Tag: auto scaling
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DynamoDB vs. MongoDB
DynamoDB vs. MongoDB: Advantages of DynamoDB (Detailed) DynamoDB vs. MongoDB: A Detailed Comparison of Advantages for DynamoDB Both Amazon DynamoDB and MongoDB are prominent NoSQL databases known for their scalability and flexibility. However, their underlying architectures and feature sets lead to distinct advantages for DynamoDB in specific use cases. 1. Fully Managed and Serverless Architecture… Read more
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DynamoDB vs. Bigtable: Cost Optimization
DynamoDB vs. Bigtable: Cost Optimization When choosing a NoSQL database like Amazon DynamoDB or Google Cloud Bigtable, cost optimization is a crucial consideration. Both databases offer different pricing models and strategies for managing expenses. This article explores how to optimize costs with DynamoDB and Bigtable. Amazon DynamoDB Cost Optimization DynamoDB offers two capacity modes: Provisioned… Read more
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AWS EMR with Flink
Comprehensive Details: Fusion of EMR with Flink Together Comprehensive Details: Fusion of EMR with Flink Together The synergy between Amazon EMR (Elastic MapReduce) and Apache Flink represents a powerful paradigm for processing large-scale data, particularly streaming data, within the cloud. This “fusion” involves leveraging EMR’s managed infrastructure and ecosystem to deploy, run, and manage Flink… Read more
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Top 50 Design Patterns for Enterprise-Scale Applications
Top 50 Design Patterns for Enterprise-Scale Applications Building robust, scalable, and maintainable enterprise-scale applications requires careful architectural considerations and the strategic application of design patterns. Here are 30 important design patterns categorized for better understanding, along with details and relevant links: 1. Microservices Details: An architectural style that structures an application as a collection of… Read more
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Databricks High level Concepts
Databricks High-Level Concepts: A Detailed Overview Databricks High-Level Concepts: A Detailed Overview Databricks is a unified analytics platform built on top of Apache Spark, designed to simplify big data processing and machine learning. It provides a collaborative environment for data scientists, data engineers, and business analysts. Here’s a detailed overview of its key high-level concepts:… Read more