- 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.
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.
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
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).
Without explicit bqstorage_client parameter, to_arrow_iterable() silently
falls back to REST API pagination (~5K rows/sec). With explicit client,
it uses parallel gRPC streams via BQ Storage API (~300K rows/sec).
No temp table materialization - BQ already writes query results to an
internal temp table automatically. We just tell the reader to use the
fast gRPC path instead of slow HTTP pagination.
QueryJob only has to_arrow(), not to_arrow_iterable().
Must call query_job.result() first to get RowIterator,
which has the streaming to_arrow_iterable() method.
Replace to_arrow() (loads entire result into RAM) with
to_arrow_iterable() (streams RecordBatches). Each batch is written
directly to disk via ParquetWriter - constant memory regardless
of table size. Prevents OOM on 8GB server for multi-million row tables.
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).
Zero-dependency profiler for Parquet/CSV files producing JSON profiles
with column statistics, histograms, alerts, and sample data.
Supports single files, directories, composite primary keys, and
optional HTML report generation.
Extend theming from 3 CSS variables (primary colors only) to 14
configurable properties covering colors, fonts, borders, and shape.
All values are optional with sensible defaults.
- New _theme.html include replaces duplicated inline injection
- Wire theme include into all 7 templates (base, login, dashboard,
catalog, admin_tables, activity_center, corporate_memory)
- Conditional font loading: skip default Inter when custom font_url set
- Config.theme_overrides() classmethod generates CSS variable dict
- Visual theme-reference.html guide for instance configurators
- Document all theme keys in instance.yaml.example
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)
Move hardcoded Keboola SVG logo from 4 templates into config.
Templates now use {{ config.LOGO_SVG | safe }}.
Default falls back to Keboola logo when not configured.
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.
Add docs/metrics/ to project structure, Business Metrics and Table
Registry patterns to implementation details, password auth provider
to extensibility section, fix sync command for returning users.
These Keboola-specific data source cards don't belong in the OSS repo.
The catalog now shows only dynamic content: Core Business Data (from
data_description.md) and Business Metrics (from docs/metrics/*.yml).
Also update auto-install.md with Business Metrics documentation,
pipeline diagram, and expanded checklist.
- 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
Generator now supports --format {csv,parquet,both}. Parquet mode
uses src.parquet_manager.ParquetManager for snappy compression,
proper column types (DATE, TIMESTAMP, DOUBLE), and metadata.
No more ad-hoc pandas conversion needed on the server.
- Check for existing SSH config entry before overwriting
- Use --no-perms --no-group in rsync (fixes macOS permission errors)
- Explicit mkdir instead of brace expansion (Claude Code compatibility)
- Gracefully handle missing server directories (empty server is OK)
- Conditional steps for setup_views.sh and CLAUDE.md template
- Get Started page now has 4 steps (folder, SSH key, pubkey, register)
- After account creation, dashboard shows prominent "Set up your local
environment" CTA with claude command and Copy Setup Instructions
- CTA only visible when user hasn't synced yet (last_sync is empty)
- Bottom banner demoted to subtle secondary style for returning users
- Merge steps 1-3 into a single dark terminal block with copy buttons
- Inline registration form with single-row layout for step 4
- Compact step 5 with Claude Code command and copy button on one line
- Full-width layout (960px) instead of narrow 640px column
- Everything fits on one screen without scrolling
Merge the two-column layout (setup steps + registration form) into one
unified flow. Step 4 now contains the registration form inline, creating
a natural top-to-bottom progression through the setup process.
- Add steps 2-4 (SSH key generation, copy pubkey, create account)
- Fix clipboard copy using textarea fallback for non-HTTPS contexts
- Generate simple plain-text Claude Code prompt instead of full YAML
- Show what Claude will do (SSH, rsync, DuckDB, CLAUDE.md)
- Support comma-separated domains in auth.allowed_domain config
- Use full email as system username (user@domain.com -> user_domain_com)
to avoid collisions with reserved names and across domains
- Update both auth providers (google, email) for multi-domain display
- Add tests for username generation and update email auth tests
New pluggable auth provider that sends passwordless sign-in links.
Works with domain restriction (same as Google OAuth). Falls back to
showing the link in browser when SMTP is not configured (dev mode).
Add auth providers, standalone services, and service patterns
to project structure in README, ARCHITECTURE, and CLAUDE.md.
Reflects the completed extraction of auth, telegram bot,
ws gateway, corporate memory, and session collector.
Move dev_run.py and test_sync.sh from dev_scripts/ to scripts/,
eliminating the separate dev_scripts directory. Update scripts
README with development scripts section.
All references to server/telegram_bot/, server/ws_gateway/,
server/corporate_memory/, server/session_collector* updated
to their new locations under services/.
