Data Processing

Data Processing

When to Use

• Querying, filtering, or transforming JSON files
• Reading or converting YAML, TOML, or XML config files
• Analyzing, aggregating, or joining CSV/TSV/Parquet files
• Running SQL queries against local data files without a database server
• Converting between data formats (JSON to YAML, CSV to JSON, etc.)
• Exploring deeply nested JSON structures

Tools

Patterns

JSON: Filter array elements by field value

jq '.items[] | select(.status == "active")' data.json

JSON: Extract specific fields from array

jq '[.users[] | {name: .name, email: .email}]' data.json

JSON: Count items grouped by field

jq '[.events[] | .type] | group_by(.) | map({type: .[0], count: length})' data.json

YAML: Read a nested value

yq '.spec.containers[0].image' deployment.yaml

YAML: Convert entire file to JSON

yq -o json config.yaml

TOML: Read a value

yq -p toml '.database.host' config.toml

JSON: Explore unknown structure by grepping paths

gron data.json | grep -i "error"

CSV: Column statistics (min, max, mean, stddev)

xsv stats data.csv | xsv table

CSV: Search rows matching a pattern in a column

xsv search -s status "active" data.csv

CSV: Select specific columns

xsv select name,email,created_at users.csv

CSV: Sort by column

xsv sort -s revenue -R sales.csv

SQL: Query a CSV file

duckdb -c "SELECT department, COUNT(*) as cnt, AVG(salary) as avg_sal FROM 'employees.csv' GROUP BY department ORDER BY avg_sal DESC"

SQL: Query a JSON file

duckdb -c "SELECT * FROM read_json_auto('events.json') WHERE type = 'error' LIMIT 20"

SQL: Query Parquet files

duckdb -c "SELECT * FROM 'data/*.parquet' WHERE created_at > '2026-01-01'"

CSV/JSON: Transform records with miller

mlr --csv filter '$revenue > 1000' then sort-by -nr revenue sales.csv

Format conversion: CSV to JSON

mlr --icsv --ojson cat data.csv

Pipelines

YAML config → JSON → SQL query

yq -o json config.yaml | duckdb -c "SELECT key, value FROM read_json_auto('/dev/stdin') WHERE env = 'production'"

Each stage: yq converts YAML to JSON, DuckDB runs SQL on the JSON stream.

Grep nested JSON paths → reconstruct matching subset

gron large.json | grep "\.errors\[" | gron --ungron

Each stage: gron flattens JSON to paths, grep filters, ungron reconstructs valid JSON from matches.

CSV filter → aggregate with SQL

xsv search -s region "EU" sales.csv | duckdb -c "SELECT product, SUM(revenue) as total FROM read_csv_auto('/dev/stdin') GROUP BY product ORDER BY total DESC"

Each stage: xsv filters rows by region, DuckDB aggregates the filtered stream.

Join two CSV files with SQL

duckdb -c "SELECT u.name, u.email, o.total, o.date FROM 'users.csv' u JOIN 'orders.csv' o ON u.id = o.user_id ORDER BY o.date DESC"

Multi-format pipeline: JSON → CSV → stats

jq -r '.records[] | [.name, .score] | @csv' data.json | xsv stats

Each stage: jq extracts fields to CSV format, xsv computes statistics.

Prefer Over

• Prefer DuckDB over Python/pandas for ad-hoc SQL queries on files — single command, no script, handles large files
• Prefer jq over Python json module for one-off JSON transforms — single pipeline vs. multi-line script
• Prefer xsv over awk/cut for CSV operations — correct CSV parsing, handles quoted fields and escapes
• Prefer miller over awk for format-aware record transformations — understands CSV/JSON headers natively
• Prefer yq over custom parsers for config file reads — handles YAML, TOML, XML with consistent syntax

Do NOT Use When

• Data is already in a running database — query the database directly
• File is under 10 lines — just use the Read tool and process in-context
• Task requires complex multi-step logic with conditionals — write a Python script instead
• JSON is simple enough to read by eye — use the Read tool, don't over-engineer
• Working with binary or non-structured data formats