data-dictionary-builder 0.1.4

Extract database metadata, generate dbt-compatible YAML, compare schemas, and deliver reports — all in a single Python import.

data_dictionary_builder

A Python library that automates database documentation — extract live schema metadata, generate dbt-compatible YAML, compare schemas across environments, and deliver PDF reports, all in a single import.

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What's new in v0.1.4

• Multi-recipient email delivery — email_to now accepts a list of addresses (e.g. ["[e-mail]", "[e-mail]"]) in addition to a single string. The EMAIL_TO environment variable supports comma-separated addresses (EMAIL_TO=[e-mail],[e-mail]). All recipients receive the PDF attachment in one send.
• Multi-target Slack delivery — slack_target now accepts a list of targets (e.g. ["#data-alerts", "U012AB3CD"]). The SLACK_NOTIFY_TARGET environment variable supports comma-separated values. Each target receives the full Block Kit report and optional PDF upload independently.
• send_notification updated — both email_to and slack_target parameters accept strings or lists; the method routes correctly for either form with no API change.

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What's new in v0.1.3

• Slack notifications — deliver schema comparison reports directly to any Slack channel or DM alongside the existing email delivery. Use notification_type="slack" or "both" in send_notification(). Requires a Bot User OAuth Token (xoxb-…) and the slack extra: pip install "data-dictionary-builder[slack]".
• Optimised metadata generation — parallel extraction is faster with improved thread scheduling and reduced connection overhead across all supported databases.
• Smarter schema comparison — type normalisation has been expanded to cover more cross-database equivalences, reducing false-positive mismatches in mixed-engine pipelines.
• Leaner exports — JSON metadata exports are more compact, and the to_dict() / from_dict() round-trip is validated automatically to ensure safe use in Airflow XCom and downstream catalog APIs.

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Installation

pip

# Core library (SQLite works out of the box)
pip install data-dictionary-builder

# With the connectors you need
pip install "data-dictionary-builder[postgres]"
pip install "data-dictionary-builder[mysql]"
pip install "data-dictionary-builder[clickhouse]"         # ClickHouse HTTP/HTTPS
pip install "data-dictionary-builder[clickhouse-native]"  # ClickHouse native TCP
pip install "data-dictionary-builder[spanner]"
pip install "data-dictionary-builder[oracle]"
pip install "data-dictionary-builder[sqlserver]"

# Everything at once
pip install "data-dictionary-builder[all]"

uv (recommended — faster resolver, built-in virtual environments)

# Install uv
pip install uv
# or on macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh

# Add to your project
uv add data-dictionary-builder

# With specific connectors
uv add "data-dictionary-builder[postgres]"
uv add "data-dictionary-builder[mysql]"
uv add "data-dictionary-builder[clickhouse]"         # ClickHouse HTTP/HTTPS
uv add "data-dictionary-builder[clickhouse-native]"  # ClickHouse native TCP
uv add "data-dictionary-builder[oracle]"
uv add "data-dictionary-builder[sqlserver]"
uv add "data-dictionary-builder[spanner]"

# Everything at once
uv add "data-dictionary-builder[all]"

Or use the CLI to install connectors after the fact:

ddgen install postgres
ddgen install clickhouse
ddgen install oracle
ddgen install sqlserver
ddgen install all

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Supported Databases

Database	Extra	Driver
SQLite	(built-in)	sqlite3 (stdlib)
PostgreSQL	[postgres]	psycopg2-binary
MySQL / MariaDB	[mysql]	PyMySQL
ClickHouse	[clickhouse]	clickhouse-connect (HTTP/HTTPS) · clickhouse-driver (native TCP, optional)
Oracle Database	[oracle]	oracledb (thin mode — no Oracle Client needed)
SQL Server / Azure SQL	[sqlserver]	pymssql
Google Cloud Spanner	[spanner]	google-cloud-spanner

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Quick Start

from data_dictionary_builder import MetadataExtractor, YAMLGenerator, DDHelper, ExecutionTimer

timer  = ExecutionTimer()
helper = DDHelper(".")      # creates models/, reports/json/, reports/pdf/

with timer.task("Extract"):
    with MetadataExtractor(
        db_type="postgres", host="localhost", port=5432,
        database="mydb", user="readonly", password="secret",
    ) as ext:
        db_meta = ext.extract_all_schemas(
            schema_filter=["public", "analytics"],
            parallel_workers=8,
        )

with timer.task("Generate YAML"):
    YAMLGenerator(output_dir=str(helper.models_dir)).generate_yaml_files(db_meta)

timer.summary()

