Skip to content

Qualytics/qualytics-cli

BranchesTags

Repository files navigation

Qualytics CLI

This is a CLI tool for working with the Qualytics API. With this tool, you can manage your configurations, export checks, import checks, and more. It's built on top of the Typer CLI framework and uses the Rich library for enhanced terminal outputs.

Requirements

  • Python 3.9 or higher

Installation

From PyPI (recommended)

pip install qualytics-cli

Using uv (faster)

uv pip install qualytics-cli

From source

git clone https://github.com/Qualytics/qualytics-cli.git
cd qualytics-cli
uv sync
uv pip install -e .

Usage

Help

qualytics --help

Initializing the Configuration

You can set up your Qualytics URL and token using the init command:

qualytics init --url "https://your-qualytics.qualytics.io/" --token "YOUR_TOKEN_HERE"
Option Type Description Default Required
--url TEXT The URL to be set. Example: https://your-qualytics.qualytics.io/ None Yes
--token TEXT The token to be set. None Yes

Qualytics init help

qualytics init --help

Display Configuration

To view the currently saved configuration:

qualytics show-config

Export Checks

You can export checks to a file using the checks export command:

qualytics checks export --datastore DATASTORE_ID [--containers CONTAINER_IDS] [--tags TAG_NAMES] [--output LOCATION_TO_BE_EXPORTED]

By default, it saves the exported checks to ./qualytics/data_checks.json. However, you can specify a different output path with the --output option.

Option Type Description Default Required
--datastore INTEGER Datastore ID None Yes
--containers List of INTEGER Containers IDs None No
--tags List of TEXT Tag names None No
--status List of TEXT Status Active, Draft or Archived None No
--output TEXT Output file path ./qualytics/data_checks.json No

Export Check Templates

You can export check templates to the _export_check_templates table to an enrichment datastore.

qualytics checks export-templates --enrichment_datastore_id ENRICHMENT_DATASTORE_ID [--check_templates CHECK_TEMPLATE_IDS]
Option Type Description Required
--enrichment_datastore_id INTEGER The ID of the enrichment datastore where check templates will be exported. Yes
--check_templates TEXT Comma-separated list of check template IDs or array-like format. Example: "1, 2, 3" or "[1,2,3]". No

Import Checks

To import checks from a file:

qualytics checks import --datastore DATASTORE_ID_LIST [--input LOCATION_FROM_THE_EXPORT]

By default, it reads the checks from ./qualytics/data_checks.json. You can specify a different input file with the --input option.

Note: Any errors encountered during the importing of checks will be logged in ./qualytics/errors.log.

Option Type Description Default Required
--datastore TEXT Comma-separated list of Datastore IDs or array-like format. Example: 1,2,3,4,5 or "[1,2,3,4,5]" None Yes
--input TEXT Input file path HOME/.qualytics/data_checks.json No

Import Check Templates

You can import check templates from a file using the checks import-templates command:

qualytics checks import-templates [--input LOCATION_OF_CHECK_TEMPLATES]

By default, it reads the check templates from ./qualytics/data_checks_template.json. You can specify a different input file with the --input option.

Option Type Description Default Required
--input TEXT Input file path ./qualytics/data_checks_template.json No

Schedule Metadata Export

Allows you to schedule exports of metadata from your datastores using a specified crontab expression.

qualytics schedule export-metadata --crontab "CRONTAB_EXPRESSION" --datastore "DATASTORE_ID" [--containers "CONTAINER_IDS"] --options "EXPORT_OPTIONS"
Option Type Description Required
--crontab TEXT Crontab expression inside quotes, specifying when the task should run. Example: "0 * * * *" Yes
--datastore TEXT The datastore ID Yes
--containers TEXT Comma-separated list of container IDs or array-like format. Example: "1, 2, 3" or "[1,2,3]" No
--options TEXT Comma-separated list of options to export or "all". Example: "anomalies, checks, field-profiles" Yes

Run a Catalog Operation on a Datastore

Allows you to trigger a catalog operation on any current datastore (requires admin permissions on the datastore).

