Enterprises today are under increasing pressure to extract value from their data — not just for reporting, but to power real-time decision-making, personalization, and AI innovation. Yet for many large organizations, legacy infrastructure stands in the way. Systems built for transactional processing, not analytics, are still deeply entrenched in core operations, making it difficult to evolve toward a unified, insights-driven model.
To compete in today’s data economy, companies must rethink their foundational data architecture. That means moving beyond outdated, transactional OLTP systems and embracing analytics platforms designed for scale, flexibility, and speed. This modernization isn’t simply about faster performance — it’s about enabling new capabilities across the business, from automated workflows to intelligent forecasting.
In this blog, Parag Shah, a seasoned data leader who has led transformation efforts at companies like Rocket Software and CarGurus, shares how modern platforms, purposeful vendor selection, and extensibility through tools like the Fivetran Connector SDK are helping enterprises bridge the gap between legacy systems and next-gen analytics.
Modernizing the legacy stack: From OLTP to unified analytics
Legacy systems are deeply embedded in many large enterprises and can present real roadblocks as organizations aim to become more data-driven. Designed for transactional processing, not analytics, many legacy databases are simply not equipped to support today’s AI, machine learning, and real-time decision-making workloads.
Shah, reflecting on his experience at Rocket Software and beyond, explains that for too long, enterprises tried to force-fit modern analytics use cases into outdated OLTP systems. The result was limited visibility, missed opportunities, and the inability to tap into growing volumes of semi-structured and unstructured data.
This necessitated a shift from transactional databases to modern analytics platforms like columnar data warehouses — systems that are purpose-built for aggregate functions and large-scale data workflows. This transition has been accelerated by the cloud, but this evolution isn’t just about performance. It’s about unifying all data types — structured, semi-structured, and unstructured — into a single, scalable analytics platform, resulting in simpler architectures, faster insights, and greater agility.
Successful data modernization starts with buy-in and focus
Modernizing the data stack is more than a technical upgrade — it’s an organizational shift. And according to Shah, it starts with one key step: buy-in. “When I think about a modernization effort, the first thing I think of is getting buy-in up front, right? You have to explain the value of why you're trying to modernize your data stack,” he explains. That means clearly communicating the business value of modernization, especially to stakeholders who may not yet see the gaps in their current visibility or capabilities.
A successful strategy doesn’t just outline the technical tools — it frames the initiative in terms of the insights it will unlock and the operational improvements it will drive. Shah emphasizes the importance of transparency when it comes to timelines and technology choices. Leaders need to articulate not only what the modernization effort entails, but why specific platforms are being selected and how long it will take to realize value.
When it comes to choosing vendors, Shah’s philosophy is straightforward: favor specialization. Having implemented platforms like Snowflake and Fivetran across multiple companies, Shah has seen firsthand the value of choosing tools that are optimized for a singular function — rather than spread thin across multiple domains.
He likens this approach to the automotive industry: if you’re serious about electric vehicles (EVs), you’re more likely to get cutting-edge innovation from a company built exclusively around EVs than from a legacy automaker juggling internal combustion, hybrid, and electric platforms. The same principle applies to software. Vendors focused on one core function are often more agile, more innovative, and better equipped to deliver long-term value.
Unlocking data with custom connectors and community input
As organizations become increasingly data-driven, the ability to integrate a wide range of data sources — including niche or proprietary systems — is essential. That’s where Fivetran’s Connector SDK comes in.
At CarGurus, Shah’s team used the SDK to build a more advanced GitHub integration tailored to their specific needs — surpassing even the capabilities of Fivetran’s standard connector. “We were able to use our custom SDK to get some of the fields that we were looking for and get some of the functionality that we were looking for that wasn't there.”
Initially developed to address the limitations of traditional integration approaches, the SDK gives data teams a powerful tool to extend the platform’s 700+ prebuilt connectors to virtually any data source. And with the Fivetran Connector SDK, custom-built connectors are treated like native Fivetran connectors: they fall under the same support umbrella, are managed automatically, and integrate seamlessly into the broader data movement framework.
Ultimately, the Connector SDK democratizes the process of data integration. For organizations with complex ecosystems, long-tail tools, or specialized requirements, it’s a way to eliminate integration barriers without sacrificing automation, support, or scale. As Shah puts it, “There's thousands upon thousands of software tools out there. Nobody's ever gonna build a connector to all of them.” But with Fivetran Connector SDK, you can.
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