Data observability refers to the ability to collect, measure, and analyse data from various sources in order to understand the current state and behaviour of a system.
This includes monitoring the system's performance, availability, and errors, as well as identifying patterns and anomalies in the data.
By implementing data observability, organisations can gain insights into their systems and make data-driven decisions to improve performance, optimize resources, and reduce costs. Common tools used for data observability include logging, metrics, tracing, and alerting.
In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Lior Gavish, Co-Founder and CTO at Monte Carlo, to discuss:
- Getting started with data observability
- Observability trends for 2023
- How to implement and common challenges
Data observability is your company’s ability to fully understand the health of the data in its systems. Healthy—high-quality, reliable, and trusted—data starts with the ability to monitor and understand the five pillars of data observability at each stage of the pipeline: Freshness, Distribution, Volume, Schema, and Lineage.
Monte Carlo's data observability platform connects to your existing data stack quickly and seamlessly to monitor for freshness, distribution, volume, and schema changes continuously, and immediately notifies stakeholders once data incidents are detected.
With end-to-end visibility, our data observability platform generates insights that help data teams change and modify tables and schema responsibly, manage storage and compute costs, and make smarter data architecture decisions.
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