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1. Streamlining Membership Data Engineering at Netflix with Psyberg

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. What is late-arriving data? Let’s dive in!

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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

AI data analysis can help development teams release software faster and at higher quality. So how can organizations ensure data quality, reliability, and freshness for AI-driven answers and insights? And how can they take advantage of AI without incurring skyrocketing costs to store, manage, and query data?

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What is chaos engineering?

Dynatrace

But with the complexity that comes with digital transformation and cloud-native architecture, teams need a way to make sure applications can withstand the “chaos” of production. Chaos engineering answers this need so organizations can deliver robust, resilient cloud-native applications that can stand up under any conditions.

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The history of Grail: Why you need a data lakehouse

Dynatrace

Some time ago, at a restaurant near Boston, three Dynatrace colleagues dined and discussed the growing data challenge for enterprises. At its core, this challenge involves a rapid increase in the amount—and complexity—of data collected within a company. Work with different and independent data types. Grail architectural basics.

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Netflix Studio Engineering Overview

The Netflix TechBlog

Our mission in Studio Engineering is to build a unified, global, and digital studio that powers the effective production of amazing content. link] Why Does Studio Engineering Exist? In this overview, we will shed light on the complexity and magnitude of this journey and update this post with links to deeper technical blogs over time.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Rule Execution Engine is responsible for matching the collected logs against a set of predefined rules.

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IT teams seek observability for, and control over, serverless architecture

Dynatrace

Key takeaways from this article on modern observability for serverless architecture: As digital transformation accelerates, organizations need to innovate faster and continually deliver value to customers. Companies often turn to serverless architecture to accelerate modernization efforts while simplifying IT management.