openingest 2.5.0

Configuration-driven data ingestion framework with quality checks, metadata, and orchestration

OpenIngest — Usability and Observability

This document covers the monitoring, metadata, and notification capabilities of OpenIngest.

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Metadata tables

Every pipeline execution writes to four PostgreSQL tables automatically created on first run.

pipeline_runs

One row per full pipeline execution.

Column	Type	Description
run_id	TEXT	Unique ID — format OI-YYYYMMDD-XXXXXX
status	TEXT	SUCCESS or FAILED
total_datasets	INTEGER	Datasets processed
total_rows	BIGINT	Total rows loaded
total_duration	FLOAT	Seconds
started_at	TIMESTAMP	Run start time
finished_at	TIMESTAMP	Run finish time

pipeline_dataset_runs

One row per dataset per execution.

Column	Type	Description
run_id	TEXT	Foreign key to pipeline_runs
dataset_name	TEXT	Dataset name from datasets.yaml
status	TEXT	SUCCESS or FAILED
rows_loaded	BIGINT	Rows loaded this run
duration_seconds	FLOAT	Seconds
target_table	TEXT	Staging table name
load_strategy	TEXT	replace, append, or incremental
load_mode	TEXT	FULL, APPEND, or INCREMENTAL
watermark_value	TEXT	Latest watermark (incremental only)
loaded_at	TIMESTAMP	Completion timestamp

pipeline_quality_runs

One row per dataset per execution with quality metrics.

Column	Type	Description
run_id	TEXT	Foreign key to pipeline_runs
dataset_name	TEXT	Dataset name
status	TEXT	PASS or FAIL
score	FLOAT	Quality score 0.0–100.0
checks_total	INTEGER	Total checks run
checks_passed	INTEGER	Checks that passed
checks_failed	INTEGER	Checks that failed

pipeline_incremental_state

Persists watermark state between runs for incremental datasets.

Column	Type	Description
dataset_name	TEXT	Primary key
target_table	TEXT	Staging table
incremental_column	TEXT	Watermark column name
last_watermark_value	TEXT	Last processed watermark
last_rows_loaded	BIGINT	Rows loaded on last run
last_source_rows	BIGINT	Total source rows on last run
last_loaded_at	TIMESTAMP	Timestamp of last successful run

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CLI monitoring commands

# Latest execution report — one-line summary per dataset
openingest report

# Full run history
openingest history
openingest history --limit 10

# Full monitoring dashboard — KPIs, dataset health, quality trends
openingest dashboard

The dashboard shows:

• Pipeline KPIs (latest run status, total rows, quality score, duration)
• Dataset health snapshot (per-dataset rows, quality, watermark)
• Dataset-level trends across the last 20 runs
• Incremental loading statistics (new records, skipped, latest watermark)
• Quality distribution (PASS / FAIL counts)
• Slowest datasets by average duration

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Notifications (v2.5)

Configure in configs/pipeline.yaml:

notifications:
  slack:
    webhook: ${SLACK_WEBHOOK_URL}
    on: [success, failure]      # which events trigger a notification

  email:
    smtp_host: smtp.gmail.com
    smtp_port: 587
    username: ${EMAIL_USERNAME}
    password: ${EMAIL_PASSWORD}
    from: [e-mail]
    to:
      - [e-mail]
      - [e-mail]
    on: [failure]               # only alert on failure

Both channels support on: [success], on: [failure], or on: [success, failure] independently.

Slack message format

Uses Slack Block Kit. Example success notification:

✅ OpenIngest — Pipeline SUCCESS

Run ID   : OI-20260703-3BB09C
Status   : SUCCESS
Datasets : 8
Rows     : 174,777
Duration : 4.21s

Email format

Plain-text email via SMTP. Subject line:

✅ OpenIngest Pipeline: SUCCESS — OI-20260703-3BB09C

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Data lineage (v3.0)

The lineage engine builds a directed graph of how data flows through the pipeline per dataset.

from core.lineage import LineageGraph

graph = LineageGraph()

# Build lineage for all datasets after a run
for dataset in datasets:
    graph.add_dataset_lineage(dataset)

# Terminal view
graph.print_ascii()

# Mermaid diagram (paste into any Mermaid renderer)
print(graph.to_mermaid())

# JSON for web UI or API
data = graph.to_dict()

ASCII output

  customers
  ├─ customers.csv                   [source]
  │
  ├─ Dataset Discovery
  │
  ├─ Schema Validation               [PASS]
  │
  ├─ Quality Engine                  [PASS]  100.0%
  │
  ├─ Ingest (replace)                174,777 rows
  │
  └─ stg_customers                   [staging]

Mermaid output

flowchart TD
    customers_source[("customers.csv")]
    customers_discovery[["Dataset Discovery"]]
    customers_schema_validation[["Schema Validation"]]
    customers_quality_check{{"Quality Engine"}}
    customers_ingest[["Ingest (replace)"]]
    customers_staging[("stg_customers")]

    customers_source --> customers_discovery
    customers_discovery --> customers_schema_validation
    customers_schema_validation --> customers_quality_check
    customers_quality_check --> customers_ingest
    customers_ingest --> customers_staging

---

Observability queries

Direct SQL examples against the metadata tables:

-- Latest run summary
SELECT run_id, status, total_datasets, total_rows, total_duration, started_at
FROM pipeline_runs
ORDER BY started_at DESC
LIMIT 1;

-- Quality score trend for orders
SELECT r.started_at, q.score, q.status
FROM pipeline_quality_runs q
JOIN pipeline_runs r USING (run_id)
WHERE q.dataset_name = 'orders'
ORDER BY r.started_at DESC
LIMIT 30;

-- Rows loaded per dataset on the latest run
WITH latest AS (SELECT run_id FROM pipeline_runs ORDER BY started_at DESC LIMIT 1)
SELECT dataset_name, rows_loaded, load_strategy, load_mode, watermark_value
FROM pipeline_dataset_runs
WHERE run_id = (SELECT run_id FROM latest)
ORDER BY dataset_name;

-- Datasets that failed quality checks in the last 7 days
SELECT r.started_at, q.dataset_name, q.score, q.checks_failed
FROM pipeline_quality_runs q
JOIN pipeline_runs r USING (run_id)
WHERE q.status = 'FAIL'
  AND r.started_at >= NOW() - INTERVAL '7 days'
ORDER BY r.started_at DESC;

-- Current watermark state for incremental datasets
SELECT dataset_name, incremental_column, last_watermark_value,
       last_rows_loaded, last_loaded_at
FROM pipeline_incremental_state
ORDER BY dataset_name;