Data Observability: A Brief Overview

The word "data observability" refers to the ability to comprehend the health and status of data in your system. Data observability, in essence, refers to a set of actions and technologies that, when combined, enable you to discover, debug, and fix data issues in near real-time. Observability is significantly more valuable for engineers since it encompasses a wide range of activities. Unlike the data quality frameworks and technologies that accompanied the data warehouse concept, it does not end at defining the problem. It offers enough background for the engineer to remedy the problem and initiate dialogues to avoid making the same mistake again. To do this, best practices from DevOps should be applied to Data Operations. Data observability is the logical continuation of the data quality movement, and it enables DataOps as a practice. And in order to adequately articulate what data observability implies, you must first understand where DataOps is now and where it is headed.

Criteria and Pillars for Data Observability Platforms

The effect of "junk data" may be easily framed via the lens of software application dependability. For the past decade or more, software developers have relied on specialized technologies such as New Relic and DataDog to assure high application uptime (that is, functional, performant software) while minimizing downtime (outages and sluggish software). This is referred to as Data Downtime in the data world. Data downtime refers to periods of time when data is incomplete, incorrect, missing, or otherwise wrong, and it only grows as data systems get more complicated, supporting an ever-expanding ecosystem of sources and consumers. These difficulties may be recognized, remedied, and even prevented by applying the same concepts of software application observability and dependability to data. This provides data teams confidence in their data's ability to produce useful insights. The five pillars of data observability are discussed in detail below. Each pillar has a set of questions that, when combined, give a comprehensive picture of data health. 

• Is the information current? When was the last time you saw it? What upstream data is included/omitted?
• Is the data distributed within acceptable ranges? Is it formatted correctly? Is it finished?
• Has all of the info arrived?
• What is the schema, and how has it evolved? Who made these modifications, and why were they done?
• Lineage: What are the upstream and downstream assets that are touched by a specific data asset? Who is creating this data, and who is depending on it to make decisions?
• A comprehensive and holistic approach to data observability necessitates constant and dependable monitoring of these five pillars via a data observability platform that acts as a primary source of truth about the health of your data.

How is Data Observability Different?

Application performance management (APM) solutions such as Datadog and New Relic have offered developers with visibility into infrastructure concerns. Prior to the advent of APM technologies, only administrators were in charge of dealing with performance issues.

The focus of data observability is on establishing a multidimensional perspective of data that includes performance, quality, and the influence on other components of the stack. The overarching purpose of data observability is to determine how effectively data meets business needs and objectives. 

One significant distinction between APM and data observability is that applications change significantly less than data. As a result, while it is easier to automate applications, data is considerably more volatile. Observability is the glue that holds the dynamic nature of data together and delivers real-time insights.

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