Bigeye, a leader in data observability, and dbt Labs, the pioneer in analytics engineering, announced a formal partnership to bring integrated data observability to dbt Cloud customers.
The new partnership is a result of a robust integration model to support joint customers. The new integration brings helpful context from dbt Cloud directly into Bigeye to help analytics engineering teams simplify root cause analysis and speed issue resolution.
Amy Deora, dbt Labs VP of Partnerships, remarked, “Congrats to the Bigeye team for announcing a brand new integration with dbt Cloud! Our joint customers can now layer on Bigeye data observability to get a complete picture of the health of their dbt pipelines, find and fix issues faster, and give business leaders confidence in the integrity of their data.”
Building more reliable data pipelines with Bigeye and dbt Cloud
dbt is a powerful, SQL-based transformation and modeling tool that has become the standard for cloud data transformations. The dbt Cloud platform comes with a host of simple yet powerful features for building and testing data pipelines. With the combined power of Bigeye and dbt Cloud, joint customers can layer on data observability to track changes in data over time and find previously unidentified anomalies, often referred to as “unknown unknowns,” across their environment.
Also Read:
By applying the dbt testing framework and Bigeye data observability together, customers can get a complete picture of the health of their dbt pipelines and significantly reduce the burden on the data platform and analytics engineering teams.
“Our integration with dbt Cloud helps users resolve data issues before they impact their business and ultimately keep their data pipelines reliable,” said Kyle Kirwan, CEO and Co-founder at Bigeye. “Partnering with dbt Labs has allowed us to create a streamlined workflow so data platform and analytics engineering teams have everything they need to keep dbt pipelines running smoothly 24/7 every day of the year.”
Symptoms of data pipeline and modeling problems can vary widely and include data not arriving when expected, fluctuations in the volume of data delivered, unintentional schema changes, incorrect data values, and many others. Any one of these issues, if not detected and resolved quickly, can have real-world impacts on an organization’s operations and its customers. Bigeye uses machine learning-driven anomaly detection to help teams quickly identify and resolve an array of pipeline failures and data quality issues.
SOURCE: Prnewswire