DltHub
In the vast ocean of data management, the processes of extracting data from various sources and loading it into target systems are fundamental yet critical steps for businesses aiming to leverage data for insightful decision-making. The “Extract, Load, Transform” (ELT or ELT) approach has been the staple approach, emphasizing the importance of efficiently moving data before applying any transformations. This blog post delves into a new and exciting Python library called Data Load Tool. It streamlines the EL operations, thereby enhancing our data integration strategy. This is a perfect fit for the Data Build Tool (dbt) as it relies on other tools for EL process.
What is DLT?
Data Load Tool (DLT) is an open-source Python library that aims to simplify the creation and maintenance of data pipeline Extract and Load process. We can add it to Python scripts to load data from various sources into well-structured datasets.
Following are the key advantages of dlt:
- Python Based
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Leverages Python to build data pipelines. Run it where Python runs.
- Dependency Free
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No need to use any backends or containers. Simply import dlt in a Python file.
- Automatic Schema Management
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With schema inference, evolution, alerts, and with short declarative code, maintenance becomes simple.
- Declarative Syntax
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User-friendly, declarative interface that removes knowledge obstacles. Makes code maintenance easy.
- Compatible
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Integrate with existing tools and services ranging from Airflow, serverless functions, notebooks and Python scripts.