As organizations seek to drive more value from their data, observability plays a vital role in ensuring the performance, security and reliability of applications and pipelines while helping to reduce costs. At Snowflake, we aim to provide developers and engineers with the best possible observability experience to monitor and manage their Snowflake environment. One of our partners in this area is Observe, which offers a SaaS observability product that is built and operated on the Data Cloud.

When it comes to supporting the latest tech, here at Moesif, we always aim to integrate with partners that offer something new and exciting. In the API gateway space, Zuplo has been quickly changing the game regarding platforms focused on developers and performance. Similar to the work we’ve focused on for the last few years, Zuplo customers have also been focused on understanding their APIs better and learning how to monetize them efficiently.

Struggling to keep up with your organization’s hunger for data? That’s an obstacle that many data teams face when their data stack grows and they don’t have complete control over their complex data pipelines. If that’s a challenge you’re looking to solve, you’re in the right place. Below, we’ve curated our list of the best data observability tools every data team should know about.

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.

As data becomes an increasingly important asset for organizations, ensuring its quality and reliability is critical. Data observability is a practice that helps organizations maintain visibility into their data pipelines and quickly identify and resolve any issues that arise.

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.