When most teams talk about going real-time, what they really mean is: “we want speed without tradeoffs.” But speed, especially in data systems, is an illusion if the foundations are brittle. Dashboards can refresh every second, APIs can respond in milliseconds, but if the underlying pipelines are inefficient, uncoordinated, or unmonitored, velocity becomes vanity.
For Balakrishna Aitha, Lead Data Engineer at Macy’s, and Judge at the Globee® Awards for Technology- real-time is not just a checkbox, but a system capability which is earned through orchestration, layered architecture, and engineering maturity. Across more than a decade of hands-on experience in backend systems, cloud-native pipelines, and data infrastructure, he has come to see performance not as a frontend phenomenon but a backend obligation. His current work on Google Cloud Platform, spanning BigQuery, Dataflow, Pub/Sub, and Cloud Composer, goes far beyond ingestion. It is about stability under pressure, cost-aware scaling, and the quiet discipline of building pipelines that do not just work once, but work always.
Beyond the Ingest: What Streaming Systems Actually Demand
The modern retail engine does not sleep. Inventory signals shift every few minutes. Customer behavior loops trigger personalization models at scale. Pricing updates travel upstream and downstream within seconds. The pressure to operate in this high-frequency environment has made stream-based architectures an attractive proposition. But according to Balakrishna, many teams adopt streaming for the wrong reasons, or implement it without appreciating the architectural debt that comes with it.
“Streaming is not speed. Streaming is a responsibility,” he explains. Systems built on Pub/Sub and Dataflow may seem reactive and fluid on the surface, but sustaining them requires engineering depth: handling message deduplication, preserving ordering guarantees, managing backpressure, enforcing idempotency, and debugging intermittent delays. The system must not only emit quickly; it must fail predictably.
These challenges force architectural trade-offs. For example, streaming architectures can offer near-instant insights but often require significant operational overhead and maintenance to manage state, consistency, and ordering. Batch systems, in contrast, are generally easier to validate and cost-optimize but lack immediacy. Balakrishna frequently designs hybrid models, using streaming for real-time personalization during online shopping sessions, while relying on batch systems for nightly financial reconciliations. The trade-off is clear: throughput and immediacy versus traceability and consistency.
“There is no perfect mode,” Balakrishna says. “Every decision comes down to risk tolerance and the context of use.”
Streaming cannot be viewed as a wholesale replacement for batch, it is a strategic complement. Throughput must be weighed against fault tolerance, and sometimes a few seconds of delay is the tradeoff for correctness.
Designing for Failure: Making Real-Time Durable
In retail, the stakes are material. A pricing rule applied out of order, an inventory event dropped in transit, or a customer action double-counted can all cascade into loss, financial and reputational. Balakrishna’s design approach involves layering safety mechanisms, validating payload schemas, and separating mission-critical flows from experimentation pipelines. This attention to operational integrity transforms streams from risky abstractions into dependable business infrastructure.
“You do not get reliability by accident. You get it by expecting failure and designing with it in mind,” Balakrishna explains. Streaming systems often introduce complexity in monitoring, rollback design, and state management that is underestimated during initial architecture. Balakrishna counters this by defining narrow SLAs, tuning Pub/Sub topic retention with precision, and documenting fallback behavior.
His philosophy of engineering accountability was reinforced during his time as a Session Judge at the Washington Hackathon held from May 24–25, 2025 at Washington University of Science and Technology. Reflecting on the experience, he shared, “What I look for is not who writes the fastest API. It is who can explain how they will monitor it, evolve it, and debug it two months later.” Whether designing production pipelines or evaluating emerging talent, his focus remains the same: build systems that earn their speed through transparency, maintainability, and architectural discipline.
Storage, Orchestration, and Queryability: The Full Velocity Stack
What gets less attention in most velocity conversations is everything that comes after the stream. Balakrishna emphasizes that the real measure of pipeline health is not ingestion latency but end-to-end system observability: can we trust the number on the dashboard? Can we audit it? Can we explain it?
BigQuery plays a central role in Macy’s analytics backbone, but its value hinges on how data is staged and partitioned before query time. “Data that lands fast but reads slow is not a win,” Balakrishna notes. He optimizes schema designs, clustering strategies, and storage tiers to ensure both retrieval performance and cost discipline.
Meanwhile, Cloud Composer serves as the orchestration layer, managing complex DAGs, synchronizing batch and stream jobs, and alerting teams to drift or data freshness issues before they become blind spots. Orchestration is not an afterthought, it is the connective tissue that ensures velocity does not degrade into chaos. Broken DAGs, left unnoticed, can stall entire workflows, introduce silent errors into downstream dashboards, or even trigger erroneous business decisions. Balakrishna notes, “One broken dependency in a DAG can cascade into hours of invisible failure if you are not watching.”
According to a Google Cloud benchmark at Yahoo, Dataflow pipelines outperformed Apache Flink by 1.5–2× in cost efficiency for real-time stream processing workloads. Similarly, an InfoWorld report noted that Dataflow beat Apache Spark by 2–5× in batch scenarios with aggregation-heavy operations while offering more granular resource scaling and monitoring. These performance wins are not just academic, they translate into real operational savings when building pipelines at scale. Balakrishna’s architecture minimizes waste by enforcing lifecycle rules, enabling dynamic scaling, and embedding fail-safes into workflow logic.
Engineering Maturity as the Benchmark for Data Velocity
In the race to modernize analytics, most organizations default to tools. But Balakrishna’s career underscores that the toolset is only as effective as the mindset. Streaming, in his view, is not about reacting quickly, it is about building slowly, with intention, so that the system can respond reliably.
As retailers increase their investment in AI-driven personalization and automated decisioning, the need for dependable, low-latency data infrastructure will only grow. According to a Gartner forecast, by 2026, organizations that operationalize AI transparency, trust, and security will see their AI models achieve a 50% improvement in terms of adoption, business goals, and user acceptance. Additionally, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number and time it takes to operationalize AI models by at least 25%.
But the winners will not be those who chase speed blindly. They will be the engineers and architects who understand that data velocity is not just a measure of movement, but a reflection of architectural responsibility.