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CLI

# Show all commands and supported databases
ddgen --help

# Full module and API reference
ddgen features

# Check which connectors are installed
ddgen connectors

# Install a connector
ddgen install postgres
ddgen install clickhouse
ddgen install all

# Extract metadata and generate YAML in one step
ddgen extract --db-type postgres --host prod.db.io --database mydb --user readonly

# Compare two environments
ddgen compare --source-host prod.db.io --dest-host staging.db.io --source-database mydb

# Show library version and connector summary
ddgen info

# Show version number
ddgen --version

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Schema Comparison

from data_dictionary_builder import SchemaComparator, DDHelper

helper = DDHelper(".")
report = SchemaComparator(
    source_config={"db_type": "postgres", "host": "prod-db", ...},
    destination_config={"db_type": "postgres", "host": "staging-db", ...},
).compare_and_generate_report("public", include_yaml_gaps=True)

json_path = helper.save_report(report)
pdf_path  = helper.compile_pdf(source_json=json_path)

# Send via email, Slack, or both — credentials fall back to env vars
# email_to and slack_target accept a string or a list of recipients
helper.send_notification(
    notification_type="both",    # "email" | "slack" | "both"
    report=report,
    pdf_path=pdf_path,
    email_to=["[e-mail]", "[e-mail]"],
    slack_target=["#data-alerts", "#data-eng"],
)

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Airflow Integration

DatabaseMetadata serialises to/from plain dicts for XCom:

@task
def extract():
    with MetadataExtractor(**config) as ext:
        return ext.extract_all_schemas(parallel_workers=8).to_dict()

@task
def generate_yaml(db_meta_dict):
    from data_dictionary_builder import DatabaseMetadata, YAMLGenerator
    YAMLGenerator("./models").generate_yaml_files(DatabaseMetadata.from_dict(db_meta_dict))

See tests/airflow_dag_example.py for a complete DAG.

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Key Features

• Parallel extraction — ThreadPoolExecutor with configurable workers; ClickHouse uses 2 queries and PostgreSQL uses 5 queries per schema regardless of table count
• Dual ClickHouse transport — HTTP/HTTPS via clickhouse-connect (default) or native TCP via clickhouse-driver; auto-detected, with dynamic port defaults based on transport and TLS
• Schema filtering — exact, glob, prefix, suffix, contains, regex — mix freely
• Smart YAML merge — re-running never overwrites descriptions you've written by hand
• YAML-aware gap detection — documentation coverage checks read from your existing YAML files, so descriptions you've added are always recognised
• Cross-database comparison — compare any two database types; type aliases normalised before diffing
• PDF reports — paginated, no row limits, table of contents (requires reportlab)
• Unified notifications — send_notification(notification_type="email"|"slack"|"both", ...) delivers PDF reports via SMTP email, Slack (channel or DM), or both simultaneously; all credentials fall back to environment variables
• Email delivery — SMTP with env-var credential fallback; PDF attached automatically
• Slack delivery — Block Kit–formatted comparison summaries; optional PDF file upload; supports #channel, @user, channel IDs, and user IDs
• ExecutionTimer — named task timing with a formatted summary table
• Server mode — omit database to scan all databases on a MySQL, ClickHouse, or PostgreSQL server
• Rich CLI — ddgen extract, ddgen compare, ddgen features (full API reference), ddgen connectors, ddgen install

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Environment Variables

Set these in a .env file (see tests/.env.example) or in your shell:

# ── Notification channel ───────────────────────────────────────────────
# "email" | "slack" | "both"  (default: email)
NOTIFICATION_TYPE=email

# ── SMTP — DDHelper.send_notification() / send_report_email() ──────────
SMTP_HOST=smtp.gmail.com
SMTP_PORT=587
SMTP_USER=[e-mail]
SMTP_PASSWORD=xxxx xxxx xxxx xxxx
EMAIL_TO=[e-mail],[e-mail]   # comma-separated for multiple recipients

# ── Slack — DDHelper.send_notification() ───────────────────────────────
# Bot Token Scopes required: chat:write, files:write, channels:read,
#                            users:read, im:write
SLACK_BOT_TOKEN=xoxb-your-token-here
SLACK_NOTIFY_TARGET=#data-alerts,#data-eng   # comma-separated for multiple targets

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Documentation

Full user guide, API reference, and troubleshooting: DOCUMENTATION.md

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License

MIT — free to use, modify, and distribute in personal and commercial projects.