What are the common pitfalls when loading large datasets?

Loading large datasets often leads to memory-related issues. When datasets exceed available RAM, attempting to load them all at once can crash applications or slow systems to a crawl. For example, using libraries like Pandas in Python without caution can cause problems because pandas.read_csv() defaults to loading the entire file into memory. A 10GB CSV file loaded directly into a DataFrame might consume 15-20GB of RAM due to type conversions or intermediate operations. To avoid this, developers should use chunked loading (e.g., chunksize in Pandas) or tools like Dask or Apache Spark that process data incrementally. For instance, streaming data row-by-row or using generators can prevent memory overload while maintaining performance.

Another common pitfall is inefficient data type handling. Large datasets often contain columns stored as overly generic types (e.g., strings or 64-bit floats) that waste memory. For example, a column of integers stored as int64 might only need int16 if values are small. Similarly, categorical data stored as strings (e.g., “Male"/"Female”) can be converted to Pandas’ category dtype, reducing memory usage by up to 90%. Dates parsed as strings instead of datetime types also bloat memory and limit query efficiency. Developers should explicitly define column types during loading (e.g., using dtype parameters) and use profiling tools to identify optimization opportunities.

A third issue is inadequate error handling for data inconsistencies. Large datasets often contain missing values, malformed rows, or encoding errors that disrupt loading. For instance, a CSV with inconsistent quotes or unexpected delimiters can cause parsers to fail mid-process. Similarly, missing values in numeric columns may trigger errors if not handled (e.g., NaN handling in Pandas). Developers should validate data early by specifying error-handling strategies (e.g., error_bad_lines=False in Pandas) or using schema validation libraries. Tools like Great Expectations or custom scripts can pre-scan datasets for anomalies, ensuring smoother ingestion. For example, skipping invalid rows or logging errors for later review prevents crashes and maintains workflow continuity.