Describe a data pipeline architecture that ingests data from APIs or files, processes it with ETL transformations, and loads it into Snowflake for recording purposes.

A typical architecture starts with an ingestion layer that pulls data from APIs or uploads files into a staging area such as an S3 bucket or Azure Data Lake. For APIs, a scheduler or event‑driven function (e.g., AWS Lambda or Azure Function) writes JSON or CSV to the bucket. For files, a data‑ingestion tool like AWS Glue or Azure Data Factory can monitor a folder and copy new files.

Next, a transformation layer runs ETL jobs. Spark or dbt can read the raw data, perform schema validation, clean missing values, and apply business logic. The transformed data is written back to a separate “processed” bucket or a temporary Snowflake stage.

The loading layer uses Snowflake’s COPY INTO command to bulk load the processed files into target tables. Snowflake’s micro‑partitioning and clustering keep query performance high. Metadata and lineage are tracked in a catalog (e.g., Snowflake Information Schema or a third‑party tool).

Monitoring and alerting are added via CloudWatch, Azure Monitor, or Snowflake’s Query History. Security is enforced with IAM roles, encryption at rest and in transit, and network policies. This architecture scales horizontally, supports incremental loads, and provides auditability for compliance.