Real-Time Data Streaming That Delivers Insights in Seconds
Numlytics builds production-grade real-time data streaming pipelines for enterprises across the US, UK, Australia & UAE. Apache Kafka, Azure Event Hubs, Apache Flink, and Spark Streaming - designed for sub-second latency, fault tolerance, and millions of events per second. When batch ETL isn't fast enough, we build the streaming data architecture that closes the gap.
event to dashboard
handled in production
across all deployments
engineering firms
When Batch Is Too Slow,
You Need Streaming
Batch pipelines are the right answer for most data workloads.
But some decisions can't wait for the nightly run.
Fraud detection that alerts after the transaction has cleared.
Operational dashboards that show yesterday's inventory.
Customer experience systems reacting to events that happened
two hours ago. When the latency gap between data and decision
is costing you money, real-time data streaming
closes it.
Our real-time data streaming service designs
and implements the event-driven architecture your use case
requires, from simple CDC-to-dashboard pipelines to
complex streaming data architectures
processing millions of events per second with sub-second
end-to-end latency. Built on Apache Kafka, Azure Event Hubs,
Apache Flink, and Spark Streaming - with the same
production-grade standards as everything else we deliver:
monitoring, alerting, fault tolerance, and documentation
from day one.
Six Streaming Architecture Components We Build
From event ingestion to real-time dashboards - every layer of your streaming architecture designed and built to production standards.
Design and implementation of your core event streaming platform, Apache Kafka, Confluent Cloud, or Azure Event Hubs - including topic design, partition strategy, consumer group architecture, and retention policies for your event volumes and latency requirements.
Producers that capture events from your source systems - application logs, databases via CDC, IoT sensors, APIs, and clickstreams - and publish them to your streaming platform reliably, with exactly-once or at-least-once delivery guarantees as required.
The processing layer that enriches, filters, aggregates, and transforms events in-flight - built in Apache Flink or Spark Streaming. Windowed aggregations, real-time joins, stateful computations, and anomaly detection logic applied to every event as it arrives.
Sinks that land processed stream data into the right destination, real-time databases for low-latency queries, Delta Lake for lakehouse integration, Snowflake for warehouse persistence, or Elasticsearch for search and observability use cases.
The serving layer that puts processed stream data in front of business users - Power BI real-time dashboards via streaming datasets, push notifications, operational alert systems, and auto-refresh reports, with sub-second display latency from event to screen.
Production observability built into every streaming pipeline - consumer lag monitoring, throughput metrics, dead-letter queue handling, and automated alerts when pipelines fall behind. Plus event replay capability so historic data can be reprocessed without data loss.
From Use Case to Live Stream in 4 Phases
First streaming pipeline live in 2 weeks. We start with a single high-value use case, not a generic platform build - and expand from there.
We identify the highest-value streaming use case and define latency requirements, event volumes, source systems, and target destinations before selecting any platform or writing any code. Not every problem needs Kafka.
Platform selection and provisioning, Kafka cluster, Event Hubs namespace, or Confluent Cloud - with topic design, schema registry, network security, and monitoring infrastructure set up before any pipeline code begins.
Sprint-based pipeline development - producers capturing source events, Flink or Spark Streaming processing logic, and sinks writing to the target data store. First live pipeline end-to-end in week 2 with full monitoring active.
Load testing at 2× production event volume before cutover. Gradual traffic migration with rollback capability. Full documentation - architecture diagrams, runbooks, monitoring playbooks, and team training on operating the streaming platform.
Apache Kafka
Azure Event Hubs
Apache Flink
Spark Structured Streaming
AWS Kinesis
Confluent Cloud
Azure Stream Analytics
Microsoft Fabric Eventstream
Debezium CDC
Delta Lake Streaming
Power BI Streaming Datasets
Python / Kafka-PythonWhy Choose Numlytics for Real-Time Data Streaming
We've built real-time streaming architectures for enterprises in financial services, retail, manufacturing, and SaaS across the US, UK, and Australia.
"We were running fraud detection on hourly batch jobs. By the time an alert fired, the fraudulent transactions had already settled. Numlytics designed a Kafka-based streaming architecture that processes every transaction event in real time - running our fraud model via Flink against a feature store updated continuously from our customer behaviour stream. End-to-end latency from transaction event to risk score is under 200 milliseconds. Fraud losses in the first 90 days post-launch were down 34%. The architecture has handled our peak volumes - over 800,000 events per second, without a single incident."
Related Data Engineering Services
Streaming is one layer of a complete data architecture. These services build the rest of it.
Real-Time Streaming FAQs
Common questions before starting a real-time data streaming engagement with Numlytics.
Ask Us Anything →From Event to Decision in Under One Second
Get production-grade real-time data streaming - Kafka, Event Hubs, Flink, and Spark Streaming, with sub-second latency, 99%+ uptime, and full monitoring. Certified engineers. Proposal in 24 hours. US, UK, Australia & UAE.