qualytics run catalog --datastore "DATASTORE_ID_LIST" --include "INCLUDE_LIST" --prune --recreate --background
Option Type Description Required
--datastore TEXT Comma-separated list of Datastore IDs or array-like format. Example: 1,2,3,4,5 or "[1,2,3,4,5]" Yes
--include TEXT Comma-separated list of include types or array-like format. Example: "table,view" or "[table,view]" No
--prune BOOL Prune the operation. Do not include if you want prune == false No
--recreate BOOL Recreate the operation. Do not include if you want recreate == false No
--background BOOL Starts the catalog but does not wait for the operation to finish No

Run a Profile Operation on a Datastore

Allows you to trigger a profile operation on any current datastore (requires admin permissions on the datastore).

qualytics run profile --datastore "DATASTORE_ID_LIST" --container_names "CONTAINER_NAMES_LIST" --container_tags "CONTAINER_TAGS_LIST"
--inference_threshold "INFERENCE_THRESHOLD" --infer_as_draft --max_records_analyzed_per_partition "MAX_RECORDS_ANALYZED_PER_PARTITION"
--max_count_testing_sample "MAX_COUNT_TESTING_SAMPLE" --percent_testing_threshold "PERCENT_TESTING_THRESHOLD" --high_correlation_threshold
"HIGH_CORRELATION_THRESHOLD" --greater_than_time "GREATER_THAN_TIME" --greater_than_batch "GREATER_THAN_BATCH" --histogram_max_distinct_values
"HISTOGRAM_MAX_DISTINCT_VALUES" --background
Option Type Description Required
--datastore TEXT Comma-separated list of Datastore IDs or array-like format. Example: 1,2,3,4,5 or "[1,2,3,4,5]" Yes
--container_names TEXT Comma-separated list of container names or array-like format. Example: "container1,container2" or "[container1,container2]" No
--container_tags TEXT Comma-separated list of container tags or array-like format. Example: "tag1,tag2" or "[tag1,tag2]" No
--inference_threshold INT Inference quality checks threshold in profile from 0 to 5. Do not include if inference_threshold == 0 No
--infer_as_draft BOOL Infer all quality checks in profile as DRAFT. Do not include if you want infer_as_draft == False No
--max_records_analyzed_per_partition INT Number of max records analyzed per partition No
--max_count_testing_sample INT The number of records accumulated during profiling for validation of inferred checks. Capped at 100,000 No
--percent_testing_threshold FLOAT Percent of testing threshold No
--high_correlation_threshold FLOAT Number of correlation threshold No
--greater_than_time DATETIME Only include rows where the incremental field's value is greater than this time. Use one of these formats %Y-%m-%dT%H:%M:%S or %Y-%m-%d %H:%M:%S No
--greater_than_batch FLOAT Only include rows where the incremental field's value is greater than this number No
--histogram_max_distinct_values INT Number of max distinct values in the histogram No
--background BOOL Starts the profile operation but does not wait for the operation to finish No

Run a Scan Operation on a Datastore

Allows you to trigger a scan operation on a datastore (requires admin permissions on the datastore).

qualytics run scan --datastore "DATASTORE_ID_LIST" --container_names "CONTAINER_NAMES_LIST" --container_tags "CONTAINER_TAGS_LIST"
--incremental --remediation --max_records_analyzed_per_partition "MAX_RECORDS_ANALYZED_PER_PARTITION" --enrichment_source_record_limit
--greater_than_time "GREATER_THAN_TIME" --greater_than_batch "GREATER_THAN_BATCH" --background
Option Type Description Required
--datastore TEXT Comma-separated list of Datastore IDs or array-like format. Example: 1,2,3,4,5 or "[1,2,3,4,5]" Yes
--container_names TEXT Comma-separated list of container names or array-like format. Example: "container1,container2" or "[container1,container2]" No
--container_tags TEXT Comma-separated list of container tags or array-like format. Example: "tag1,tag2" or "[tag1,tag2]" No
--incremental BOOL Process only new or updated records since the last incremental scan No
--remediation TEXT Replication strategy for source tables in the enrichment datastore. Either 'append', 'overwrite', or 'none' No
--max_records_analyzed_per_partition INT Number of max records analyzed per partition. Value must be greater than or equal to 0 No
--enrichment_source_record_limit INT Limit of enrichment source records per run. Value must be greater than or equal to -1 No
--greater_than_time DATETIME Only include rows where the incremental field's value is greater than this time. Use one of these formats %Y-%m-%dT%H:%M:%S or %Y-%m-%d %H:%M:%S No
--greater_than_batch FLOAT Only include rows where the incremental field's value is greater than this number No
--background BOOL Starts the scan operation but does not wait for the operation to finish No

