Describe the path a single file takes from the moment it lands in a storage location to the moment it is ready for querying, including all intermediate steps that can be queried.
💡 Model Answer
When a file lands in a storage bucket (e.g., S3 or ADLS), the ingestion pipeline first records metadata in a catalog (AWS Glue Data Catalog or Azure Purview). A trigger (S3 event or Azure Event Grid) starts a processing job, such as an AWS Glue crawler or an Azure Data Factory pipeline. The job validates the schema, extracts the data, and writes it to a staging area in a structured format (Parquet or Delta Lake). During this stage, a lineage record is created so that each transformation step can be queried. Next, a transformation job (Spark, Flink, or DBT) reads the staged data, applies business logic, and writes the result to a target table in a data warehouse (Redshift, Snowflake, or Synapse). Each step—ingestion, staging, transformation, and loading—updates the metadata store, allowing users to query the file’s journey via lineage tools or custom dashboards. Finally, the data is available in the warehouse, and a query engine (Presto, Snowflake SQL, or Spark SQL) can retrieve it for analytics.
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