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Data Observability

 

Our modern data stack has many disparate sources and complex interwoven data pipelines leading up to the reporting layer. When we encounter a data error on a Report or a Dashboard, it becomes time consuming to find the root cause of errors and resolve them. 

Data Observability tries to shine a light on the black box inside pipelines to catch more issues before they reach the Dashboards and make it exponentially faster to resolve issues. In today’s data stack, systems for ensuring data quality are embedded deep inside code or within complex data pipelines where they are inscrutable for data consumers and intractable for data administrators. Data Observability is an evolution of old-world data quality tooling where the quality measures are surfaced on user-friendly interfaces and integrated into incident-management processes. The goal is to make it easy to determine what went wrong, how to resolve it quickly, and how to prevent it in the future. 

That goal is what drives ExpertSense - our data observability solution for Databricks and Snowflake. It allows your team to easily create new rules, deploy them independent of your code, receive alerts when the rules fail, and have relevant information at their fingertips to quickly resolve the issues before business dashboards and reports are impacted. In addition to the tool itself, we provide consulting services to help you evaluate your current state and institute best practices to extract maximum value from the quality process.

 

 

 

 

 

 

 

For more information on how Data Observability can help your organization, please Contact us.

Data Quality Status Assessment

 

Evaluate your current state, identify stakeholders, plan your implementation

Map your needs to a right-sized toolkit 

Data Architecture Assessment

 

Identify elements of your as-is data architecture that are outdated or need improvement, design a to-be architecture that is modern and scalable

Data Observability Implementation

 

Integrate the tool into your existing pipelines and Devops teams processes. Define new processes integrating business superusers into a unified quality team

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