Check Operation Status

Allows a user to check an operation's status. Useful if a user triggered an operation but had it running in the background.

qualytics operation check_status --ids "OPERATION_IDS"
Option Type Description Required
--ids TEXT Comma-separated list of Operation IDs or array-like format. Example: 1,2,3,4,5 or "[1,2,3,4,5]" Yes

Configuring Connections

Before creating datastores, you need to define your database connections in a YAML configuration file. This allows you to:

  • Reuse connection configurations across multiple datastores
  • Manage connection credentials in a centralized location
  • Automatically create new connections or reference existing ones in Qualytics

Setting Up connections.yml

Create a file at ~/.qualytics/config/connections.yml with your connection configurations.

Important: All connection values must be directly written in the connections.yml file. The CLI does not use .env files for connection configuration.

Configuration Structure

connections:
  <connection_key>:                  # Identifier for this connection block
    type: <connection_type>          # Required: Database type (postgresql, snowflake, mysql, etc.)
    name: <connection_name>           # Required: Unique name to identify this connection
    parameters:                       # Required: Connection-specific parameters
      host: <hostname>
      port: <port_number>
      user: <username>
      password: <password>
      # Additional parameters vary by connection type

Supported Connection Types

  • postgresql - PostgreSQL databases
  • snowflake - Snowflake data warehouse
  • mysql - MySQL databases
  • bigquery - Google BigQuery
  • redshift - Amazon Redshift
  • abfs - Azure Blob File System
  • sqlserver - Microsoft SQL Server
  • And more...

Connection Examples

PostgreSQL Connection

connections:
  my_postgres:
    type: postgresql
    name: production_postgres_db
    parameters:
      host: postgres.example.com
      port: 5432
      user: postgres_user
      password: your_secure_password

Snowflake Connection (Username/Password)

connections:
  my_snowflake:
    type: snowflake
    name: prod_snowflake_connection
    parameters:
      host: account.snowflakecomputing.com
      role: DATA_ANALYST
      warehouse: COMPUTE_WH
      user: snowflake_user
      password: your_snowflake_password

Snowflake Connection (Key-Pair Authentication)

connections:
  my_snowflake_keypair:
    type: snowflake
    name: prod_snowflake_keypair
    parameters:
      host: account.snowflakecomputing.com
      role: DATA_ANALYST
      warehouse: COMPUTE_WH
      database: ANALYTICS_DB
      schema: PUBLIC
      authentication:
        method: keypair
        user: snowflake_user
        private_key_path: /path/to/private_key.pem

MySQL Connection

connections:
  my_mysql:
    type: mysql
    name: prod_mysql_db
    parameters:
      host: mysql.example.com
      port: 3306
      user: mysql_user
      password: mysql_password

BigQuery Connection

connections:
  my_bigquery:
    type: bigquery
    name: prod_bigquery
    parameters:
      project_id: my-gcp-project-id
      credentials_path: /path/to/service-account-key.json

Azure Blob File System (ABFS)

connections:
  my_abfs:
    type: abfs
    name: azure_data_lake
    parameters:
      storage_account: mystorageaccount
      container: mycontainer
      access_key: your_access_key

Complete Example connections.yml

connections:
  # PostgreSQL for production analytics
  prod_postgres:
    type: postgresql
    name: production_analytics
    parameters:
      host: analytics.postgres.example.com
      port: 5432
      user: analyst
      password: secure_pass_123

  # Snowflake data warehouse
  snowflake_dw:
    type: snowflake
    name: data_warehouse
    parameters:
      host: mycompany.snowflakecomputing.com
      role: ANALYST_ROLE
      warehouse: ANALYTICS_WH
      user: dw_user
      password: snowflake_password

