Instead of hardcoded Python constants, load profiler settings from config:
- instance.yaml: profiler section with all parameters
- Defaults: fallback to sensible defaults if config not found
- Centralized: all profiler tuning in one place, no code changes needed
Replace SAMPLE_THRESHOLD + SAMPLE_SIZE with single SAMPLE_SIZE:
- If table > SAMPLE_SIZE: sample that many rows
- Otherwise: use all rows
Cleaner, easier to configure.
Add OpenMetadata REST API connector and enricher to merge table/column metadata
from OpenMetadata catalog at sync and query time.
Changes:
- connectors/openmetadata/client.py: HTTP client for OM API
- connectors/openmetadata/enricher.py: Data enrichment with TTL cache
- tests/test_openmetadata_*: Unit tests for client and enricher
- src/config.py: Add catalog_fqn field to TableConfig
- src/data_sync.py: Use enricher in _generate_schema_yaml (catalog > BQ API > data_description.md)
- webapp/app.py: Initialize enricher, enrich catalog data with tags/tier/owners/url
- config/instance.yaml.example: Document openmetadata section
Features:
- FQN auto-derivation: bigquery.{table.id}
- TTL cache (default 1h) to avoid repeated API calls
- Graceful degradation: disabled if token missing, silent on HTTP errors
- Column description priority: catalog > BQ API > (none)
- Table description priority: catalog > data_description.md
Server has 8GB RAM with other services running. DuckDB defaults to
using all available memory, causing OOM killer when profiling large
tables (22M rows, 39 cols triggered 7.5GB RSS -> killed).
Propagate column selection and row filtering from data_description.md
through the BigQuery adapter to the BQ client. This enables exporting
only needed columns and applying date range filters at the SQL level,
critical for large DataView tables (e.g., 412-col unit_economics).
BigQuery connector that syncs BQ tables to local Parquet files via PyArrow
(no CSV intermediate step). Supports full refresh, timestamp-based
incremental (via incremental_column), and partition-based sync strategies.
- connectors/bigquery/client.py: BQ API wrapper with ADC auth, parameterized
queries, metadata cache, cross-project support (job project != data project)
- connectors/bigquery/adapter.py: DataSource implementation with merge/dedup
- src/config.py: Add incremental_column field to TableConfig
- 72 unit tests (mocked, no GCP SDK required)
- Profiler computes file_size_mb from actual parquet files when
sync_state.json is absent (sample data / no-sync deployments)
- Catalog header falls back to profiles.json for aggregate stats
(tables count, total rows) when sync_state.json is missing
Move all Jira-specific code into a self-contained connector module:
- 22 files moved via git mv (transform, service, webhook, scripts,
systemd units, tests, docs, bin helper)
- All imports updated to use connectors.jira.* paths
- Jira is now conditional: auto-detected via JIRA_DOMAIN env var
- Webapp registers Jira blueprint only when available
- Health service monitors Jira timers only when enabled
- Profiler loads Jira tables dynamically from filesystem
- Sync settings uses config-driven dependency validation
- Renamed keboola_platform_url -> custom_url in transform
- Updated deploy.sh, sudoers-deploy, backfill_gap.sh paths
- Fixed pytest.ini to skip live tests by default
Open-source AI data analyst platform extracted from internal repo.
Includes data sync engine, Keboola adapter, Flask web portal,
server deployment scripts, and configuration templates.
