Read server.project_dir from instance.yaml (default: 'data-analyst').
Replace hardcoded 'data-analyst' folder name and 'data_analyst_server'
SSH key name in dashboard template with Jinja variables.
Replace hardcoded 'data-analyst' and '~/.ssh/data_analyst_server' in
the copyBootstrapInstructions JS function with values from instance config.
Pass ssh_alias and ssh_key to dashboard template context.
Config reads server.ssh_alias and server.ssh_key from instance.yaml
(defaults: 'data-analyst' and '~/.ssh/data_analyst_server' for backward compat).
App.py substitutes {ssh_alias} and {ssh_key} in bootstrap.yaml template.
Flask will now include git commit hash as URL parameter (v=abc1234)
for metric_modal.js and other static assets. This ensures browser
doesn't cache stale JavaScript when code changes.
Cache invalidation based on actual git history rather than timestamps.
FQN can contain spaces (e.g., 'Active2 Customers') which get URL-encoded
as 'Active2%20Customers' in the path parameter. Need to decode before
passing to OpenMetadata API.
OpenMetadata uses different field names than expected:
- metricExpression instead of expression
- metricType instead of type
- unitOfMeasurement instead of unit
- granularity instead of grain
Remove 'fields' query parameter from /api/v1/metrics - returns 400 Bad Request
when invalid field names are specified. Let API return full metric objects.
Update parsing to extract metadata from proper OpenMetadata fields instead
of relying on tags (tags are optional, fields are always present).
- Add get_metric_by_fqn() to OpenMetadataClient
- Add get_metrics() to CatalogEnricher with TTL caching
- Implement _parse_om_metric() to extract category/grain from OpenMetadata tags
- Implement _load_metrics_from_catalog() to fetch and categorize metrics
- Implement _build_om_metric_detail() to convert OpenMetadata format to MetricParser JSON
- Add /api/catalog/metrics/<fqn> endpoint for metric detail modal
- Update _load_metrics_data() to prefer catalog over YAML fallback
- Update metric_modal.js to route catalog:{fqn} to catalog API endpoint
- Delete 10 demo YAML files from docs/metrics/
- Replace metric tests with new unit tests for catalog parsing functions (19 tests)
Catalog metrics provide single source of truth vs maintaining demo YAML files.
UI remains unchanged - only data source changes from YAML to OpenMetadata catalog.
Pass partition_by, partition_granularity, partition_column_type, and
incremental_window_days from YAML to TableConfig to avoid validation errors
when sync_strategy='partitioned'
- API endpoint /api/catalog/profile/ enriches response with catalog metadata (tier, owners, tags, url)
- renderOverview() template function displays 'Data Catalog' section with tier, owners, tags, and catalog link
- Graceful degradation: section only shown if catalog enrichment available
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
The activity_center view was passing an empty dict but the template
expected nested keys (executive_summary, maturity_roadmap, etc).
Added _build_activity_data() that returns properly structured defaults.
- 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
Extract 4 self-contained services into services/ module:
- server/telegram_bot/ -> services/telegram_bot/
- server/ws_gateway/ -> services/ws_gateway/
- server/corporate_memory/ -> services/corporate_memory/
- server/session_collector.py -> services/session_collector/
Each service now has its own systemd/ directory with .service and .timer files.
deploy.sh updated to auto-discover service units from services/*/systemd/*.
server/ now contains only deployment infrastructure (deploy.sh, setup scripts,
bin/ management tools, sudoers, nginx config).
All imports updated: webapp/app.py, server/bin/ scripts, systemd ExecStart paths.
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.
