Data observability in the cloud: 3 things to look for in comprehensive data platform governance

Data observability in the cloud: 3 things to look for in comprehensive data platform governance

Thanks to AI, the next generation of data observability tools will go beyond identifying problems to explaining how to solve them. To do so, your data platform will need three key features:

• Written by Eric Chu
• February 20, 2024

Today, the speed of innovation is accelerating at an astronomical pace, and companies, regardless of market sector, are running the gamut of more data, more AI and LLM, more users, more projects, and, spookily. We are striving to do more with more opportunities in the pipeline. break or stall. All of this adds up to potentially prohibitive costs and slow time-to-value for cloud data platforms.

There’s often a black hole in understanding where cloud data spending is going, and cloud data vendors aren’t particularly willing to explain these costs. To make matters worse, data teams are tasked with building, scaling, and monitoring more applications faster. However, these projects need to find opportunities to optimize performance even as teams struggle to figure out where the pipeline is breaking and why it’s breaking (because it breaks). So you always end up in a “hurry up and wait” situation. It is these performance issues that inevitably slow down time to market and value.

As the saying goes, you can’t manage what you can’t see. Historically, data observability tools have focused on this visibility, providing insight into where problems are occurring. This feature is now serving as the starting point for the next wave of data observability tools. By incorporating AI, these next-generation tools create a comprehensive data platform governance solution that not only tells you where the problem exists, but also tells you what to do about it and how to prevent it from happening again. will now be provided.

Look for three key features of the data platform:

automation

Data observability solutions should incorporate AI/ML to detect anomalies and issues with cost, performance, and data. These tools should include:

• Predictive analytics. Predict and avoid data issues before they occur, ensuring your pipelines run as cost-effectively as possible without sacrificing reliability.
• Guardrails are predictive analytics enforcement mechanisms that use AI and automation to help developers optimize their code for cost and performance efficiency.
• Automated self-healing (or human-involved systems) triggers alerts and automated corrective actions in real time, reducing the time (and need) for manual intervention.

FinOps goes beyond data observability

To truly meaningfully address issues related to data and AI pipelines, data observability tools must extend to FinOps.Knowing is no longer enough where If a pipeline goes down or breaks, data teams need to understand the cost of the pipeline.

In the cloud, performance inefficiencies increase computing costs and therefore total costs. Tools should incorporate FinOps to enable you to view costs related to both infrastructure and computing resources, broken down by job, user, and project. It should also include advanced analytics to provide guidance on how to make individual pipelines more cost-effective. This allows data teams to focus on strategic decision-making rather than spending time reconfiguring pipelines based on cost considerations.

Dedicated platform specific

Each cloud data platform has its own pricing and performance nuances, so data observability tools with FinOps and AI-driven insights must be compatible with each specific data platform. .

To meet these demands, data observability solution vendors provide detailed cost visibility, efficient management of storage costs, chargebacks/showbacks, expensive projects, queries, and user lies. Additionally, it should provide insights to help alleviate costly jobs with little or no ROI and prevent poorly written code (costly and fragile code) from being introduced into production.