  # Development MySQL database
  dev_mysql:
    type: mysql
    name: dev_database
    parameters:
      host: localhost
      port: 3306
      user: dev_user
      password: dev_password

Security Notes

  • File Permissions: Ensure your connections.yml file has restricted permissions: 
    chmod 600 ~/.qualytics/config/connections.yml
  • Sensitive Data: Passwords, tokens, and keys are automatically redacted in CLI output
  • Version Control: Never commit connections.yml to version control. Add it to .gitignore

How Connections Work with Datastores

When creating a datastore with --connection-name:

  1. Check Existing: The CLI checks if a connection with that name already exists in your Qualytics instance
  2. Reuse Existing: If found, the CLI uses the existing connection ID automatically
  3. Create New: If not found, the CLI creates a new connection using the configuration from connections.yml

This means you can define your connection once and reuse it across multiple datastores!

Add a New Datastore

Create a new datastore in Qualytics. You can either reference an existing connection by ID or specify a connection name from your connections.yml file.

Example: Adding a Regular Datastore

qualytics datastore new \
  --name "Production Analytics" \
  --connection-name "prod_snowflake_connection" \
  --database "ANALYTICS_DB" \
  --schema "PUBLIC" \
  --tags "production,analytics" \
  --teams "data-team,engineering" \
  --trigger-catalog

Example: Adding an Enrichment Datastore

qualytics datastore new \
  --name "Data Quality Enrichment" \
  --connection-name "enrichment_db_connection" \
  --database "DQ_ENRICHMENT" \
  --schema "QUALITY" \
  --enrichment-only \
  --enrichment-prefix "dq_" \
  --enrichment-source-record-limit 1000 \
  --enrichment-remediation-strategy "append" \
  --trigger-catalog
Option Type Description Required
--name TEXT Name for the datastore Yes
--connection-name TEXT Connection name from the 'name' field in connections.yml (mutually exclusive with connection-id) No
--connection-id INTEGER Existing connection ID to reference (mutually exclusive with connection-name) No
--database TEXT The database name from the connection being used Yes
--schema TEXT The schema name from the connection being used Yes
--tags TEXT Comma-separated list of tags No
--teams TEXT Comma-separated list of team names No
--enrichment-only BOOL Set if datastore will be an enrichment one (use flag to enable) No
--enrichment-prefix TEXT Prefix for enrichment artifacts No
--enrichment-source-record-limit INTEGER Limit of enrichment source records (min: 1) No
--enrichment-remediation-strategy TEXT Strategy for enrichment: 'append', 'overwrite', or 'none' (default: 'none') No
--high-count-rollup-threshold INTEGER High count rollup threshold (min: 1) No
--trigger-catalog/--no-trigger-catalog BOOL Whether to trigger catalog after creation (default: True) No
--dry-run BOOL Print payload only without making HTTP request No

Note: You must provide either --connection-name or --connection-id, but not both. Use --connection-name to create a new connection from your YAML config, or --connection-id to reference an existing connection.

List All Datastores

qualytics datastore list

Get a Datastore

Retrieve a datastore by either its ID or name.

By ID:

qualytics datastore get --id DATASTORE_ID

By Name:

qualytics datastore get --name "My Datastore Name"
Option Type Description Required
--id INTEGER Datastore ID (mutually exclusive with --name) No*
--name TEXT Datastore name (mutually exclusive with --id) No*

Note: You must provide either --id or --name, but not both.

Remove a Datastore

qualytics datastore remove --id DATASTORE_ID

Warning: Use with caution as this will permanently delete the datastore.

Computed Tables

The computed-tables command group allows you to import computed tables from files and automatically create quality checks for error detection queries.

Import Computed Tables

Import computed tables from a file (Excel, CSV, or TXT) and optionally create satisfiesExpression checks.

qualytics computed-tables import --datastore DATASTORE_ID --input FILE_PATH [OPTIONS]

Input File Structure

The input file must have 3 columns in positional order (the first row is treated as a header and skipped):

Column Name Required Description
1 name Yes Unique identifier for the computed table
2 description No Description stored in metadata and check
3 query Yes SQL query for the computed table

Important: Column names in the header row don't matter - only the position matters. You can name them anything (e.g., check_id, check_description, check_query).

