Real-time Streaming Data Pipeline (Kafka)

An event-streaming pipeline that moves data the moment it happens — not hours later. I build ingestion on Apache Kafka or AWS Kinesis, stream processing with Flink or Spark Structured Streaming, and sinks into your warehouse or lake (Snowflake, BigQuery, Redshift, or S3/Iceberg). Think live order events for a Dubai e-commerce store, ride/delivery telemetry, IoT readings, or app clickstream landing in your analytics within seconds.

The build is production-grade: exactly-once processing so events are never double-counted, a schema registry (Confluent / AWS Glue) so producers and consumers stay compatible as data evolves, and consumer-lag monitoring with alerts so you know the instant the stream falls behind. I provision on your AWS or self-hosted infrastructure and hand over runbooks so your team can operate it across Dubai, Abu Dhabi and Sharjah deployments.

This is real-time streaming for live events. It differs from my Data Engineering (ETL, dbt, Airbyte) gig, which moves data in scheduled batches — choose this when you need sub-minute freshness; choose the batch gig when nightly or hourly loads are fine.

How it works

1. 1
   Design topics / streams

   We map event sources, partitioning, schemas and the target sinks.

2. 2
   Build processors + sinks

   I implement the stream processors with exactly-once semantics and wire up the warehouse/lake sinks.

3. 3
   Monitor + handover

   I add lag monitoring and alerts, load-test it, and hand over runbooks and documentation.