When building a new data ingestion system, how do you decide which data manipulation library or framework to use? When would you choose Spark, and when would you not? What criteria guide that decision?

When designing a new data ingestion pipeline, I first assess the data volume, velocity, and schema volatility. If the data is high‑volume, requires distributed processing, or needs complex transformations, I lean toward Spark or a Spark‑based service like Glue. Spark excels at batch and streaming workloads, fault tolerance, and iterative algorithms, making it ideal for ETL jobs that process terabytes of data. For smaller, low‑latency pipelines or when the target database can handle transformations, I might choose a lightweight framework such as Pandas, Dask, or AWS Glue ETL jobs that run on serverless compute. I also consider the existing skill set and infrastructure: if the team is familiar with Spark and the cluster is available, Spark is a natural fit. If the data is semi‑structured and the target is a data lake, I might use Glue crawlers and Spark for schema inference. I avoid Spark when the data volume is small, the transformations are trivial, or the cost of running a cluster outweighs the benefits. In those cases, a simple Python script or Glue ETL job is more efficient.