The market for data integration tools is projected to rise from $10.5 billion in 2024 to $20.3 billion by 2030. That growth reflects a familiar enterprise problem: customer information is scattered across products, event streams, service databases, and operational tools, while the people and AI systems using it expect one current answer.
Bharat Patel, a Staff Software Engineer, has spent years solving that challenge by building large-scale customer data platforms that unify fragmented information into trusted, real-time profiles. His recognition as an IEEE Senior Member reflects work focused on engineering systems that combine scale, reliability, and responsible data governance. His perspective is practical: organizations do not gain value by collecting more data, but by making existing data continuously usable. To understand what it takes to build AI-ready customer platforms, we spoke with Patel about the engineering decisions behind them.
Make Every Source Follow a Repeatable Path
“I designed config-driven Flink streaming pipelines, one per data source, with reusable processing stages that could be changed through configuration without redeploying. That enabled rapid onboarding of new data sources,” Patel explains.
That work addressed the ingestion problem first. In 2024, 86% of surveyed IT leaders identified data streaming as a strategic or important investment priority, while 95% said streaming systems improve access to the diverse data needed for modern analytics and AI. Enterprises rarely struggle to collect information. The harder challenge is continuously absorbing it from hundreds of different systems without building custom engineering for each one.
Patel helped architect a large-scale customer data platform built on Apache Flink, creating standardized streaming pipelines for every incoming data source. Each pipeline followed the same reusable processing model, validating incoming records before performing transformation, enrichment, and deduplication. Rather than requiring new code for every source, teams could alter pipeline behavior through configuration alone, changing processing stages without redeploying services.
The approach supported more than 500 data sources while processing over 8 billion events each day with write latency below 100 milliseconds. By moving common logic into configuration instead of application code, new integrations became operational exercises instead of software development projects.
Treat Production Operations as Part of the Product
Once ingestion becomes repeatable, the next challenge is keeping it dependable as systems evolve. Kubernetes adoption has reached 93% when production deployments, pilots, and evaluations are combined, with 80% of organizations already running Kubernetes in production. Large streaming systems are ultimately judged not by normal operation, but by how safely they handle failures, configuration changes, and traffic spikes.
Patel recalls one architecture review where what appeared to be a simple configuration change revealed a surprisingly large operational impact once engineers traced every downstream dependency. That discussion reinforced an important principle: production configuration deserves the same engineering discipline as application code, including testing, staged deployment, and rapid rollback.
“I engineered production-grade operations through Kubernetes pod autoscaling and canary deployments for both API services and pipeline configuration changes,” Patel explains. “I also built scalable multi-region failover spanning DynamoDB, Kafka, and the API layer.”
That architecture provided resilience across the entire data path, allowing localized failures to remain isolated instead of disrupting every downstream consumer. Systems processing billions of daily events must be designed not only to run continuously but also to evolve safely.
Build One Profile That Products Can Actually Use
Operational resilience creates the conditions for unified data, but it does not guarantee it. By late 2024, only 57% of surveyed organizations reported maintaining a unified customer database, while 68% had implemented some form of customer data platform. Building one trusted customer profile remains considerably harder than connecting systems together.
Patel helped build unified customer profiles representing individuals, businesses, and anonymous visitors within a centralized platform. Rather than relying on periodic batch synchronization, profile updates immediately triggered real-time Kafka events so downstream applications could respond as soon as customer information changed.
The result was a continuously updated profile that supported customer service, personalization, and analytics from the same authoritative source.
The benefits appear in everyday interactions. Customer support representatives no longer need to reconcile conflicting records across multiple systems, while personalization engines can make decisions using current information rather than stale snapshots. The platform succeeds because it removes operational friction without requiring users to think about where the underlying data originated.
Use GraphQL as a Controlled Access Layer
A unified profile still requires an interface flexible enough for many different consumers. GraphQL adoption is projected to grow from under 30% of enterprises in 2024 to more than 60% by 2027 because it allows applications to retrieve exactly the information they need through a strongly typed interface instead of depending on expanding collections of REST endpoints. Greater flexibility, however, also demands stronger governance.
Patel designed a GraphQL API over the unified profile platform while maintaining read latency below 50 milliseconds. The interface enforced attribute-level permissions for every consumer, evaluated customer consent during query execution, and supported carefully audited override paths for legally authorized use cases. Independent application- and service-level throttling further protected platform stability.
“The controlled non-consent override path provides an auditable, governed mechanism for legally authorized use cases such as fraud detection and regulatory compliance, avoiding ungoverned backdoors,” Patel explains. “Asset-based access control provides the fine-grained, auditable data-access records that privacy regulations increasingly require.”
The result was flexible data access without separating privacy enforcement from the application itself.
Let Natural Language Become the Next Interface
The next evolution is making enterprise data not only accessible but conversational. By 2028, one-third of enterprise software applications are expected to incorporate agentic AI, while the global customer data platform market is projected to reach $85.18 billion by 2032. As AI assistants become everyday users of enterprise data, the underlying information architecture must remain consistent regardless of whether requests originate through APIs or natural language.
Patel extended the platform by building a Natural Language Query layer on top of the GraphQL interface. Rather than requiring users to understand the platform’s schema, the system translates plain-English requests into structured GraphQL queries while preserving the same governance, permissions, and consent enforcement already built into the platform.
He describes the implementation directly. “Incoming plain-English queries are vectorized, matched via similarity search against customer-profile metadata in a vector database, and automatically translated into GraphQL queries. The Natural Language Query layer is live in production, allowing customer support experts to retrieve profile information in real time using plain English.”
That progression reflects Patel’s broader philosophy. Organizations should first build a reliable, governed data foundation before layering new AI capabilities on top of it. When customer data is unified, observable, and securely governed, new interfaces become additions rather than redesigns. The most valuable AI systems are ultimately built on platforms that already make trusted information available at scale.
