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.
Read about how BigQuery now allows you to use manifest files for querying open table formats.
The data landscape is constantly evolving, and with it come new challenges and opportunities for data teams. While generative AI and large language models (LLMs) seem to be all everyone is talking about, they are just the latest manifestation of a trend that has been evolving over the past several years: organizations tapping into petabyte-scale data volumes and running increasingly massive data pipelines to deliver ever more data analytics projects and AI/ML models.
