Metadata Extraction, Indexing, and Search Tooling

A digital archive without robust metadata is just a dumping ground for data. This guide covers CLI utilities, automated extraction patterns, and database indexing strategies used to catalog and search large-scale digital collections.

Essential Extraction Utilities

1. ExifTool (Images, Documents, Media)

ExifTool reads, writes, and manipulates metadata across virtually all file formats.

# Extract comprehensive JSON metadata for a file
exiftool -j -g image.tiff > image_metadata.json

# Extract recursively across a directory, ignoring duplicate tags
exiftool -r -json -ext jpg -ext tiff /path/to/archive > catalog.json

2. MediaInfo (Audio / Video Pipelines)

MediaInfo extracts technical stream attributes (codecs, frame rates, color spaces, bit depths) necessary for AV archives.

# Output full technical breakdown in JSON format
mediainfo --Output=JSON video_master.mkv > video_metadata.json

3. FITS (File Information Toolset)

Developed by Harvard University Library, FITS wraps multiple extraction tools (ExifTool, MediaInfo, DROID, Jhove) into a single unified XML output.

# Run FITS extraction against a directory
fits.sh -i /path/to/incoming/sip -o /path/to/metadata/

Sidecar Metadata Workflow

To prevent corrupting original binary files during indexing, store descriptive and technical metadata in external JSON or YAML sidecar files alongside the master asset.

Example Directory Structure

/archive/2026/DOC001/
├── master_document.tiff
└── master_document.tiff.json   <-- Sidecar metadata

Automated Sidecar Generation Script

Save this script as generate_sidecars.sh to automatically build sidecars for incoming assets:

#!/usr/bin/env bash
set -euo pipefail

TARGET_DIR="${1:-.}"

find "$TARGET_DIR" -type f \( -name "*.tiff" -o -name "*.jpg" -o -name "*.mov" -o -name "*.mp4" \) | while read -r file; do
    sidecar="${file}.json"
    if [ ! -f "$sidecar" ]; then
        echo "Extracting metadata for: $file"
        exiftool -json -g "$file" > "$sidecar"
    fi
done

Indexing & Full-Text Search Engines

Once sidecars are generated, ingest them into a centralized query layer for rapid discovery.

Ingesting Sidecars into SQLite (Example)

# Create SQLite database and table
sqlite3 archive_index.db "CREATE TABLE assets(id INTEGER PRIMARY KEY, path TEXT, metadata JSON);"

# Ingest a sidecar file directly into the JSON column
sqlite3 archive_index.db "INSERT INTO assets(path, metadata) VALUES('/archive/photo.jpg', readfile('photo.jpg.json'));"