What are the different types of transformations you apply to your data before loading it into Redshift, and what kinds of validations do you perform? For example, if you have a table, how would you handle it?

In an ETL pipeline that targets Amazon Redshift, I typically apply several categories of transformations: 1) Data cleansing – removing or correcting malformed records, trimming whitespace, normalizing case, and handling nulls. 2) Type casting and formatting – converting strings to dates, numbers to the appropriate precision, and ensuring consistent column types. 3) Deduplication – using window functions or hash checks to eliminate duplicate rows based on a business key. 4) Enrichment – joining with reference tables or adding calculated columns (e.g., age from birthdate). 5) Aggregation – summarizing detailed data into fact tables when required. 6) Validation – applying business rules such as range checks, referential integrity, and consistency checks (e.g., ensuring that a foreign key exists in the dimension table). 7) Data masking or redaction – for sensitive fields before loading into a shared environment. After transformations, I load the data into Redshift using the COPY command, which is optimized for bulk ingestion. The overall complexity is O(n) for most row‑level operations, with occasional O(n log n) for sorting during deduplication. This approach ensures clean, consistent, and query‑ready data in Redshift.