Which end-to-end data pipeline do you recommend for processing large datasets?

An end‑to‑end data pipeline for large datasets typically consists of ingestion, transformation, storage, and analytics layers. For ingestion, I recommend using a managed streaming service like AWS Kinesis or Kafka for real‑time data, and batch ingestion via AWS S3 or Azure Blob Storage for historical data. Transformation can be handled by a distributed processing engine such as Apache Spark or Flink, which scales horizontally and supports complex ETL logic. For storage, a columnar data warehouse like Snowflake, Amazon Redshift, or Google BigQuery offers fast query performance and elastic scaling. Finally, analytics and reporting can be built on top of BI tools such as Tableau, Looker, or Power BI, or through custom dashboards using Python libraries. Key decisions include choosing the right data format (Parquet or ORC for compression), implementing schema evolution handling, and ensuring data quality through automated validation. Monitoring and alerting should be integrated using CloudWatch or Prometheus to detect failures early. This architecture balances scalability, cost, and maintainability, making it suitable for processing terabytes of data daily.