Know-I-Have

Google Cloud Storage & AWS S3  Here is a quick recap of the main functionalities of both products: AWS S3 pricing: S3 costs $0.023/GB; For replicating data across multiple regions costs $0.046/GB, plus a $0.01/GB transfer fee. AWS’ cool storage service, S3 Infrequent Access (IA) $0.0125/GB Cloud storage pricing: No doubt Google pricing is cheaper than AWS is many aspects. Single-region storage costs $0.02/GB Multi-region cost is $0.026/GB, with free transfer of data Cool storage platform Nearline costs $0.01/GB Cold/archival product Coldline costs $0.007/GB Monthly per-GB Prices for the First TB Stored Amazon Web Services Microsoft Azure Google Cloud Storage HOT $0.023 $0.0208 $0.026 COOL $0.0125/$0.01* $0.0152 $0.01 COLD $0.004 $0.002 $0.007 * Amazon S3 Standard I/A / Amazon S3 Standard Z-I/A Volume Discounts for HOT Storage Class Amazon Web Services Microsoft Azure Google Cloud Storage 2–50 TB/month $0.0230 $0.0208 $0.026 50–500 TB/month $0.02

Google Cloud SQL & AWS RDS Here is a quick recap of the main functionalities of both products: Functionality AWS RDS Cloud SQL CPU & RAM  RDS supports three types of instance classes: Standard, Memory Optimized, and Burstable Performance.  Cloud SQL is divided into shared-core, standard and high memory machines.  Types of Storage General Purpose SSD, Provioned IOPS (SSD) and Magnetic Supports both SSD and HDD (magnetic) IOPS ratio (SSD) General Purpose (SSD) has the ability to burst to 3,000 IOPS for extended periods of time. Baseline performance for these volumes is determined by the volume's size. Baseline is 3 IOPS per GiB. SSD provides IOPS gurantee in a 30:1 ratio. The included 100-GB gurantees for 3000 IOPS. SSDs are preferred over HDD, as it provides lower latency and higher data throughput. Scaling options Allocated storage can be scaled or converted to another storage type (General SSD to PIOPS SSD). During the scaling proce

Google Cloud Functions & AWS Lambda First of all, I have to admit that comparing an alpha release with a two-year-old stable product is not completely fair. That said, I believe that some of the functionalities already offered by Google Cloud Functions will make a substantial positive difference, especially from a development point of view. Here is a quick recap of the main functionalities of both products: Functionality AWS Lambda Cloud Functions Scalability & availability Automatic scaling (transparent) Automatic scaling Max. # of functions Unlimited functions 1000 functions per project Concurrent executions 1000 parallel executions per account per region ( default safety throttle ) 400 parallel executions (per function, soft limit) Max. execution time 300 seconds (5 minutes) 540 seconds (9 minutes) Supported Languages  JavaScript, Java, C# and Python Node js, Go, Python Dependencies Deployment Packages npm package.json Deployments Only ZIP upload (to Lambda or

ECR-Token-automation aws ecr login token automation to kubernetes secrets This shell script will make kubermetes secrets which will use to pull docker images from aws ECR. Secret will contain login information of Secret will contain login information of AWS ECR For code : Click here ...! https://github.com/harsh4870/ECR-Token-automation ACCOUNT=123456789                                     #aws account number REGION=ap-south-1                                     #aws ECR region SECRET_NAME=${REGION}-ecr-registry                    #secret_name EMAIL=abc@xyz.com                                     #can be anything TOKEN=`aws ecr --region=$REGION get-authorization-token --output text --query authorizationData[].authorizationToken | base64 -d | cut -d: -f2` kubectl delete secret --ignore-not-found $SECRET_NAME kubectl create secret docker-registry $SECRET_NAME \  --docker-server=https://$ACCOUNT.dkr.ecr.ap-south-1.amazonaws.com \  --docker-u