Introduction

The VTL Engine is an open-source Python library designed to simplify and streamline the processing of statistical data, particularly within the SDMX framework. It provides a powerful and flexible environment for data manipulation, validation, and transformation, making complex data tasks more manageable for statistical organizations.

Primary Use Cases

1. Statistical Data Validation

• Implement complex validation rules for statistical datasets
• Ensure data quality and consistency across multiple statistical domains
• Validate structural and semantic constraints in SDMX datasets

2. Data Transformation

• Transform data between different statistical concepts and classifications
• Perform statistical calculations and aggregations
• Convert between different data formats and structures

3. Statistical Production Pipelines

• Integrate with existing statistical production systems
• Automate data processing workflows
• Standardize transformation processes across different statistical domains

About VTL

At its core, the VTL Engine implements the Validation and Transformation Language (VTL), a language specifically designed for the manipulation and transformation of statistical data. VTL offers a rich set of functions and operators for data cleaning, aggregation, filtering, and more. The VTL Engine brings the power of this language to the Python ecosystem, enabling users to define and execute sophisticated data transformations with ease. This is particularly valuable for tasks such as data validation and the creation of derived statistics within national statistical offices.

Integration with SDMX

The VTL Engine seamlessly integrates with SDMX data and metadata, leveraging the standardized structure of SDMX for efficient and reliable data workflows. This integration allows users to apply VTL transformations directly to SDMX datasets, simplifying tasks such as data validation, the calculation of aggregates, and preparation for further analysis or reporting.

Furthermore, the VTL Engine works beautifully with pysdmx, a popular Python library for working with SDMX. Together, these tools create a powerful and flexible foundation for building robust SDMX data pipelines, enabling statisticians at national statistical offices to streamline their data processing workflows.

For more information, refer to the github repo.

Installation

VTL Engine is published on PyPi and can be installed using the following command:

pip install vtlengine