What differentiates data observability from software observability? Let’s delve into the differences and similarities.
For the past two decades, software observability tools have surged in demand, with notable examples like Datadog, New Relic Splunk assisting software developers in monitoring software applications and infrastructure by logs, metrics, and traces. These products aim to minimize downtime and optimize the performance of software. Subsequently, the tenets of observability extended beyond just software, leading to the rise of data observability as a trusted method for data reliability.
Despite their distinct purposes, both data observability and software observability encompass shared features. We’ll spotlight these commonalities and set the two concepts side-by-side for comparison.
Observability has evolved beyond just software, finding its niche in the domain of data management ensuring data trustworthiness and reliability.
Why Logs Matter in Observability Solutions Log monitoring sits at the heart of observability tools. With every system event, a corresponding log is generated, detailing:
Software observability tools use log data to identify issues affecting software application performance. On the other hand, while many data observability tools claim to focus on logs, they often use intensive SQL queries. These queries look into table data to check things like data volume and freshness rather than just the logs. In simple terms, despite their claims, many data tools don’t just look at logs but dive deeper, as they request access to read or edit clients’ data using methods that require the ability to run scheduled SQL queries against clients’ data.
Most data observability solutions employ SQL queries for data insight, which actually makes this solution a data quality product rather than a data observability solution (I probably will need to have a separate blog post about it later). Log-focused has been proved to be a strategy is the gold standard is software observability and will prevail in the long run among data observability, too. The advantages of this approach include:
Wrapping Up Log-focused data observability presents an array of benefits, encompassing heightened data quality and reliability without security compromises or escalated cloud expenses via SQL queries. With Masthead, receive real-time data quality notifications, ensuring flawless data for end-users and accurate decision-making.
Post Tags :
BigData, Data Culture, Data Engineering Practices, Data Quality