Excel File Example (.xlsx)

check_id check_description check_query
CHK001 Detect orders with negative totals SELECT * FROM sales_orders WHERE total_amount < 0
CHK002 Find customers without email SELECT * FROM customer_master WHERE email IS NULL OR email = ''
CHK003 Identify duplicate invoices SELECT invoice_no, COUNT() FROM invoices GROUP BY invoice_no HAVING COUNT() > 1

CSV File Example (.csv)

check_id,check_description,check_query
CHK001,Detect orders with negative totals,"SELECT * FROM sales_orders WHERE total_amount < 0"
CHK002,Find customers without email,"SELECT * FROM customer_master WHERE email IS NULL OR email = ''"
CHK003,Identify duplicate invoices,"SELECT invoice_no, COUNT(*) FROM invoices GROUP BY invoice_no HAVING COUNT(*) > 1"

Note: For multiline SQL queries in CSV, wrap the entire query in double quotes:

check_id,check_description,check_query
CHK004,Complex order validation,"SELECT o.*
FROM sales_orders o
JOIN customer_master c ON o.customer_id = c.id
WHERE o.status = 'SHIPPED'
  AND c.country IS NULL"

Validation Rules

The import process validates each row before processing:

Validation Behavior
Empty name Row is skipped with warning
Empty query Row is skipped with warning
Empty description Row is processed (description defaults to "")
Duplicate name Second occurrence is skipped with warning
Blank row Row is skipped silently

Example validation output:

Found 5 records in the file.
Warnings during validation:
  - Row 3: Empty name, skipping.
  - Row 4: 'CHK001' has empty query, skipping.
  - Row 7: Duplicate name 'CHK002' (first seen at row 2), skipping.
3 valid records to import.

Options

Option Type Description Default
--datastore INTEGER Datastore ID to create computed tables in Required
--input TEXT Input file path (.xlsx, .csv, or .txt) Required
--prefix TEXT Prefix for computed table names ct_
--delimiter TEXT Delimiter for CSV/TXT files , for CSV, \t for TXT
--as-draft FLAG Create checks in Draft status (default) True
--as-active FLAG Create checks in Active status False
--skip-checks FLAG Skip creating quality checks (only create computed tables) False
--skip-profile-wait FLAG Skip waiting for profile operation (Warning: checks may fail) False
--tags TEXT Tags for checks (comma-separated) None
--dry-run FLAG Preview what would be created without making changes False
--debug FLAG Enable debug mode with API logging False

Check Status: Draft vs Active

The --as-draft and --as-active flags control the status of created quality checks:

Status Flag Behavior
Draft --as-draft Check exists but won't run during scans. Review before activating.
Active --as-active Check runs immediately during scan operations.

Default: Checks are created as Draft for safety.

Use Cases

Below are common use cases showing what happens with different option combinations:

1. Basic Import (Default)

qualytics computed-tables import --datastore 123 --input checks.xlsx
What happens
Creates computed tables with ct_ prefix
Waits for profile operation to complete
Creates quality checks in Draft status
Skips existing computed tables
Skips check creation if check already exists

2. Import with Active Checks

qualytics computed-tables import --datastore 123 --input checks.xlsx --as-active
What happens
Same as basic import
Checks are created in Active status
Checks will run during the next scan operation

Use when: Rules are tested and ready for production.

3. Import Only Computed Tables (No Checks)

qualytics computed-tables import --datastore 123 --input checks.xlsx --skip-checks
What happens
Creates computed tables only
No quality checks are created
Still waits for profile operations

Use when: You want to configure checks manually in the UI.

4. Skip Profile Wait

qualytics computed-tables import --datastore 123 --input checks.xlsx --skip-profile-wait
What happens
Creates computed tables without waiting for profile
⚠️ Checks will likely FAIL - container has no fields until profile completes
Faster for bulk imports

Warning: If profiling hasn't completed, check creation will fail with: Container X has no fields. Cannot create check.

