jpbarto

#!/usr/bin/env python3
"""
The following script demonstrates how to use the AWS Boto3 SDK to iterate through 
all of the users in an AWS Cognito User Pool and examine the events associated
with each user.  

If any failed authentication events are found the script formats them as messages
and logs them to CloudWatch logs.

This script could easily be modified to run periodically as a Lambda function 

jpbarto

#!/usr/bin/bash

# This script is built to compile Qt5 on an Amazon Linux2-based Amazon Workspace
# This script is known to fail on a Standard Workspace with 2 vCPU and 4 GB of RAM (Out of Memory error suspected)
# Script has been tested on a Power Workspace with 4 vCPU and 32 GB of RAM
# based upon documentation at
# https://doc.qt.io/qt-5/linux-requirements.html

set -e

jpbarto

import os
import boto3

lclient = boto3.client ('lambda')
print ('function loaded')

def lambda_handler(event, context):
    lclient.delete_function (FunctionName = context.function_name)
    print ('I am deleted')
    return True

jpbarto

/** The following is a NodeJS port of the AWS SigV4 signing sample code (Python) to NodeJS
 *  The addition of the Authorization header has been informed by the use of Postman
 *  For more information see the following documentation:
 *  http://docs.aws.amazon.com/general/latest/gr/sigv4-signed-request-examples.html
 */

var https = require('https');
var crypto = require('crypto');

function sign(key, message) {

jpbarto

Machine Learning 101 on SageMaker Immersion Day

Lab 1: Feature Engineering

Step 1. Get into Event Engine

Following the steps online access your AWS account through AWS Event Engine:

https://sagemaker-immersionday.workshop.aws/en/prerequisites/option1.html

jpbarto

#!/usr/bin/env bash

###############################################################################################
#
# The following shell script uses the AWS CLI to forcefully detach a non-root volume from a 
# running EC2 instance.  In preliminary testing this produced a read-only filesystem on a 
# running host with the volume mounted.  This is intended to grossly simulate communication
# errors with the EBS volume from an EC2 instance.
#
# Note that this script will NOT detach the root volume from an EC2 instance.

jpbarto

#!/bin/bash

# The following shell script uses Amazon Athena to query AWS CloudTrail logs for any occurrences of the
# action sagemaker:ListNotebookInstances.  The query returns the user identity who invoked the API, along
# with the region where the API was called.  A simple count of the number of invokes is outputted as a
# result.

SQL="SELECT useridentity.arn, eventname, eventsource, awsregion, sourceipaddress, errorcode, eventtime FROM cloudtrail_logs_account_logging WHERE eventsource = 'sagemaker.amazonaws.com' AND eventname in ('ListNotebookInstances') AND eventtime > '2020-04-15' LIMIT 3;"

EXEC_ID=$(aws athena start-query-execution --query-string "$SQL" --result-configuration OutputLocation=s3://my-s3-logging-bucket/queries --query 'QueryExecutionId' --output text)

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[
  {
    "Entry": "/public/research",
    "Target": "/maxld-public-bucket"
  },
  {
    "Entry": "/subscribed/2018/indices",
    "Target": "/maxld-subscribe-bucket/historical/2018/indices"
  },
  {

jpbarto

#!/bin/bash

set -e

AWS_REGION=eu-west-1

aws rds describe-db-instance-automated-backups --region $AWS_REGION --query 'DBInstanceAutomatedBackups[*].{DatabaseID:DBInstanceIdentifier,Earliest:RestoreWindow.EarliestTime,Latest:RestoreWindow.LatestTime}' --output table

jpbarto

#!/bin/bash

set -e

AWS_REGION=eu-west-2
SOURCE_DATABASE_NAME=database-1
RESTORE_TIME='2020-10-28T22:02:01+00:00'

NEW_DATABASE_NAME="rds-db-$RANDOM"