Backend Engineering for Big Data Applications: Java, Scala, and Python Compared

As enterprises continue to modernize their data systems, professionals like Ujjawal Nayak have quietly been making some of the most meaningful strides behind the scenes. Currently serving as a Software Development Manager at Experian, Nayak has played a central role in overhauling legacy systems, improving infrastructure efficiency, and advancing the company’s backend architecture through a combination of big data and cloud-native technologies.

Reportedly, Nayak’s promotion came on the back of several high-impact projects, including the migration of data workloads from Amazon Redshift to Snowflake. This move, described by team insiders as a “significant architectural leap,” resulted in approximately 30% reduction in operational costs and brought about measurable gains in scalability and data performance. Much of this transformation has been detailed in his recent paper, “Migrating Legacy Data Warehouses to Snowflake,”where Nayak outlines a practical roadmap for large enterprises looking to transition to cloud-native data warehouses while minimizing disruption.

One of Nayak’s major initiatives involved reengineering critical ETL pipelines using a mix of Java, Scala, and Python a combination selected to address both performance needs and development agility. The shift also included dismantling monolithic data processing structures in favor of a more modular, fault-tolerant framework. His work in this area culminated in another technical paper, “Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake,”which emphasizes real-world patterns for constructing resilient, cloud-ready data ingestion systems.

Coming from the expert’s table, Nayak explained, “In backend engineering, there’s no one-size-fits-all. You have to look at how each tool serves a particular use case. Java gives us stability, Scala adds concurrency benefits, and Python offers speed where we need it.”

Additionally, he was instrumental in migrating distributed AWS Lambda functions originally written in Java to more maintainable Apache Airflow DAGs. This transition not only improved observability but reportedly led to a 50% reduction in pipeline failures, allowing operations teams to intervene less frequently and with greater accuracy.

Beyond infrastructure modernization, Nayak also focused on operational reliability. He led the development of automated alerting systems written in Python, which have since been integrated into Experian’s monitoring stack. According to internal metrics, this system improved operational transparency and response times by about 40%, particularly during periods of high data activity.

Furthermore, Nayak took charge of designing a bi-coastal disaster recovery plan for Experian’s Audience Engine platform a critical component of the company’s advertising data operations. The implementation reportedly allowed the platform to maintain continuity during outages without compromising regulatory compliance, including mandates from the FCRA and CCPA.

Colleagues familiar with Nayak’s work describe him as a hands-on leader who bridges the gap between engineering execution and long-term strategy. “He doesn’t just delegate he codes, he reviews, he architects,” said one team member. “His ability to operate across Java, Scala, and Python is rare, and it’s been crucial to getting some of our tougher projects off the ground.”

In earlier roles, including at Impetus, Nayak made a similar transition from a Java developer to a full-fledged big data engineer working across different ecosystems and data platforms. This cross-functional experience has proved vital in his current responsibilities, where interoperability between systems is a daily challenge.

Looking forward, Nayak believes that backend infrastructure will increasingly be shaped by real-time data requirements and AI-driven insights. “We’re seeing a shift toward event streaming and more granular data observability,” he noted. “The challenge is maintaining speed and accuracy without inflating complexity.”

Furthermore, he anticipates more seamless integration across programming ecosystems. “There’s a growing push toward frameworks that let Java, Scala, and Python co-exist more naturally in cloud-native environments. That’s going to unlock new possibilities in how teams collaborate and build.”

In addition to his technical contributions on the ground, Nayak has added to the broader engineering dialogue through his published writings. Both of his papers “Migrating Legacy Data Warehouses to Snowflake” and “Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake”offer engineers and architects detailed insights into building efficient, scalable backend infrastructure in enterprise contexts.

While the work of backend engineers often remains behind the curtain, Ujjawal Nayak’s contributions at Experian highlight the essential role of technical leadership in navigating data modernization. Through thoughtful system design and a pragmatic approach to technology integration, he has helped lay the groundwork for infrastructure that is not only faster and cheaper but also better aligned with the complex needs of today’s data-driven businesses.