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Part 2: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1. Because games differ from series/films, its crucial to validate this estimation method for games.

Analytics 190
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Transform data into insights with Dynatrace Dashboards and Notebooks

Dynatrace

You can now: Kickstart your creation journey using ready-made dashboards Accelerate your data exploration with seamless integration between apps Start from scratch with the new Explore interface Search for known metrics from anywhere Let’s look at each of these paths through an end-to-end use case focused on Kubernetes monitoring.

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HTTP monitors on the latest Dynatrace platform extend insights into the health of your API endpoints and simplify test management

Dynatrace

Thanks to the power of Grail, those details are available for all executions stored for the entire retention period during which synthetic results are kept. It now fully supports not only Network Availability Monitors but also HTTP synthetic monitors. Details of requests sent during each monitor execution are also available.

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Reliability indicators that matter to your business: SLOs for all data types

Dynatrace

This lets you build your SLOs around the indicators that matter to you and your customers—critical metrics related to availability, failure rates, request response times, or select logs and business events. While the SLO management web UI and API are already available, the dashboard tile will be released within the next weeks.

Metrics 277
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Next generation Dynatrace Davis AI becomes the default causation engine

Dynatrace

Back during Perform 2019, we introduced the next generation of the Dynatrace AI causation engine , also known as Davis. becomes the default causation engine and will replace the previous version as the default for all new environments. as the default AI engine. AI causation engine. All existing Davis 1.0

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Observability engineering: Getting Prometheus metrics right for Kubernetes with Dynatrace and Kepler

Dynatrace

For busy site reliability engineers, ensuring system reliability, scalability, and overall health is an imperative that’s getting harder to achieve in ever-expanding, cloud-native, container-based environments. To get a more granular look into telemetry data, many analysts rely on custom metrics using Prometheus. What is Prometheus?

Metrics 245
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Analyze all AWS data in minutes with Amazon CloudWatch Metric Streams available in Dynatrace

Dynatrace

For quite some time already, Dynatrace has provided full observability into AWS services by ingesting CloudWatch metrics that are published by AWS services. Amazon CloudWatch gathers metric data from various services that run on AWS. Dynatrace ingests this data to perform root-cause analysis using the Dynatrace Davis® AI engine.

AWS 231