In your project, you bring JSON data, run a cron job, and load it into S3. What transformations do you perform, and what difficulties do you face in this flow?

In the described flow, the JSON payload is fetched, a cron job triggers the ingestion, and the data lands in an S3 bucket. Typical transformations include: normalizing nested JSON into a flat schema, converting data types (e.g., string dates to timestamps), handling missing fields, and enriching records with metadata. You might also apply deduplication or filtering before loading into downstream systems. Common difficulties are schema drift (new keys appearing), inconsistent data quality, and handling large files that exceed the memory limits of the processing engine. Scheduling the cron job can lead to overlapping runs if the previous run hasn’t finished, causing duplicate data. To mitigate, use a lock file or a state store to track the last processed timestamp, and implement idempotent writes to S3. Monitoring and alerting on job failures and data quality metrics are essential to keep the pipeline robust.