Use when: Only use with --skip-checks for bulk computed table creation. Add checks manually later after profiling completes.

5. Import with Custom Prefix

qualytics computed-tables import --datastore 123 --input checks.xlsx --prefix "dq_"
What happens
Computed tables use dq_ prefix instead of ct_
Example: CHK001 → dq_CHK001

Use when: Organizing different types of rules with different prefixes.

6. Import with Tags

qualytics computed-tables import --datastore 123 --input checks.xlsx --tags "production,finance"
What happens
Checks are created with specified tags
Multiple tags separated by commas

Use when: Organizing checks for filtering in the UI.

7. Dry Run (Preview Only)

qualytics computed-tables import --datastore 123 --input checks.xlsx --dry-run
What happens
No changes are made
Shows preview table of what would be created
Shows which tables would be skipped (already exist)

Use when: Validating input file before actual import.

8. Debug Mode

qualytics computed-tables import --datastore 123 --input checks.xlsx --debug
What happens
Shows API requests/responses in console
Writes detailed logs to ~/.qualytics/logs/

Use when: Troubleshooting import failures.

9. CSV with Custom Delimiter

qualytics computed-tables import --datastore 123 --input checks.txt --delimiter ";"
What happens
Reads file using semicolon as delimiter

Use when: Files exported from systems with non-standard delimiters.

10. Production-Ready Import

qualytics computed-tables import --datastore 123 --input checks.xlsx \
  --prefix "prod_" \
  --tags "production,automated" \
  --as-active \
  --debug
What happens
Computed tables with prod_ prefix
Checks in Active status
Tags: production, automated
Full API logging for audit trail

11. Fast Bulk Import (Minimal)

qualytics computed-tables import --datastore 123 --input checks.xlsx \
  --skip-checks \
  --skip-profile-wait
What happens
Fastest possible import
No profile waiting, no checks created
Computed tables only

Use when: Bulk setup of computed tables only. You must add checks manually after profiling completes.

Note: This is the recommended way to use --skip-profile-wait - always combine it with --skip-checks.

Computed Table Naming

The final computed table name follows the pattern: <prefix><name>

For example, with default prefix ct_:

  • Input name: CHK001 → Computed table: ct_CHK001
  • Input name: order_validation → Computed table: ct_order_validation

Common suffixes like _SF, _DB, _BQ, _SNOWFLAKE are automatically stripped from the rule_id stored in metadata:

  • Input name: CHK001_SF → rule_id in metadata: CHK001

Check Behavior

When checks are created (default behavior), a satisfiesExpression check is automatically generated where:

  • Empty result set (no rows) = PASS (all data is valid)
  • Any rows returned = FAIL (each row is flagged as an anomaly)

This is ideal for error detection queries where returned results indicate data quality issues.

The check expression wraps all field names with backticks for compatibility with special characters and functions:

`order_id` IS NULL AND `customer_name` IS NULL AND `coalesce(trim(status))` IS NULL

Metadata Storage

Both the computed table and quality check store metadata for traceability:

Computed Table additional_metadata:

{
  "description": "Detect orders with negative totals",
  "rule_id": "CHK001",
  "imported_from": "qualytics-cli",
  "import_timestamp": "2026-01-28T12:00:00"
}

Quality Check additional_metadata:

{
  "rule_id": "CHK001",
  "computed_table_name": "ct_CHK001",
  "original_description": "Detect orders with negative totals",
  "imported_from": "qualytics-cli",
  "import_timestamp": "2026-01-28T12:00:00"
}

SQL Query Handling

Cross-catalog/schema references are preserved as-is:

SELECT * FROM analytics_prod.sales_schema.orders o
JOIN finance_db.accounting.invoices i ON o.invoice_id = i.id
WHERE o.status = 'PENDING'

Automatic alias addition: Columns without aliases get unique aliases added automatically (expr_1, expr_2, etc.):

-- Original query (columns without aliases)
SELECT coalesce(trim(name), 'Blank'), upper(status), id as order_id FROM orders

-- After processing (aliases added)
SELECT coalesce(trim(name), 'Blank') as expr_1, upper(status) as expr_2, id as order_id FROM orders

This ensures all fields have proper names for the quality check expression.

