Real-Time Hadoop Pipeline: Kafka, Spark & HDFS

I will design and build a real-time data pipeline using Apache Kafka, Spark Structured Streaming, and HDFS — covering Kafka topic design and producer configuration, Spark Structured Streaming consumer with your transformation logic, micro-batch or continuous processing configuration, exactly-once semantics with checkpointing, HDFS sink with time-partitioned output, Kafka consumer lag monitoring, and YARN deployment configuration. Real-time pipelines built without exactly-once semantics produce duplicate records that corrupt aggregations; without consumer lag monitoring they have no operational visibility into whether the pipeline is keeping up with incoming data volume.

The pipeline covers Kafka topic creation with appropriate partition count, Spark Structured Streaming application with your specified transformation, watermark configuration for late data handling, checkpoint directory configuration, micro-batch interval tuning, HDFS output with time partitioning, Kafka consumer group lag metric export to Prometheus, and YARN deployment with appropriate resource allocation.

This service suits data engineering teams building real-time data landing zones, streaming ETL pipelines, event-driven analytics, or IoT data ingestion systems on Hadoop infrastructure.