A quiet revolution is occurring in the data industry. While organizations work on modernizing how they do business, their aging data warehouses are struggling to meet the demands of real-time use cases. Traditional platforms that used to flourish on SQL Server and Oracle can’t keep pace with the demands of immediacy. Many organizations are coming to terms with the fact that cloud-first intelligent data platforms aren’t just a tech upgrade—they’re a business imperative.
Urvang Kothari is a data engineering professional at the forefront of this transition, and his experience has helped many industries see new possibilities with their data. For the last six years, he has helped revolutionize organizations across various sectors, including modern-day gaming, dairy, mining, and utilities, by transitioning from legacy applications to AI-enabled analytics. He has helped organizations see beyond ingesting data and into using it for intelligent purposes.
“The future of enterprise data isn’t just about storing it anymore,” says Kothari. “It’s about creating smart platforms that can understand data in real time, anticipate what’s coming next, and help businesses move faster and smarter.”
Walk into any enterprise IT department, and you’ll hear the same frustrations. Data teams spend weeks preparing reports that executives need today. Analytics projects stall because systems can’t handle the load. Business units work with outdated information because real-time processing isn’t possible.
Kothari has seen this struggle firsthand across multiple organizations. “Traditional systems often imposed rigid capacity limits and created data silos,” he reflects. “Teams would tell me they needed three days just to run a basic analysis that should take minutes.”
The human cost is real. Data engineers burn out trying to patch together incompatible systems. Business leaders are ultimately making decisions with stale information. Organizations do not lose competitive advantage because they don’t have the data to make informed decisions, but rather because they can’t effectively use data. “Where legacy systems had walls around the data, Cloud systems break down the walls and allow for rapid and secure access to trusted data,” Kothari notes.
The kind of transformation our expert leads is not about simply moving data to a Cloud Data Warehouse; it is about flipping how organizations think about information. Modern platforms like Snowflake, Google BigQuery, and Azure Data Factory do not simply store data; they unchain data.
Kothari’s biggest impact is in the architectural transformation of legacy systems to modern Cloud-native ecosystems. He has led complex migration experiences with SQL Server, Oracle, and MongoDB to Snowflake, Google BigQuery, and Azure Data Factory and has been successful in building end-to-end solutions that scale with the needs of organizations.
In the gaming industry, for instance, he led the shift from SQL Server to GCP BigQuery. With Python and Cloud Composer, he built pipelines that integrate real-time IoT data to analyze player behavior and enable live personalization. “We moved from fragmented batch processing to real-time personalization—players now get experiences tailored to their behavior within seconds,” he explains.
The work didn’t just modernize systems—it delivered tangible results. Across multiple projects, Kothari helped reduce data delivery times by up to 60% and cut infrastructure costs by nearly 35%. In utilities and manufacturing, his cloud-first ELT architectures significantly boosted forecasting accuracy and minimized operational downtime by 20%.
Kothari’s focus isn’t purely technical—it’s deeply strategic. He works across cross-functional teams to ensure data modernization aligns with broader business goals. At several organizations, his data platforms became the backbone of operational decisions, enabling real-time reporting and predictive maintenance.
“One of the biggest wins was helping a dairy company automate its compliance reporting and maintenance planning,” he notes. “Using Azure Data Factory, we connected sensors and historical data to forecast equipment failures before they happened. That meant less downtime and more confidence in their operations.”
By merging batch data, streaming analytics, and IoT inputs, Kothari enabled unified platforms that are both intelligent and actionable. His implementations are governed, secure, and built for the long haul, with CI/CD pipelines through GitHub and version-controlled deployments ensuring repeatability and resilience.
Modernizing infrastructure is rarely just about the code. One of his most impressive strengths is navigating the human side of transformation. He’s faced significant resistance—from teams unsure about cloud security to leaders hesitant to abandon legacy investments. “Convincing stakeholders isn’t always easy,” he admits. “But when you show the cost savings, speed, and strategic advantage, the conversation shifts.”
Kothari addresses resistance with data-backed results, comprehensive training, and a clear roadmap. His leadership ensures that cloud-first isn’t seen as a technical risk, but as a competitive edge.
“Cloud platforms today don’t just store data—they power intelligent decisions,” he says. In manufacturing, for example, his solutions improved forecasting by up to 25%, directly impacting inventory management and resource planning.
The ecosystems being built today are intelligently designed to enable experimentation, automation, and continuous optimization. They’re not just built for today’s demands but are fundamentally future-ready: architected to evolve alongside innovation.
As industries push to become truly data-first, the focus must shift beyond tools to culture, says Kothari. “It’s not just about adopting cloud platforms—and of course, each comes with its own nuances. What really matters is building a data-first mindset,” he explains. That means investing in training, establishing strong governance, and viewing modernization not merely as a tech upgrade but as a comprehensive business transformation.
His recent publication, AI Agents in Data Engineering: Revolutionizing Data Management (2025), explores how cloud-first platforms are becoming intelligent backbones for AI-powered enterprises. Other works, like How Apache Iceberg Outperforms Traditional Table Formats, reflect his depth in both architecture and application.
Kothari’s vision is clear: intelligent cloud-first platforms will not only redefine how data is stored, but how it’s used, shared, and monetized. His advice for future-facing organizations is both practical and powerful: “Start small but think big. Align technology with business value. And most importantly, treat data as a living, strategic asset.”
In a world that’s drowning in data but starving for insight, professionals like Urvang Kothari are charting the path forward, where intelligence, not just infrastructure, defines the enterprise edge.