What is Reinforcement Learning (RL)? Definition, Algorithms, Examples
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
Join 34,209 IT professionals who already have a head start
Meet the panel
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