From Batch To Streaming: Data Engineering’s New Normal

Cloud adoption has changed the cadence of enterprise data work. Where nightly extracts once satisfied reporting teams, today’s retailers and utilities want immediate signals on inventory, generation capacity, and customer sentiment. The shift to real-time demands new tooling—object storage instead of tape archives, serverless services instead of monoliths—and disciplined engineering habits.

That context led me to the work of Praveen Kumar Dora Mallareddi, an engineer who has spent a decade translating database craft into cloud-native practice. His career, spread across energy, insurance, and retail, illustrates how mature data pipelines can deliver tactical value without sacrificing governance. 

Building Resilient Pipelines: Insights from Praveen Kumar Dora Mallareddi

When I reached Mallareddi at his home office in Texas, he described his role in simple terms: “I keep the information moving so decisions stay honest,” he said. After early years debugging Oracle packages, he pivoted toward AWS and Python, earning two AWS Machine Learning certifications and mastering Boto3.

At NextEra Energy he led a four-person offshore team that migrated five years of solar-asset telemetry into Amazon S3 and Redshift, feeding predictive models that optimize maintenance windows. The migration replaced a snowflake schema with a star model, reducing query latency by 40 percent and giving data scientists a single fact table for revenue, loss, and capacity metrics. 

His current post with a leading national retailer pushes the envelope further. Tasked with automating assortment analytics, he built a change-data-capture pipeline that lifts millions of rows from on-prem Oracle into a managed Postgres instance on Google Cloud, then triggers PySpark jobs that refresh demand forecasts every hour. “Parallel processing used to be a luxury,” Mallareddi noted, “now it’s the baseline for meeting shelf-replenishment targets.”

He complemented the pipeline with dashboards that alert operations staff when API throughput degrades, cutting manual surveillance time by half. Earlier, at Florida Power & Light, he wrote Flask-based services that exposed KPIs via REST so that field technicians could query turbine health from a mobile device. Those services relied on disciplined PL/SQL, partitioned fact tables, and list-agg analytics—proof that legacy skills still matter inside cloud programs. 

Observing Craft Up Close: A Journalist’s Notebook

Spending several afternoons with Mallareddi’s code reviews revealed a habit of incremental rigor rather than headline-grabbing rewrites. He documents every Airflow DAG in Confluence, labels object-storage paths with lifecycle policies, and resists deploying a new Lambda until test harnesses cover edge cases. The approach echoes his own words: “Good data engineering is boring in the best way—repeatable, observable, and cheap to rerun.” 

Watching him coach recent graduates underscored that point. During an onboarding session he walked a junior engineer through Matillion’s version-control plugin, pausing to explain why each transformation should log row counts before and after deduplication. He compared the discipline to aviation pre-flight checks—nothing glamorous but essential to safety.

In daily stand-ups he peppers conversations with gentle questions: “Which partition key will age fastest?” or “How will this CDC job behave on February 29th, 2028?” The queries turn abstract backlog tickets into concrete safeguards. One mentee told me Mallareddi’s insistence on idempotent loads saved an overnight batch when an upstream vendor resent duplicate files. Mallareddi’s value also lies in translation between business and technical dialects.

During a planning workshop, merchandising leaders asked why model refreshes could not be instantaneous. He sketched the trade-off on a whiteboard, mapping data ingestion latency against compute spend. Within minutes the team agreed to a fifteen-minute SLA that balanced accuracy with budget. His ability to mediate such discussions stems from a postgraduate diploma focused on AI and machine learning applications—coursework that lets him challenge data-science assumptions without toppling collaboration dynamics. 

Why Cloud Discipline Matters for the Next Decade of Retail Data

Mallareddi’s trajectory mirrors a broader industry mandate: move fast—yet prove lineage, security, and cost restraint. Retailers experimenting with generative-AI demand signals soon learn that without clean, timely, governed facts, models drift and trust erodes.

Engineers like Mallareddi demonstrate that the answer is neither a wholesale rip-and-replace of SQL traditions nor blind faith in new services. Instead, success arrives through small, composable wins: a CDC function here, a partition strategy there, an alert tuned to the right percentile. Those wins accrue, delivering the holy grail of analytics—fresh, reliable data that shows up before the board meeting ends. 

Looking ahead, Mallareddi is prototyping an event-driven ingestion layer that will let category managers test promo hypotheses in near real-time. He envisions a feedback loop where streaming metrics flow back into Snowflake-style warehouses and, eventually, into reinforcement-learning models that fine-tune store layouts daily.

“The tooling will evolve,” he told me in our closing conversation, “but the fundamentals—respect for data quality and clarity—will stay exactly the same.” That philosophy may be the quiet engine powering the next wave of cloud discipline.