How did data get into your system?
💡 Model Answer
Data entered our system through a combination of batch and streaming pipelines. For historical data, we used an S3 bucket as a landing zone; an AWS Glue crawler discovered the schema and populated a Glue Data Catalog. A scheduled AWS Lambda function then triggered an AWS Glue ETL job that transformed the raw CSVs into Parquet and loaded them into a Snowflake stage. For real‑time events, we used Kafka on Confluent Cloud; a Kafka Connect sink connector streamed records directly into a Snowflake pipe. In both cases, we applied schema validation and enrichment in the ETL step. We also exposed a REST API built on FastAPI that allowed external partners to POST JSON payloads; the API wrote to an SQS queue, and an AWS Step Functions workflow processed the messages and inserted them into Snowflake. This layered approach ensured data quality, scalability, and low latency for downstream analytics.
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