How would you read and process this raw transaction log file in a Spark or Pandas ETL job?

To process the raw transaction log, you first decide on the ingestion framework. In Spark, you would use spark.read.text or spark.read.csv with the appropriate delimiter (e.g., tab or comma). You can supply a schema to enforce data types: transaction_id as string, customer_id as integer, transaction_date as timestamp, amount as decimal, channel as string. Spark will parse each line into a Row and create a DataFrame. You can then cast the transaction_date string to a TimestampType using to_timestamp. After cleaning (e.g., trimming whitespace, handling missing values), you can write the DataFrame to a columnar format like Parquet or Delta Lake for fast querying. If you prefer Pandas, you would use pd.read_csv with sep, parse_dates=['transaction_date'], and dtype specifications. Pandas is suitable for smaller datasets or prototyping. In both cases, you should consider partitioning by date or channel to optimize downstream queries. Finally, you can register the DataFrame as a temporary view and run SQL queries or use it in downstream ETL steps. For large volumes, you might also use AWS Glue or Databricks to orchestrate the job, schedule it, and handle schema drift by using a dynamic frame. Logging and monitoring are essential to detect parsing errors and data quality issues.