When Excel Gives Up and Your RAM Taps Out
Picture this: Your client sends you a “small” CSV. You double-click it. Excel freezes. You try a text editor. It crashes. You check the file size: 500MB. Five million rows. Your laptop fan sounds like it’s preparing for takeoff.
Welcome to the world of enterprise data, where “just open it in Excel” is the punchline to a very expensive joke.
At Capital Compute, we’ve seen it all the corrupted CSVs that make grown developers cry, files so massive they have their own gravitational pull, and data so messy it puts a teenager’s bedroom to shame. Here’s our battle-tested guide to taming these beasts.
Step 1: Don’t Be a Hero but Analyze Before You Act
Before you charge in with scripts blazing, take a breath and get to know your enemy:
# The reconnaissance mission
head -50 large_file.csv
tail -50 large_file.csv
wc -l large_file.csv
ls -lh large_file.csv
Take a random sample to test safely:
shuf -n 100 large_file.csv > sample.csv
Check for patterns and errors:
grep -c “ERROR” large_file.csv
grep -n “NULL\\|null\\|NaN” large_file.csv | head -20
Need to inspect specific lines?
sed -n ‘1000,1050p’ large_file.csv
Step 2: Organize Your Workspace
A good folder structure saves hours of headache:
# The holy trinity of data processing
mkdir -p project/{backup,clean,logs,scripts,validation}
# Rule #1: NEVER touch the original
cp large_file.csv backup/large_file_$(date +%Y%m%d_%H%M%S).csv
# Rule #2: Work on copies like your career depends on it
cp backup/large_file_*.csv clean/
Think of it like surgery and you don’t operate on the original patient. Backups are your lifeline.
Step 3: Surgical Precision With sed
When CSVs look messier than a teenager’s bedroom, here’s your toolkit for quick fixes:
# Fix quotes that forgot how to quote
sed ‘s/”\\([^”]*\\),\\([^”]*\\)”/”\\1;\\2″/g’ broken.csv > fixed.csv
# Windows line endings? In MY Unix file? More likely than you think
sed ‘s/\\r//g’ input.csv > output.csv
# That one intern who used spaces instead of tabs
sed ‘s/ */ /g’ input.csv > output.csv
# The “in-place” edit (with a safety net)
sed -i.bak ‘s/old_pattern/new_pattern/g’ large_file.csv
# Delete lines that offend you
sed ‘/pattern_to_delete/d’ input.csv > output.csv
# Target specific lines like a data sniper
sed ‘100,200s/old/new/g’ input.csv > output.csv
Step 4: Call in awk when sed isn’t enough
# Fix just column 3 (because columns 1 and 2 are perfect angels)
awk -F’,’ ‘{gsub(/old/,”new”,$3); print}’ OFS=’,’ input.csv > output.csv
# Conditional replacement (if column 1 > 1000, fix it)
awk -F’,’ ‘$1 > 1000 {gsub(/pattern/,”replacement”)} 1’ input.csv > output.csv
Step 5: Divide and Conquer
Massive files? Split them into bite-sized chunks:
split -l 10000 large_file.csv chunk_
# Split with style (numbered files with proper extensions)
split -l 5000 –numeric-suffixes=1 –additional-suffix=.csv large_file.csv part_
# Split by size (100MB chunks – bite-sized pieces)
split -b 100M large_file.csv chunk_
# The fancy split (preserves headers in each chunk – because we’re not savages)
head -1 large_file.csv > header.csv
tail -n +2 large_file.csv | split -l 10000 – chunk_
for file in chunk_*; do
cat header.csv $file > temp && mv temp $file
done
Step 6: Logging Your Future Self Will Thank You
command | tee -a processing_$(date +%Y%m%d_%H%M%S).log
# Timestamp everything
echo “[$(date ‘+%Y-%m-%d %H:%M:%S’)] Starting the marathon…”
- Log every 10,000 lines to track progress
- Detailed logs are like insurance you think you won’t need them until you do
Step 7: Trust, But Verify
Never trust a CSV that says it’s clean. Check everything:
awk -F’,’ ‘{print NF}’ file.csv | sort | uniq -c
# Find troublemakers
awk -F’,’ ‘NF != 8 {print “Line”, NR, “has”, NF, “columns”}’ file.csv
# The quote police
grep -n ‘”[^”]*$’ file.csv
# Empty field patrol
awk -F’,’ ‘{for(i=1;i<=NF;i++) if($i==””) print “Line”, NR, “Column”, i, “is empty”}’ file.csv
# Compare row counts
wc -l original.csv processed.csv
# The usual suspects
grep -c “^,” file.csv
grep -c “,$” file.csv
grep -c “^$” file.csv
Step 8: The Paranoid’s Checksum
md5sum original.csv > checksums.txt
md5sum processed.csv >> checksums.txt
# Count unique values
cut -d’,’ -f1 file.csv | sort | uniq | wc -l
# Date format validator
cut -d’,’ -f3 file.csv | grep -v “^[0-9]{4}-[0-9]{2}-[0-9]{2}$” | head
Step 9: Make It Interactive
Command-line tools can be friendly:
- Use select for menus (democracy in action)
- Add confirmation prompts: read -p “Delete everything? (y/n): ” (save yourself from yourself)
- Show progress with pv: pv huge.csv | magic_script > output.csv (progress bars make everything better)
- Use dialog or whiptail for that retro GUI feel
Step 10: Make It Fly
# Parallel processing
parallel -j 4 ‘process_script.sh {}’ ::: chunk_*.csv
# Unix pipe dream
cat file.csv | grep pattern | sed ‘s/old/new/g’ | awk ‘{print $1}’ > output.csv
# Monitor the action
htop
iostat -x 1
Capital Compute’s Commandments
- Thou shalt not modify originals – Backups are your religion
- Thou shalt test on samples – 1000 lines of testing saves 5 million lines of tears
- Thou shalt log everything – Future you is watching, judging
- Thou shalt validate – Count everything twice, trust nothing once
- Thou shalt use the right tool – Bash for simple, Python for complex, both for beautiful
- Thou shalt stream – Loading 5GB into memory is a sin
- Thou shalt make it resumable – Because Murphy’s Law is real
When to Use This Arsenal
- Excel freezes on large files
- Your data looks like it went through a blender
- You have 100+ files needing identical processing
- Data loss could be catastrophic
- You want to look like a command-line wizard
The Bottom Line
At Capital Compute, we believe that real-world code is about solving problems, not creating new ones. These command-line tools aren’t just powerful but they are often the only thing standing between you and a data disaster.
Master them, and massive CSVs won’t scare you anymore. You might even enjoy working with them (we said might).
Remember: In the world of big data, the command line isn’t just a tool but it’s a superpower. Use it wisely, and always keep your backups.
