Real-Time Analytics: Stream Processing for Decisions That Can't Wait
Batch processing runs overnight. Your fraud detection system, inventory tracker, and operational dashboard need answers in seconds. Real-time data engineering is the infrastructure that makes sub-second analytics possible — and it requires a fundamentally different skill set than traditional batch ETL.
Event Streaming
Kafka, Event Hubs, Eventstreams — ingesting millions of events per second
Stream Processing
Spark Structured Streaming, Flink, KQL — transformations on data in motion
Real-Time Dashboards
Sub-second refresh dashboards for operations, finance, and IoT monitoring
Event-Driven Architecture
Triggers, alerts, and automated actions based on data patterns in real time
Batch analytics tells you what happened yesterday. Real-time tells you what's happening now.
Most enterprise data platforms are built for batch processing — extract data overnight, transform it, load it into a warehouse, and serve it to dashboards in the morning. That cadence works for monthly financial reports. It fails catastrophically for fraud detection, real-time pricing, operational monitoring, and any use case where the value of data decays with time.
Real-time data engineering isn't a faster version of batch ETL. It's a fundamentally different architecture: event-driven messaging, stream processing frameworks, stateful transformations, windowing functions, watermarking, and exactly-once delivery guarantees. The skill set barely overlaps with traditional batch data engineering — which is why streaming specialists are among the scarcest roles in the data talent market.
Xylity matches streaming engineers who've built production event-driven systems — not batch engineers who've done a Kafka tutorial. Through our consulting-led matching, we verify real-time architecture experience: throughput handling, state management, failure recovery, and late-arrival semantics.
Real-time data engineering capabilities
Every streaming engagement is led by engineers with production experience in event-driven architectures — matched to your specific platform, throughput requirements, and use cases.
Event Streaming Architecture
Design and implementation of event streaming infrastructure using Apache Kafka, Azure Event Hubs, or Fabric Eventstreams. Topic design, partitioning strategy, schema registry, consumer group management, and throughput optimization for millions of events per second.
Stream Processing & Transformation
Spark Structured Streaming, Apache Flink, or KQL-based stream processing for real-time transformations: windowed aggregations, sessionization, pattern detection, and enrichment from reference data. Stateful processing with exactly-once semantics.
Real-Time Dashboards & Monitoring
Operational dashboards with sub-second refresh for IoT monitoring, financial trading, logistics tracking, and production line oversight. Built on Power BI real-time streaming, Fabric KQL dashboards, Grafana, or custom visualization layers.
Event-Driven Automation
Automated triggers and actions based on real-time data patterns: fraud alerts, inventory reorder triggers, SLA breach notifications, anomaly detection alerts. From detection to action in seconds, not hours.
Change Data Capture (CDC)
Real-time replication from transactional databases (SQL Server, PostgreSQL, Oracle) to analytical platforms using Debezium, Azure CDC, or platform-native change tracking. Keep your data platform in sync without batch delay.
See data integration →Lambda & Kappa Architecture
Hybrid architectures that combine batch and streaming layers for the best of both worlds — or unified kappa architectures that simplify the stack by processing everything as a stream. Architecture selection based on your latency, cost, and complexity trade-offs.
Technologies we build with
Apache Kafka
Event streaming platform, Kafka Connect, Schema Registry, ksqlDB
Fabric Real-Time
Eventstreams, KQL databases, Real-Time Intelligence, Reflex triggers
Spark Streaming
Structured Streaming on Databricks, Fabric, or Azure HDInsight
Apache Flink
Stateful stream processing, event-time semantics, complex event processing
Azure Event Hubs
Cloud-native event ingestion, capture to storage, Kafka compatibility
Debezium CDC
Change data capture from PostgreSQL, SQL Server, MySQL, Oracle, MongoDB
Grafana / Prometheus
Real-time monitoring dashboards, alerting, metrics aggregation
AWS Kinesis
Data Streams, Data Firehose, Analytics — serverless stream processing
Streaming engineers matched to your throughput and latency needs
Streaming Assessment
We map your real-time use cases, throughput requirements, latency targets, and existing infrastructure. The matching starts from your specific streaming needs.
Specialist Matching
Streaming engineers matched for your specific platform (Kafka, Fabric, Flink) and use case (IoT, fraud, operational monitoring). Production throughput experience verified through scenario assessment.
Architecture & Build
Event streaming infrastructure, stream processing pipelines, and real-time dashboards built with production-grade error handling, state management, and exactly-once delivery guarantees.
Scale & Optimize
Performance tuning for throughput and latency targets, cost optimization, monitoring setup, and knowledge transfer. Your streaming infrastructure is operational and your team owns it.
Streaming expertise for enterprises and IT services companies
Use cases that demand real-time but a data team built for batch?
Fraud detection, IoT monitoring, real-time pricing, and operational dashboards all require fundamentally different engineering than traditional batch analytics. Xylity matches streaming architects who've designed and operated production event-driven systems — engineers who understand Kafka internals, stateful stream processing, and exactly-once semantics, not just batch engineers who've watched a tutorial.
Start a Consulting Engagement →Client needs streaming engineers but your bench is batch-oriented?
Real-time data engineering is a niche specialty — most data engineers are batch-first. When your client's project requires Kafka architects, Spark Streaming developers, or Flink specialists your bench doesn't have, Xylity's network delivers curated streaming profiles from partners who specialize in event-driven systems. First profiles in an average of 4.3 days.
Scale Your Streaming Delivery →Real-time analytics — answered
Your real-time use cases deserve
engineers who've built streaming systems before.
Tell us about your streaming use case, throughput requirements, and latency targets. We'll match engineers with proven production experience in event-driven architectures.