Study Note dlt Fundamentals Course - Lesson 1 Quick Start

What is dlt?

• An open-source Python library designed to simplify and streamline the ETL (Extract, Transform, Load) process.
• Infers schemas and data types, normalizes data, and handles nested data structures.
• Normalizing Data:

In the context of dlt, "normalizing the data" refers to the process of organizing and structuring the data in a consistent and standardized way, making it easier to work with and analyze.

Data normalization is a technique used to:

1. Eliminate redundant data: Remove duplicate or unnecessary information, reducing data size and improving data quality.
2. Improve data consistency: Standardize data formats, formats, and values, making it easier to compare and combine data from different sources.
3. Enhance data integrity: Ensure data accuracy and reliability by detecting and correcting errors, inconsistencies, and invalid values.
4. Simplify data analysis: Make it easier to perform data analysis, reporting, and visualization by providing a consistent and organized data structure.

In the context of dlt, normalizing data involves:

1. Flattening nested data structures: Converting complex, hierarchical data into a flat, tabular format, making it easier to work with.
2. Standardizing data types: Converting data types to a consistent format, such as converting strings to dates or integers to floats.
3. Removing duplicates: Eliminating duplicate records or data points, improving data quality and reducing data size.

By normalizing data, dlt makes it easier to work with and analyze data, allowing users to focus on leveraging the data and driving value, while ensuring effective governance through timely notifications of any changes.

Key Features of dlt

• Lightweight interface: Easy to use, flexible, and scalable.
• Supports various sources and destinations: Load data from a wide range of sources, including REST APIs, SQL databases, cloud storage, and Python data structures.
• Reverse ETL pipelines: Allows for the addition of custom destinations.
• Schema evolution: Automates pipeline maintenance, saving valuable time and resources.
• Data contracts: Ensures effective governance through timely notifications of any changes.

Installing dlt

• Recommended to work within a virtual environment when creating Python projects.
• Install dlt with DuckDB as destination: install dlt with DuckDB as destination

dlt Pipeline

• A connection that moves data from Python code to a destination.
• Accepts dlt sources or resources, generators, async generators, lists, and any iterables.
• Instantiate a pipeline by calling dlt.pipeline().
• Run method: run() method is used to load data.

Commonly Used Arguments

• data: Pass your data to the run() method.
• sql_client(): Access the SQL client of your destination via the sql_client method on your pipeline.