What techniques do you use to move data from one service to another, such as from X3 to Redshift?

When moving data from a source like X3 (a hypothetical OLTP system) to Amazon Redshift, I employ a hybrid approach. First, I extract data using CDC or incremental snapshots to capture only changed rows. For large volumes, I export the data to S3 in Parquet or CSV format, leveraging X3’s export utilities or a custom ETL job. Once the files are in S3, I use Redshift’s COPY command to load data efficiently, specifying proper delimiters, compression, and column mappings. I also use AWS Glue or Spark to perform transformations—cleaning, deduplication, and type casting—before loading into Redshift. For near‑real‑time scenarios, I set up a Kafka Connect sink that streams changes directly into Redshift via the Redshift JDBC driver. Throughout the process, I monitor data quality with checksums, row counts, and schema validation, and I schedule the pipeline with Airflow to handle dependencies and retries. This combination of batch and streaming techniques ensures high throughput, low latency, and data consistency.