Data engineering teams face significant challenges when it comes to optimizing their data stacks. Modern organizations rely on a growing number of tools and technologies for everything from reporting to data science, which means more integrations to build, monitor, and maintain. Demand for even more (and, in many cases, rawer) data introduces numerous data quality and reliability issues. And, cost is always a factor—especially as the cloud continues to play a larger role.
Seeking to overcome these challenges and optimize for data success, organizations across all stages of the data journey are turning to data observability. Acceldata’s Head of Product, Tristan Spaulding, recently appeared on The Data Engineering Podcast and shared tips for taking a “multidimensional” approach. This blog post recaps a few of his thoughts and recommendations for data engineering teams.
What is Data Observability?
At a basic level, data observability should be focused on gaining visibility into your data and data systems. Although Application Performance Monitoring systems (APMs) can provide insights into how parts of your technology stack are performing, they fall short in other areas, such as ensuring data quality and monitoring data pipelines. Unreliable data and broken data pipelines can quickly erode trust in your data, thereby limiting the impact of the organization’s data stack.
“Most people in the data world do not feel confident about their data,” said Tristan Spaulding, Head of Product at Acceldata. “With data observability, there’s this race to figure out how to provide that foundation where we can actually trust our data assets.”
Monitoring vs. Enterprise Data Observability
Simply “monitoring” certain pieces of the data stack doesn’t go far enough. That’s why taking a multidimensional approach to enterprise data observability is key for gaining a comprehensive understanding of the organization’s data, processing, and pipelines.
“Enterprise data observability involves going very deep into the data and into the systems powering the data,” Spaulding said. “It’s one thing to look at the data, and it’s another thing to actually dig in and try to resolve the problem.”
In short, enterprise data observability is about helping organizations that build and operate data products to proactively identify potential issues in their data stacks, determining how those issues are connected, and getting to the root cause in less time.
Acceldata’s Approach to Enterprise Multidimensional Data Observability
Acceldata’s data observability cloud provides an end-to-end solution that helps organizations continuously optimize their data stacks. As Spaulding pointed out during the podcast, “We’re going to help you understand up, down, left, and right how things relate to each other.” Acceldata makes this a reality by providing customers with a single pane of glass into:
- Data pipelines: Stay informed about potential data pipeline issues. Monitor performance across multiple systems and data environments.
- Data reliability: Leverage a variety of data reliability features, including automated data quality monitoring, anomaly detection, and a built-in data catalog.
- Performance: Predict potential performance issues and receive notifications of incidents. Monitor data processing health across your cloud environments.
- Spend: Visualize your spend, detect waste, and easily identify anomalies that require additional investigation.
Take an interactive tour of Acceldata to see how our multidimensional approach to data observability can make life easier for your data engineers, data executives, and other data professionals.
Get Started with Enterprise Data Observability
Ready to get started with enterprise data observability? Request a free trial of Acceldata’s data observability cloud and see if it’s right for your organization.