Developer Strategies for Pre-Import Data Cleaning and Transformation

Preparing data for import into various systems is a common yet challenging task for many developers and data practitioners. A recent Hacker News discussion delved into the tools, techniques, and pain points associated with this crucial pre-import step, revealing a wealth of practical insights.

The Go-To Tools: Scripting Reigns Supreme

A dominant theme in the discussion is the preference for general-purpose programming languages over specialized ETL tools or spreadsheet software for complex data transformations.

• Python is the clear favorite, frequently mentioned with powerful libraries such as Pandas for data manipulation, Polars for handling larger datasets efficiently, Pydantic for data validation via schema definition, and the built-in csv module.
• Other languages like Java, C#, and TypeScript (often in serverless contexts like AWS Lambda) also get nods for their robustness.
• The rationale is clear: while tools like Excel or some ETL software might get you "80% of the way there," the final, often most complex, 20% (like nuanced date conversions or intricate code mappings) requires the flexibility and power of a real programming language. One commenter noted, "real programming languages have the tools to format dates correctly with a few lines of code... fake programming languages don't."

Common Workflows and Best Practices

Several effective strategies and workflows were shared:

• Preserve the Original: A fundamental first step echoed by many is to store a pristine, unmodified copy of the source data. This allows for reprocessing if errors are found or requirements change.
• Reproducible Chains: Build a sequence of transformation steps that can be re-run consistently.
• Define a Clean Target: Clearly specify the desired output schema or interface. This provides a concrete goal for the transformation process.
• Incremental Validation: Test scripts on a subset of data before running on the full dataset or in production.
• Handling Excel: When Excel files are unavoidable inputs, they are often treated as "free range data." One innovative approach involves using libraries like convert-excel-to-json to shred spreadsheets into a more parsable format, enabling a "blunt-force chainsaw approach" to unstructured data masquerading as spreadsheets.
• Mapping Management: For tasks like mapping old codes to new ones, some teams maintain these mappings in Excel files stored in Git, allowing business users to manage them. However, this requires robust validation of the imported rules during processing to catch errors introduced by Excel's data interpretation (e.g., numeric IDs becoming numbers instead of strings).
• SQL for Transformation: Tools like SQLite and DuckDB are used to import CSVs or other tabular data, perform SQL-based transformations, and then export the results.
• Leveraging LLMs: Some contributors use LLMs like ChatGPT to generate initial Python transformation scripts, which are then reviewed and refined. One user described a workflow of uploading a PII-redacted data sample and target schema to an LLM to get a starting script.
• Modularity and Observability: Adopting a Unix-like philosophy where each transformation step does one thing well, coupled with good logging and observability, is beneficial.

Dealing with Tedious and Painful Aspects

The discussion also highlighted recurring challenges:

• The "Last 20%" Problem: As mentioned, specialized tools often fall short for complex, edge-case transformations.
• Excel's Pitfalls: While useful for data exploration or simple tasks by non-technical users, Excel is frequently cited as a source of frustration in data pipelines due to its automatic type conversions (e.g., dates, numbers treated as strings), difficulty with version tracking, and challenges in collaborative cleaning.
• Garbage In, Garbage Out: The quality of the source data is a major factor. No amount of sophisticated tooling can perfectly fix inherently flawed or missing information. As one user put it, "You can only extract information that was actually recorded."
• Semantic Understanding: More than just structural transformation, understanding the meaning of data from different sources and ensuring correct mapping to the target system is a significant hurdle. One commenter noted, "More irritating than the data transformations is understanding the structure of the data and practical use, e.g., the same column for Partner A means something entirely different for Partner B."
• Fuzzy Matching and Deduplication: These remain complex and often require manual review and partner feedback, which can be a slow process.
• Reviewing LLM Outputs: While LLMs can accelerate script writing, ensuring the correctness of the generated code and transformed data still requires careful human oversight.

Innovative Approaches and Tools

Beyond the mainstream, some interesting tools and techniques were mentioned:

• Custom DSLs: One user described creating a simple, English-like grammar that translates to Polars operations, enabling business users to understand and even modify transformations.
• Visidata and Datasette: These tools were recommended for data exploration and inspection.
• Cloud Integration: For lightweight or one-off tasks, AWS Lambda with TypeScript is used, while larger jobs might involve Python scripts outputting to S3 for AWS Glue.
• Fuzzy Matching for Schema Alignment: A project involved using fuzzy matching (stemming, de-capitalization) to categorize varied field names into a common definition set.

In conclusion, while there's no one-size-fits-all solution, the discussion underscores a strong trend towards using robust programming languages, particularly Python, for data cleaning and transformation. This approach, combined with careful process management, version control, and an awareness of common pitfalls, enables developers to tackle the often messy reality of preparing data for system imports.