Have you ever migrated a data pipeline from an on‑premises platform to a cloud platform, or from one cloud platform to another, or from one technology stack to another?

Yes, I led a migration of a real‑time analytics pipeline from an on‑premises Hadoop cluster to AWS. The original stack used HDFS, MapReduce, and a custom Java application for data ingestion. We moved the storage to S3, replaced MapReduce with Spark on EMR for processing, and rewrote the ingestion layer in Python using Apache Flink. The migration involved several key steps: 1) Data replication – I set up an S3 bucket with versioning and used AWS DataSync to copy the existing HDFS data. 2) Schema migration – I used AWS Glue to catalog the data and generate ETL scripts. 3) Application refactor – the Java ingestion code was rewritten in Python, leveraging the AWS SDK to push data into Kinesis Data Streams, which fed into the Spark job. 4) Testing – I performed end‑to‑end tests with synthetic data to ensure latency and throughput matched the on‑prem baseline. 5) Cutover – we ran a parallel run for a week, then switched traffic to the new pipeline. The migration reduced operational overhead by 40% and improved data freshness from 15 minutes to real‑time.