Buffer Analytics Workflows & Operational Runbook #
Ingestion Architecture #
text
Buffer API (GraphQL / REST)
│
│ buffer CLI (cursor pagination, --output json)
▼
scripts/buffer_analytics.py
│
├──> channels (raw JSON + metadata)
├──> posts (raw JSON + indexed fields)
├──> post_metrics (impressions, reactions, comments, etc.)
├──> post_assets (images, video sources)
├──> post_tags (tag taxonomy)
└──> sync_history (audit run logs)
│
▼
~/.buffer/analytics.db (SQLite)
│
▼
v_posts_summary (Pivoted SQL View)
│
▼
Custom SQL Analytics & Reports
Standard Workflows #
1. Initial Historical Backfill #
Paginates through the entire post history across all connected channels from newest to oldest:
bash
python3 scripts/buffer_analytics.py sync --full
2. Routine Incremental Sync #
Syncs only new posts or updates from the last recorded timestamp (with a 2-day lookback overlap to refresh engagement metrics on recent posts):
bash
python3 scripts/buffer_analytics.py sync
3. Channel-Specific Backfill #
Filter sync to a specific channel (e.g. LinkedIn only):
bash
python3 scripts/buffer_analytics.py sync --channel-id 1234567890abcdef12345678
4. Date-Bounded Ingestion #
Backfill a specific campaign or date window:
bash
python3 scripts/buffer_analytics.py sync --start-date 2026-06-01T00:00:00Z --end-date 2026-08-18T00:00:00Z
5. Running Ad-Hoc SQL #
Execute custom queries with output formatting:
bash
# Markdown table output
python3 scripts/buffer_analytics.py query "SELECT * FROM v_posts_summary LIMIT 5" --format markdown
# JSON output
python3 scripts/buffer_analytics.py query "SELECT service, AVG(impressions) FROM v_posts_summary GROUP BY service" --format json
# CSV export
python3 scripts/buffer_analytics.py query "SELECT * FROM v_posts_summary WHERE status = 'sent'" --format csv > /tmp/posts_export.csv
6. Pre-Packaged Reports #
bash
python3 scripts/buffer_analytics.py report overview
python3 scripts/buffer_analytics.py report top-posts
python3 scripts/buffer_analytics.py report timing
python3 scripts/buffer_analytics.py report hooks