- Contentdatadoghq.com
Key metrics for monitoring Databricks
Datadog released a blog post outlining key metrics for monitoring Databricks data engineering, analytics, and Model Serving workloads. The guide aims to help users optimize health, performance, and co…
- Integrationdatadoghq.com
Monitor Databricks with Datadog
Datadog introduced a new integration enabling users to monitor performance, data quality, and costs of Databricks analytics and AI/ML workloads directly within Datadog. This expands Datadog’s observab…
- Contentdatadoghq.com
The first 72 hours of a ransomware attack: Why restored isn’t recovered
Datadog’s blog post highlights that server restoration alone does not equate to full business recovery during ransomware attacks. It emphasizes the need for ransomware recovery planning and leveraging…
- Featuredatadoghq.com
How we extended Apache DataFusion to execute one query across many machines
Datadog introduced Distributed DataFusion, an extension enabling Apache DataFusion queries to run across multiple machines. This addresses scalability challenges for interactive queries in large-scale…
- Featuredatadoghq.com
Monitor warehouse data quality beyond pipeline health
Datadog introduced a new capability to monitor data quality at rest in warehouses, identifying issues that persist even when pipelines succeed. The feature enables tracing problems to their source, ad…
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