes even more crowded, reliability is what separates the winners from everyone else. People expect flawless service, and if something fails, they can switch instantly. This framework gives companies a straightforward way to meet those high expectations. It turned reliability from a background task into a true competitive edge.
And it’s not limited to streaming. Other sectors—like online retail and cloud computing—are beginning to use the same kind of system. Solving problems, making clear plans, tracking what works: these ideas are spreading quickly. It turns out, this approach can change how any business thinks about reliability.
Aniruddha put it best: “The system didn’t just fix tech problems. It changed how the whole company thinks about keeping things running.” That’s the new standard. With a focus on preparation and clear, simple results, this framework proves even the biggest challenges can be handled—and that’s great news for companies and their customers.
