dbqt 0.1.11

DataBase Quality Tool

DBQT (DataBase Quality Tool) 🎯

DBQT is a lightweight, Python-first data quality testing framework that helps data teams maintain high-quality data through automated checks and intelligent suggestions.

🛠️ Current Tools

Column Comparison Tool (dbqt compare)

Compare schemas between databases or files:

• Table-level comparison
• Column-level comparison with data type compatibility checks
• Support for CSV and Parquet files
• Handles nested Parquet schemas (arrays, structs, maps)
• Intelligent data type compatibility checking
• Generates detailed Excel report with:
  • Table differences
  • Column differences
  • Data type mismatches
  • Formatted worksheets for easy analysis

Usage:

dbqt compare source_schema.csv target_schema.csv
# Or compare Parquet files directly:
dbqt compare source.parquet target.parquet

To generate CSV schema files from your database, run this query:

SELECT
    upper(table_schema) as SCH, --optional
    upper(table_name) as TABLE_NAME,
    upper(column_name) as COL_NAME,
    upper(data_type) as DATA_TYPE --optional
FROM information_schema.columns
where UPPER(table_schema) = UPPER('YOUR_SCHEMA')
order by table_name, ordinal_position;

Export the results to CSV format to use with the compare tool.

Parquet Combine Tool (dbqt combine)

Combine multiple Parquet files into a single file:

• Validates schema compatibility
• Preserves nested data structures
• Handles large datasets efficiently

Usage:

dbqt combine [output.parquet]  # Combines all .parquet files in current directory

Database Statistics Tool (dbqt dbstats)

Collect and analyze database statistics:

• Fetches table row counts in parallel for faster execution.
• Updates statistics in a CSV file.
• Configurable through YAML.

Usage:

dbqt dbstats config.yaml

Example config.yaml:

# Database connection configuration
connection:
  type: mysql  # mysql, snowflake, duckdb, csv, parquet, s3parquet
  host: localhost
  user: myuser
  password: mypassword
  database: mydb
  # Optional AWS configs for s3parquet
  # aws_profile: default
  # aws_region: us-west-2
  # bucket: my-bucket

  # Snowflake-specific configs
  # type: snowflake
  # account: your_account.region
  # warehouse: YOUR_WAREHOUSE
  # database: YOUR_DB
  # schema: YOUR_SCHEMA
  # role: YOUR_ROLE
  # authenticator: externalbrowser  # Optional: use SSO authentication
  # user: your_username
  # password: your_password  # Not needed if using externalbrowser auth

# Path to CSV file containing table names to analyze
tables_file: tables.csv

The tables.csv file should contain at minimum a table_name column. The tool will add/update a row_count column with the results.

Null Column Check Tool (dbqt nullcheck)

Check for columns where all records are null across multiple tables in Snowflake.

• Identifies completely empty columns.
• Reports on columns with low-distinct values (<=5).
• Efficiently checks multiple tables in parallel.
• Generates a markdown report summarizing the findings.

Usage:

dbqt nullcheck --config snowflake_config.yaml

This tool currently only supports Snowflake.

Dynamic Query Tool (dbqt dynamic-query)

Run a dynamic SQL query against Athena for a list of values from a CSV file.

• Substitutes values from a CSV into a query template.
• Executes queries sequentially and writes results to an output file.
• Useful for running the same query against multiple tables or with different parameters.

Usage:

dbqt dynamic-query --config athena_config.yaml --csv values.csv --query "SELECT COUNT(1) FROM {var_from_csv}"

This tool currently only supports AWS Athena.

Parquetizer Tool (dbqt parquetizer)

A utility to recursively find files that are Parquet but lack the .parquet extension and rename them.

• Scans a directory for files without extensions.
• Validates if a file is a Parquet file by checking its magic bytes.
• Renames valid Parquet files to include the .parquet extension.

Usage:

dbqt parquetizer [directory] # Scans from the specified directory (or current if not provided)

🚀 Future Plans

Core DBQT Features (Coming Soon)

• AI-Powered column classification using Qwen2 0.5B
• Automatic check suggestions
• 20+ built-in data quality checks
• Python-first API
• No backend required
• Customizable check framework

Planned Checks

• Completeness checks (null values)
• Uniqueness validation
• Format validation (regex, dates, emails)
• Range/boundary checks
• Value validation
• Statistical analysis
• Dependency checks

Integration Plans

• Data pipeline integration
• Scheduled runs
• Parallel check execution
• Multiple database backend support

📄 License

This project is licensed under the MIT License.