# 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:


<img src="../../assets/vhs/cli-getting-started.gif" class="img-fluid" alt="Getting Started" /> *CLI overview and your first data quality validation*


## Essential Data Quality Validations

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


<img src="../../assets/vhs/cli-essential-validations.gif" class="img-fluid" alt="Essential Validations" /> *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:


<img src="../../assets/vhs/cli-data-exploration.gif" class="img-fluid" alt="Data Exploration" /> *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:


<img src="../../assets/vhs/cli-using-polars.gif" class="img-fluid" alt="Using Polars" /> *Preview data, find missing values, and generate column summaries*


## CI/CD Integration & Automation

Learn how to integrate data quality checks into automated pipelines:


<img src="../../assets/vhs/cli-cicd-workflows.gif" class="img-fluid" alt="CI/CD Integration" /> *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:


<img src="../../assets/vhs/cli-complete-workflow.gif" class="img-fluid" alt="Complete Workflow" /> *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
```