With 38% of data teams spending between 20% and 40% of their time fixing data pipelines¹, delivering reliable data to end users can be an expensive activity for data teams. With Kensu’s latest integration with Azure Data Factory, ADF users now benefit from the ability to observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and data quality metrics.

38% of data teams spend between 20% and 40% of their time fixing data pipelines¹. Combating these data failures is a costly and stressful activity for those looking to deliver reliable data to end users. Organizations using Azure Data Factory can now benefit from the integration with Kensu to expedite this process. Their data teams can now observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and performance metrics.

Kensu is the first solution to bring advanced data observability capabilities to support Matillion, empowering organizations to gain richer insights into their data pipelines and ultimately strengthening trust and data productivity. Matillion ETL is a popular tool for building and orchestrating data integration workflows. It simplifies extracting data from various sources, transforming it according to business requirements, and loading it into a cloud data platform.

At the beginning of my career as a data analyst, I had to rely on other team members when something went wrong in our data pipeline, often only finding out about it after the event. That experience was one of the driving factors for me to join Kensu. When I spoke with the team for the first time, I had that “lightbulb moment”: data observability is a way of providing help to various data team members, including data analysts, in making their lives more productive and less painful.

It’s a common thread amongst data-driven organizations: data teams face soaring volumes of data with varying complexities, which raise issues regarding data reliability. Efficiently monitoring data pipelines has become paramount to swiftly identifying and addressing potential data incidents, ensuring minimal impact on data practitioners and end users.