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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

” Because of their versatility, teams can use IDPs for all types of software engineering projects, not just those in cloud-native scenarios. Furthermore, OneAgent observes and gathers all remaining workload logs, metrics, traces, and events. It lets us see events such as starts and traces in a standardized manner.”

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Designing Instagram

High Scalability

Architecture. FUN FACT : In this talk , Dikang Gu, a software engineer at Instagram core infra team has mentioned about how they use Cassandra to serve critical usecases, high scalability requirements, and some pain points. Sending and receiving messages from other users. High Level Design. Streaming Data Model.

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Site reliability engineering: 5 things you need to know

Dynatrace

What is site reliability engineering? Site reliability engineering (SRE) is the practice of applying software engineering principles to operations and infrastructure processes to help organizations create highly reliable and scalable software systems. Dynatrace news. Solving for SR.

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A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

This “Enterprise Data Model/Architect Agent” employs generative AI techniques for autonomous enterprise data modeling and architecture. Tulika Bhatt, Senior Data Engineer at Netflix, shared how her team manages impression data at scale. Until next time!

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Conducting log analysis with an observability platform and full data context

Dynatrace

Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams. Logs are automatically produced and time-stamped documentation of events relevant to cloud architectures.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. This significantly increases event latency.

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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

The original assumptions and architectural choices were no longer viable. Overview The figure below depicts a simplified high-level architecture of a single Titus cluster (a.k.a Let’s assume a sequence of events E?…E??, The solution is to flush all the events created up to time T? it will read version E? of the data.

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