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API Design Principles for Optimal Performance and Scalability

DZone

The goal is to help developers, technical managers, and business owners understand the importance of API performance optimization and how they can improve the speed, scalability, and reliability of their APIs. API performance optimization is the process of improving the speed, scalability, and reliability of APIs.

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Designing Instagram

High Scalability

Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. High Level Design. Component Design. There are two major processes which gets executed when a user posts a photo on Instagram. API Design. Problem Statement. Architecture.

Design 334
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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

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What Should You Know About Graph Database’s Scalability?

DZone

Having a distributed and scalable graph database system is highly sought after in many enterprise scenarios. Do Not Be Misled Designing and implementing a scalable graph database system has never been a trivial task.

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Introducing the New Dapr Jobs API and Scheduler Service

DZone

The Scheduler service enables this and is designed to address the performance and scalability improvements on Actor reminders and the Workflow API. In this post, I am going to deep dive into the details of how the Scheduler service was designed and its implementation to give you some background. Prior to v1.14 Prior to v1.14

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Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and business data at massive scale for analytics and automation in context

Dynatrace

Organizations choose data-driven approaches to maximize the value of their data, achieve better business outcomes, and realize cost savings by improving their products, services, and processes. Data is then dynamically routed into pipelines for further processing. Understanding the context.

Analytics 203