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Leveraging Infrastructure as Code for Data Engineering Projects: A Comprehensive Guide

DZone

Data engineering projects often require the setup and management of complex infrastructures that support data processing, storage, and analysis. In this article, we will explore the benefits of leveraging IaC for data engineering projects and provide detailed implementation steps to get started.

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.

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How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

Central engineering teams enable this operational model by reducing the cognitive burden on innovation teams through solutions related to securing, scaling and strengthening (resilience) the infrastructure. All these micro-services are currently operated in AWS cloud infrastructure.

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Overcoming Challenges and Best Practices for Data Migration From On-Premise to Cloud

DZone

With the rapid adoption of cloud computing , businesses are moving their IT infrastructure to the cloud. This article discusses the challenges and best practices of data migration when transferring on-premise data to the cloud.

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Bringing Software Engineering Rigor to Data

DZone

The data community is striving to incorporate the core concepts of engineering rigor found in software communities but still has further to go. This is achieved through practices like Infrastructure as Code for deployments, automated testing, application observability, and end-to-end application lifecycle ownership.

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SIEM Volume Spike Alerts Using ML

DZone

SIEM platforms offer centralized management of security operations, making it easier for organizations to monitor, manage, and secure their IT infrastructure. Problem Statement In Data Engineering , the data/log collection is a challenging task for high-volume sources.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

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