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#cloud #aws #azure #gcp #oci I always like this type of comparison between different platforms or Technologies.

Useful reference for navigating cloud service options. Valuable insights!

🚀 Cloud and SQL Go Hand-in-Hand: - With the rise of cloud-based databases like AWS RDS, Azure SQL, and Google Cloud SQL, SQL remains a critical skill. - Cloud platforms simplify database management, but understanding SQL is still necessary to leverage their full potential. #CloudComputing #SQL #FutureOfDevelopment"

Hello, LinkedIn Fam, I just completed "Cloud Computing Essentials for Azure and AWS"! I'm thrilled to share that I've completed the "Cloud Computing Essentials for Azure and AWS" course on Analyst Builder, taught by the fantastic Alex Freberg (Alex the Analyst). This journey has been eye-opening and incredibly valuable. In Azure, I discovered: How data moves and is organized Different types of data storage (databases, warehouses, and lakes) Essential Azure tools for data analytics like Storage Accounts, SQL Databases, Data Factory, Synapse Analytics, and Databricks In AWS, I explored: Key AWS tools for data analytics Storing data with S3 Setting up and using databases with RDS Analyzing data using Athena, Glue DataBrew, Redshift, and Quicksight. Managing user access with IAM Why does this matter? Cloud computing is a game-changer for data analysts. It makes managing and analyzing big data more efficient, allowing us to derive deeper insights and create more impactful solutions. I can't wait to apply this knowledge in my future roles and make a real difference! A huge thanks to Alex Freberg for the incredible course and insights. Check out my certificate below. 🚀 #CloudComputing #Azure #AWS #DataAnalytics #Learning #AlexTheAnalyst

📍Comparative studies are invaluable for architecture designers aiming to create consistent architectural designs across various cloud service providers. 📍 This approach fosters a common design language and enhances efficiency in multi-platform development to all my cloud ☁️ architecture fellows. #AWS #cloudarchitect #azure #googlecloud #oraclecloud

AWS is the world's most comprehensive and widely used cloud platform. * It provides a vast array of on-demand cloud computing services like storage, databases, networking, analytics, machine learning, and more. * You pay only for the resources you use, making it a flexible and cost-effective solution. Key AWS Services * Compute: * EC2 (Elastic Compute Cloud): Provides virtual servers (instances) you can use to run applications. * Lambda: Enables you to run code without managing servers. * Storage: * S3 (Simple Storage Service): Object storage for storing and retrieving any amount of data. * EBS (Elastic Block Store): Block-level storage volumes for use with EC2 instances. * Database: * RDS (Relational Database Service): Manages and scales relational databases. * DynamoDB: A fully managed NoSQL database. * Networking: * VPC (Virtual Private Cloud): Your own isolated section of the AWS cloud. * Route 53: A highly available Domain Name System (DNS) web service. * Analytics: * Redshift: A fully managed data warehouse. * EMR (Elastic MapReduce): A managed big data platform. * Machine Learning: * SageMaker: A fully managed platform for building, training, and deploying machine learning models. Why Use AWS? * Scalability: Easily scale your applications up or down to meet demand. * Reliability: AWS operates a massive global infrastructure with built-in redundancy. * Cost-Effectiveness: Pay only for what you use, with various pricing options. * Security: Robust security features to protect your data. * Global Reach: Deploy applications in multiple regions worldwide. * Broad and Deep Services: Access a wide range of services to build complex applications. For more information or any requirement, please email : haider@zenitech.in #cloudcomputing #aws

💻 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻𝘀: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱, 𝗮𝗻𝗱 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗦𝗔𝗩𝗘 𝗧𝗛𝗜𝗦☁️ #cloudcomputing For anyone working with cloud services, here's a direct comparison to help you understand the equivalents across four major platforms: 🟠 Virtual Machines: EC2 (AWS), Virtual Machines (Azure), Compute Engine (Google Cloud), Virtual Machine (Oracle Cloud) 🟠 Storage: S3 (AWS), Blob Storage (Azure), Cloud Storage (Google Cloud), Object Storage (Oracle Cloud) 🟠 Databases: RDS (AWS), SQL Database (Azure), Cloud SQL (Google Cloud), ATP (Oracle Cloud) 🟠 Machine Learning: SageMaker (AWS), Machine Learning (Azure), Vertex AI (Google Cloud), Data Science (Oracle Cloud) #cloudengineer #cloudcomputing Amazon Web Services (AWS) Microsoft Azure Google Cloud **#Cloud, #CloudComputing, #CloudSecurity, #AWS, #Azure, #GoogleCloud, #CloudStorage, #CloudMigration, #SaaS, #IaaS, #PaaS 💻Share for others💻 Follow for More - Krishan Bhatt ✅

☁️ 𝘾𝙤𝙢𝙥𝙡𝙚𝙩𝙚𝙙 𝙙𝙖𝙮 14 𝙤𝙣 30 𝙙𝙖𝙮𝙨 𝙘𝙡𝙤𝙪𝙙 𝙘𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚! Today's task was to launch an 𝐀𝐦𝐚𝐳𝐨𝐧 𝐓𝐢𝐦𝐞𝐬𝐭𝐫𝐞𝐚𝐦 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐚𝐧𝐝 𝐪𝐮𝐞𝐫𝐲 𝐝𝐚𝐭𝐚 from it. Here's a quick breakdown of the steps I followed: 1.Create an Amazon Timestream Database: Set up a new database via the AWS Management Console. 2.Define Tables: Created tables to organize data for storing time-series information. 3.Insert Data: Loaded sample time-series data into the tables using the Timestream console or AWS SDKs. 4.Run Queries: Used Amazon Timestream Query Editor to run SQL-like queries to analyze the data. 5.Visualize Results: Reviewed query results and visualized the output for insights. A big thank you to Chris Glary . C and Arockia Shermila S mentors for your invaluable guidance and support under PEP Cloud Computing (AWS/ Azure/ GCP) and DevOps Centre throughout this challenge. Your mentorship have been crucial in helping me navigate through this learning experience! Feeling great to have worked with AWS Timestream—great learning experience for managing time-series data at scale! 🚀 #AWS #Cloud #Timestream #DataAnalytics