{"id":30303,"date":"2022-09-13T06:00:00","date_gmt":"2022-09-13T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=30303"},"modified":"2022-09-14T09:08:07","modified_gmt":"2022-09-14T16:08:07","slug":"how-to-optimize-the-modern-data-stack-with-enterprise-data-observability","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/09\/13\/how-to-optimize-the-modern-data-stack-with-enterprise-data-observability\/","title":{"rendered":"How to Optimize the Modern Data Stack with Enterprise Data Observability"},"content":{"rendered":"\n

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\u2014especially as the cloud continues to play a larger role.<\/p>\n\n\n\n

Seeking to overcome these challenges and optimize for data success, organizations across all stages of the data journey are turning to data observability where they can get a continuous, comprehensive, and multidimensional view into all enterprise data activity. It\u2019s a critical aspect of optimizing the modern data stack, as we\u2019ll see. <\/p>\n\n\n\n

What is Data Observability?<\/strong><\/p>\n\n\n\n

At a basic level, data observability<\/a> 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\u2019s data stack.<\/p>\n\n\n\n

\u201cMost people in the data world do not feel confident about their data,\u201d said Tristan Spaulding, Head of Product at Acceldata. \u201cWith data observability, there\u2019s this race to figure out how to provide that foundation where we can actually trust our data assets.\u201d<\/p><\/blockquote>\n\n\n\n

Monitoring vs. Enterprise Data Observability<\/strong><\/p>\n\n\n\n

Simply \u201cmonitoring\u201d certain pieces of the data stack doesn\u2019t go far enough. That\u2019s why taking a multidimensional approach<\/a> to enterprise data observability is key for gaining a comprehensive understanding of the organization\u2019s data, processing, and pipelines. <\/p>\n\n\n\n

\u201cEnterprise data observability involves going very deep into the data and into the systems powering the data,\u201d Spaulding said. \u201cIt\u2019s one thing to look at the data, and it\u2019s another thing to actually dig in and try to resolve the problem.\u201d<\/p><\/blockquote>\n\n\n\n

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.<\/p>\n\n\n\n

Acceldata\u2019s Approach to Enterprise Multidimensional Data Observability<\/strong><\/p>\n\n\n\n

Acceldata\u2019s data observability cloud<\/a> provides an end-to-end solution that helps organizations continuously optimize their data stacks. As Spaulding pointed out during the podcast, \u201cWe\u2019re going to help you understand up, down, left, and right how things relate to each other.\u201d Acceldata makes this a reality by providing customers with a single pane of glass into:<\/p>\n\n\n\n