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README.md

@@ -12,7 +12,7 @@ In this example application, we deliver notes from an interview in Markdown form
 
 ### Event Trigger
 
-In this architecture, individual files are processed as they arrive. To achive this, we utilize [AWS S3 Events](https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html) and [Amazon Simple Notification Service](https://docs.aws.amazon.com/sns/latest/dg/welcome.html). When an object is created in S3, an event is emitted to a SNS topic. We deliver our event to 2 seperate [SQS Queues](https://aws.amazon.com/sqs/), representing 2 different workflows. Refer to [What is Amazon Simple Notification Service?](https://docs.aws.amazon.com/sns/latest/dg/welcome.html) for more information about eligible targets.
+In this architecture, individual files are processed as they arrive. To achieve this, we utilize [AWS S3 Events](https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html) and [Amazon Simple Notification Service](https://docs.aws.amazon.com/sns/latest/dg/welcome.html). When an object is created in S3, an event is emitted to a SNS topic. We deliver our event to 2 separate [SQS Queues](https://aws.amazon.com/sqs/), representing 2 different workflows. Refer to [What is Amazon Simple Notification Service?](https://docs.aws.amazon.com/sns/latest/dg/welcome.html) for more information about eligible targets.
 
 ### Conversion Workflow
 
@@ -26,15 +26,15 @@ Our function will take Markdown files stored in our **InputBucket**, detect the
 
 We are using [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect overall interview sentiment.  Amazon Comprehend is a machine learning powered service that makes it easy to find insights and relationships in text. We use the Sentiment Analysis API to understand whether interview responses are positive or negative.
 
-The Sentiment workflow uses the same SQS-to-Lambda Function pattern as the Coversion workflow.
+The Sentiment workflow uses the same SQS-to-Lambda Function pattern as the Conversion workflow.
 
 If our **SentimentFunction** cannot remove the messages from the **SentimentQueue**, they are sent to **SentimentDlq**, a dead-letter queue (DLQ), for inspection. A CloudWatch Alarm is configured to send notification to an email address when there are any messages in the **SentimentDlq**.
 
 ## Building and Deploying the Application with the AWS Serverless Application Model (AWS SAM)
 
 This application is deployed using the [AWS Serverless Application Model (AWS SAM)](https://aws.amazon.com/serverless/sam/).  AWS SAM is an open-source framework that enables you to build serverless applications on AWS.  It provides you with a template specification to define your serverless application, and a command line interface (CLI) tool.
 
-### Pre-requisites
+### Prerequisites
 
 * [AWS CLI version 2](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html)
 
@@ -238,9 +238,9 @@ creates the following resources:
 
 - **AlarmTopic** - A SNS topic that has an email as a subscriber.  This topic is used to receive alarms from the **ConversionDlqAlarm**, **SentimentDlqAlarm**, **ConversionQueueAlarm**, **SentimentQueueAlarm**, **ConversionFunctionErrorRateAlarm**, **SentimentFunctionErrorRateAlarm**, **ConversionFunctionThrottleRateAlarm**, and **SentimentFunctionThrottleRateAlarm**.
 
-- **ConversionDlqAlarm** - A CloudWatch Alarm that detects when there there are any messages sent to the **ConvesionDlq** within a 1 minute period and sends a notification to the **AlarmTopic**.
+- **ConversionDlqAlarm** - A CloudWatch Alarm that detects when there are any messages sent to the **ConversionDlq** within a 1 minute period and sends a notification to the **AlarmTopic**.
 
-- **SentimentDlqAlarm** - A CloudWatch Alarm that detects when there there are any messages sent to the **SentimentDlq** within a 1 minute period and sends a notification to the **AlarmTopic**.
+- **SentimentDlqAlarm** - A CloudWatch Alarm that detects when there are any messages sent to the **SentimentDlq** within a 1 minute period and sends a notification to the **AlarmTopic**.
 
 - **ConversionQueueAlarm** - A CloudWatch Alarm that detects when there are 20 or more messages in the **ConversionQueue** within a 1 minute period and sends a notification to the **AlarmTopic**.
 
@@ -250,9 +250,9 @@ creates the following resources:
 
 - **SentimentFunctionErrorRateAlarm** - A CloudWatch Alarm that detects when there is an error rate of 5% over a 5 minute period for the **SentimentFunction** and sends a notification to the **AlarmTopic**.
 
-- **ConversionFunctionThrottleRateAlarm** - A CloudWatch Alarm that detects when ther is a throttle rate of 1% over a 5 minute period for the **ConversionFunction** and sends a notification to the **AlarmTopic**.
+- **ConversionFunctionThrottleRateAlarm** - A CloudWatch Alarm that detects when there is a throttle rate of 1% over a 5 minute period for the **ConversionFunction** and sends a notification to the **AlarmTopic**.
 
-- **SentimentFunctionThrottleRateAlarm** - A CloudWatch Alarm that detects when ther is a throttle rate of 1% over a 5 minute period for the **SentimentFunction** and sends a notification to the **AlarmTopic**.
+- **SentimentFunctionThrottleRateAlarm** - A CloudWatch Alarm that detects when there is a throttle rate of 1% over a 5 minute period for the **SentimentFunction** and sends a notification to the **AlarmTopic**.
 
 - **ApplicationDashboard** - A CloudWatch Dashboard that displays Conversion Function Invocations, Conversion Function Error Rate, Conversion Function Throttle Rate, Conversion DLQ Length, Sentiment Function Invocations, Sentiment Function Error Rate, Sentiment Function Throttle Rate, and Sentiment DLQ Length.
 

tests.sh

@@ -93,7 +93,7 @@ DYNAMO_TABLE=$(aws cloudformation describe-stack-resource \
 
 
 echo "Found Input Bucket: $BUCKET_IN"
-echo "Found Ouput Bucket: $BUCKET_OUT"
+echo "Found Output Bucket: $BUCKET_OUT"
 echo "Found DynamoDB Table: $DYNAMO_TABLE"
 
 ## Get Samples