If you need to test with all tables in the same catalog/schema, pre-process your input file to remove the prefixes before importing.

List Computed Tables

List all computed tables in a datastore.

qualytics computed-tables list --datastore DATASTORE_ID
Option Type Description Required
--datastore INTEGER Datastore ID to list computed tables from Yes

Example output:

┏━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ ID   ┃ Name               ┃
┡━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ 101  │ ct_CHK001          │
│ 102  │ ct_CHK002          │
│ 103  │ ct_CHK003          │
└──────┴────────────────────┘

Total: 3 computed tables

Preview File

Preview computed table definitions from a file without importing. Useful for validating your input file before import.

qualytics computed-tables preview --input FILE_PATH [OPTIONS]
Option Type Description Default
--input TEXT Input file path (.xlsx, .csv, or .txt) Required
--delimiter TEXT Delimiter for CSV/TXT files , for CSV, \t for TXT
--limit INTEGER Number of records to preview 5
--prefix TEXT Prefix to show for computed table names ct_

Example:

qualytics computed-tables preview --input checks.xlsx --limit 3

Example output:

Reading definitions from: checks.xlsx
Found 5 records in the file.

Preview of first 3 records:

Record 1:
  Computed Table Name: ct_CHK001
  Description: Detect orders with negative totals
  Query: SELECT * FROM sales_orders WHERE total_amount < 0

Record 2:
  Computed Table Name: ct_CHK002
  Description: Find customers without email
  Query: SELECT * FROM customer_master WHERE email IS NULL...

Record 3:
  Computed Table Name: ct_CHK003
  Description: Identify duplicate invoices
  Query: SELECT invoice_no, COUNT(*) FROM invoices GROUP B...

... and 2 more records

Development

This project uses modern Python tooling with uv for dependency management and ruff for linting and formatting.

Requirements

  • Python 3.9 or higher
  • uv (recommended) or pip

Setting Up Development Environment

  1. Install uv (if not already installed):

    curl -LsSf https://astral.sh/uv/install.sh | sh

  2. Clone the repository:

    git clone https://github.com/Qualytics/qualytics-cli.git
cd qualytics-cli

  3. Install dependencies:

    uv sync

  4. Install pre-commit hooks (optional but recommended):

    uv run pre-commit install

Development Commands

# Install/update dependencies
uv sync

# Run the CLI in development mode
uv run qualytics --help

# Run linting checks
uv run ruff check qualytics/

# Auto-fix linting issues
uv run ruff check qualytics/ --fix

# Format code
uv run ruff format qualytics/

# Run all pre-commit hooks (includes linting, formatting, and Python 3.9+ upgrades)
uv run pre-commit run --all-files

# Build the package
uv build

# Run tests (if available)
uv run pytest

# Bump version (patch/minor/major)
bump2version patch   # 0.1.19 -> 0.1.20
bump2version minor   # 0.1.19 -> 0.2.0
bump2version major   # 0.1.19 -> 1.0.0

Code Quality Standards

This project enforces:

  • Python 3.9+ minimum version
  • Ruff for linting and formatting (88 character line length)
  • pyupgrade for automatic Python syntax modernization
  • Pre-commit hooks for automated quality checks

Project Structure

qualytics-cli/
├── qualytics/           # Main package
│   ├── __init__.py
│   └── qualytics.py     # CLI implementation
├── pyproject.toml       # Project configuration & dependencies
└── .pre-commit-config.yaml  # Pre-commit hooks configuration

Contributing

  1. Create a new branch for your feature/fix
  2. Make your changes
  3. Run uv run ruff check qualytics/ and uv run ruff format qualytics/
  4. Run uv run pre-commit run --all-files to ensure all checks pass
  5. Commit your changes (pre-commit hooks will run automatically if installed)
  6. Submit a pull request

License

MIT License - see LICENSE file for details.

About

Qualytics CLI

Topics

cli platform qualytics

Resources

Readme

License

MIT license
Activity
Custom properties

Stars

1 star

Watchers

2 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Contributors 5

Languages