Elementary

Elementary is an innovative open-source solution addressing a critical need in data engineering: ensuring data reliability. With the growing adoption of dbt in recent years, a gap has emerged in data monitoring and quality management. Elementary fills this void by providing a system for tracking and improving data quality, helping eliminate the risks of inconsistent or incomplete data.

Before Elementary, data quality management relied on custom scripts or dedicated tools like Great Expectations, which were often complex and costly. Elementary simplifies this process through its native integration with dbt, making it easier to implement quality checks within ETL (Extract, Transform, Load) workflows. While dbt lacks a built-in interface for tracking data trends over time, Elementary stands out with its history tracking and visualization layer, offering an intuitive dashboard that is easy to install and deploy. A single scheduled command can generate automated reports, making the process transparent and accessible to data analysts, analytics engineers, and business teams.

Additionally, Elementary includes advanced features such as anomaly detection, adding another layer to data monitoring. However, these features may require adjustments depending on specific use cases.

Theodo’s point of view

Theodo recommends Elementary for its ease of integration, especially for companies already using dbt and looking to strengthen data quality. Its active community ensures quick access to support and continuously evolving features. However, as an emerging technology, it is evolving rapidly, which may present an adaptation challenge for some organizations.