You need to build a data pipeline for a client with a social system that has various data types. What strategy would you use to ingest, transform, and load the data into Snowflake in the cloud, ensuring scalability?

I would adopt a lake‑first approach: ingest raw logs, clickstreams, and user profiles into an S3 data lake using S3 event notifications or Kafka Connect. Each raw file is stored in a raw zone with a canonical naming convention. A Glue crawler or Spark job parses the files into Parquet, applying schema‑on‑read and partitioning by date and user segment. The transformed data is then loaded into Snowflake via Snowpipe for continuous ingestion or bulk COPY for batch loads. Snowflake’s micro‑partitioning and clustering keys keep queries fast. For transformations that require joins or aggregations, I use Snowflake Tasks or dbt to materialize incremental models. I also implement data quality checks with dbt tests and monitor with Snowflake’s Query History. This architecture scales because the data lake can grow arbitrarily, Spark handles parallel processing, and Snowflake auto‑scales compute resources.