# CLI Interactive Demos

These CLI demos showcase practical data quality workflows that you can use!

> **Tip: 🎬 Workflow-Based Demonstrations**
>
> - **Essential validations** for everyday data quality checks
> - **Data exploration** tools that require no Python knowledge
> - **CI/CD integration** patterns for automated data quality
> - **Complete pipelines** from exploration to production validation

> **Note: Prerequisites**
>
> To follow along with these demonstrations:
>
> ``` bash
> pip install pointblank
> pb --help  # Verify installation
> ```

### Getting Started with the CLI

Learn the basics of Pointblank's CLI and run your first validation:

![Getting Started](../../assets/vhs/cli-getting-started.gif) *CLI overview and your first data quality validation*

### Essential Data Quality Validations

See the most commonly used validation checks that catch critical data issues:

![Essential Validations](../../assets/vhs/cli-essential-validations.gif) *Duplicate detection, null checks, and data extract debugging*

### Data Exploration Tools

Discover how to profile and explore data using CLI tools that are quick and easy to use:

![Data Exploration](../../assets/vhs/cli-data-exploration.gif) *Preview data, find missing values, and generate column summaries*

### Using Polars

You can use Polars in the CLI to load and transform data, and, pass the data to other CLI tools:

![Using Polars](../../assets/vhs/cli-using-polars.gif) *Preview data, find missing values, and generate column summaries*

### CI/CD Integration & Automation

Learn how to integrate data quality checks into automated pipelines:

![CI/CD Integration](../../assets/vhs/cli-cicd-workflows.gif) *Exit codes, pipeline integration, and automated quality gates*

### Complete Data Quality Workflow

Follow an end-to-end data quality pipeline combining exploration, validation, and profiling:

![Complete Workflow](../../assets/vhs/cli-complete-workflow.gif) *Full pipeline: explore → validate → automate*

## Getting Started

Ready to implement data quality workflows? Here's how to get started:

#### 1. Install and Verify

``` bash
pip install pointblank
pb --help
```

#### 2. Explore Various Data Sources

``` bash
# Try previewing a built-in dataset
pb preview small_table

# Access local files (even use patterns to combine multiple Parquet files)
pb preview sales_data.csv
pb scan "data/*.parquet"

# Inspect datasets in GitHub repositories (no need to download the data!)
pb preview "https://github.com/user/repo/blob/main/data.csv"
pb missing "https://raw.githubusercontent.com/user/repo/main/sales.parquet"

# Work with DB tables through connection strings
pb info "duckdb:///warehouse/analytics.ddb::customers"
```

#### 3. Run Essential Validations

``` bash
# Check for duplicate rows
pb validate small_table --check rows-distinct

# Validate data from multiple sources
pb validate "data/*.parquet" --check col-vals-not-null --column customer_id
pb validate "https://github.com/user/repo/blob/main/sales.csv" --check rows-distinct

# Extract failing data for debugging
pb validate small_table --check col-vals-gt --column a --value 5 --show-extract
```

#### 4. Integrate with CI/CD

``` bash
# Use exit codes for automation (0 = pass, 1 = fail)
pb validate small_table --check rows-distinct --exit-code
```