How to import and clean large data sets in ThorsHammer

If you’re staring down a mountain of messy data, you’re not alone. Importing and cleaning large datasets isn’t glamorous, but it’s the backbone of any halfway decent analytics or reporting project. This guide is for anyone who needs to get big, ugly data into ThorsHammer and actually make it usable—without losing days to trial and error or fighting the software.

Here’s how to import and clean large data sets in ThorsHammer, what works, what doesn’t, and what to skip so you don’t waste your time.

1. Know Your Data (Before You Touch ThorsHammer)

Let’s get this out of the way: ThorsHammer is powerful, but it won’t magically fix bad assumptions or save you from not knowing your own data. Spend a few minutes upfront on these basics:

  • File types: ThorsHammer supports CSV, TSV, Excel, Parquet, and (with plugins) some databases.
  • Size: Files over 1GB will need a different approach than a 10MB spreadsheet.
  • Encoding: UTF-8 is safest. Weird encodings (looking at you, legacy Windows) can cause silent failures.
  • Structure: Header row present? Consistent column counts? Dates formatted the same way? If not, expect headaches.

Pro tip: Open your raw file in a plain text editor—not Excel—to spot weird delimiters, extra quotes, or junk characters.

2. Set Up ThorsHammer for Big Data

ThorsHammer is pretty forgiving, but if you throw a massive file at it with default settings, it’ll choke or crawl. Here’s what you should do before importing:

  • Check your system: You want at least 8GB RAM for anything over 500,000 rows. More is better. Close Chrome tabs and other memory hogs.
  • Update ThorsHammer: Small version bumps often fix import bugs or improve speed.
  • Adjust import settings:
  • Chunk size: For huge imports, set chunk size to 50,000–100,000 rows.
  • Parallelism: If your CPU can handle it, turn on parallel imports (under Preferences > Performance).
  • Temp folder: Point ThorsHammer to a fast SSD, not a slow spinning drive.

Don’t: Try to “just import” a 10GB file on a laptop unless you want to watch a progress bar for hours.

3. Import Your Data (Without Breaking Things)

Time to bring your data in. Here’s how to keep things smooth:

3.1. Use the Right Import Tool

ThorsHammer has three main import methods: - Drag-and-drop: Fine for files under 100MB. - File Import Wizard: Best for CSV, Excel, or Parquet up to 2GB. - Command-line import: For anything bigger or automated jobs. (See docs for thorshammer import syntax.)

3.2. Map Columns Carefully

  • Double-check column headers in the preview. ThorsHammer guesses types (string, int, date), but guesses wrong about 20% of the time on real-world data.
  • Manually set types for tricky columns: dates, zip codes, IDs with leading zeros.
  • Watch out for columns with “mixed” values—these often get set as string, which can break later calculations.

3.3. Handle Errors Early

  • If ThorsHammer flags import errors, don’t ignore them or just “skip rows.” Fix the source file if possible.
  • Common gotchas:
  • Extra delimiters or unmatched quotes in CSVs.
  • Null bytes in exported database dumps.
  • Rows with wildly different column counts (usually from Excel copy-paste).

Pro tip: Import just the first 1,000 rows as a test. Fix issues, then do the full import. Saves a lot of time and swearing.

4. Clean Up Your Data (The Practical Way)

You’ve got your data in? Good. Now, the real work starts. ThorsHammer offers lots of tools, but you only need a handful for most messy datasets.

4.1. Remove Junk Rows and Columns

  • Blank rows: Use “Remove empty rows” in the Data Cleaning panel.
  • Duplicate rows: “Remove duplicates” works, but double-check which columns are used for comparison.
  • Unused columns: Hide or delete columns you don’t need. Less clutter = fewer mistakes.

4.2. Standardize Data Formats

  • Dates: Run the “Normalize Dates” function. If ThorsHammer can’t auto-detect a format, you’ll have to specify it manually (e.g., MM/DD/YYYY vs YYYY-MM-DD).
  • Text case: For names, emails, and addresses, use “Format text” to pick a case (lower, upper, title).
  • Numbers: Watch for “numbers” stored as text—especially with currency symbols, commas, or spaces. Use “Convert to number” and set the locale if needed.

4.3. Handle Missing Data

  • Find missing values: Use “Filter by nulls” on each column. ThorsHammer highlights blanks and NULL.
  • Decide what to do:
  • For key fields, you can’t fake it—delete or flag the rows.
  • For optional fields, fill with defaults or median/mean if that makes sense for your analysis.
  • Don’t: Auto-fill missing values everywhere. It’s tempting, but can make your data misleading fast.

4.4. Validate and De-Noise

  • Value constraints: Set field rules (e.g., email must contain @, age must be >0). ThorsHammer lets you set these in the “Column Constraints” tab.
  • Spot outliers: Use summary stats or quick charts. If you see someone aged 999 or a sale for -$100, flag it for review.
  • Whitespace: “Trim whitespace” gets rid of sneaky spaces that break joins and filters.

Pro tip: Save your cleaning steps as a “recipe” in ThorsHammer. That way, you can reuse it when you get the next messy file—because you will.

5. What Not To Do

Some features sound great until you try them on real, large data. Here’s what to skip (or at least be careful with):

  • Don’t use “Smart Auto-Fix” on huge files. It’s slow and often makes questionable choices.
  • Avoid running complex formulas during import. Clean first, then calculate.
  • Skip the “Magic Merge” for now. It’s hit-or-miss with big sets and can hang for hours.

If you hit a wall, break your data into smaller chunks, clean each chunk, then recombine. It’s old-school but works.

6. Export and Save (So You Don’t Have to Do It Twice)

Once you’ve cleaned your data: - Export to a standard format (CSV or Parquet are safest for re-use). - Save your cleaning “recipe” or workflow. - If you need to share, export a data summary or sample—don’t make people download the whole file.

Always keep a backup of the original dirty file. You’ll thank yourself later if you need to start over.

Keep It Simple, Iterate, and Don’t Overthink It

Big, messy data isn’t going away. ThorsHammer can make it manageable if you stick to the basics: import carefully, clean only what matters, and save your steps. Don’t get lost chasing every edge case or clicking every “AI Clean” button—start simple, check your results, and improve as you go.

If you hit a snag, odds are it’s something simple: a weird delimiter, a rogue blank, or ThorsHammer’s type detection getting confused. Step back, fix the obvious stuff, and try again. That’s how real data cleaning gets done.