Fix broken headings in Markdown files

README.md

@@ -9,7 +9,7 @@ AWS Data Pipeline is a web service that you can use to automate the movement and
 
 
 # Running the samples
-##Setup
+## Setup
 1 Get the samples by cloning this repository. 
 ```sh
  $> git clone https://github.com/awslabs/data-pipeline-samples.git
@@ -39,11 +39,11 @@ When you are finished experimenting with the examples, deactivate the virtual en
  $> aws datapipeline create-default-roles 
 ```
 
-##Run the Hello World sample
+## Run the Hello World sample
 
 The hello world sample demonstrates a pipeline that creates an EC2 instance and runs `echo Hello World!`. It can be used as a reference template for executing arbitriy shell commands.  
 
-###Step 1
+### Step 1
 Create the pipelineId by calling the *aws data pipeline create-pipeline* command. We'll use this pipelineId to host the pipeline definition document and ultimately to run and monitor the pipeline. The commands in this section should be called from within the virtual environment that you created above.
 
 ```sh
@@ -59,7 +59,7 @@ You will receive a pipelineId like this.
 #   +-------------+--------------------------+
 ```
 
-###Step 2
+### Step 2
 Upload the helloworld.json sample pipeline definition by calling the *aws datapipeline put-pipeline-definition* command. This will upload and validate your pipeline definition. 
 
 ```sh
@@ -76,7 +76,7 @@ You will receive a validation messages like this
 #   |  errored  |  False  |
 #   +-----------+---------+
 ```
-###Step 3
+### Step 3
 Activate the pipeline by calling the *aws datapipeline activate-pipeline* command. This will cause the pipeline to start running on its defined schedule. 
 
 ```sh
@@ -100,7 +100,7 @@ You will receive status information on the pipeline.
 #          @ShellCommandActivity_HelloWorld_2015-07-19T22:48:  2015-07-19T22:48:34
 
 ```
-##Examine the contents of the sample pipeline definition 
+## Examine the contents of the sample pipeline definition 
 Let's look at the Hello world example pipeline located at samples/helloworld/helloworld.json. 
 
 ```json
@@ -175,13 +175,13 @@ Let's look at the Hello world example pipeline located at samples/helloworld/hel
 }
 ```
 
-##Check out the other samples
+## Check out the other samples
 This reposity contains a collection of Data Pipeline templates that should help you get started quickly. Browse the content of the /samples folder to discover what samples exist. Also, feel free to submit samples a pull requests.  
 
 
 
 
-##Disclaimer
+## Disclaimer
 The samples in this repository are meant to help users get started with Data Pipeline. They may not be sufficient for production environments. Users should carefully inspect samples before running them.
 
 _Use at your own risk._

samples/DynamoDBExport/readme.md

@@ -1,9 +1,9 @@
-#DynamoDB to CSV export
+# DynamoDB to CSV export
 
-##About the sample
+## About the sample
 The pipeline definition is used for exporting DynamoDB data to a CSV format.
 
-##Running the pipeline 
+## Running the pipeline 
 
 Example DynamoDB table with keys: customer_id, income, demographics, financial
 

samples/DynamoDBImport/readme.md

@@ -1,6 +1,6 @@
-#XML to DynamoDB Import
+# XML to DynamoDB Import
 
-##Running the sample pipeline
+## Running the sample pipeline
 The json format could be either directly imported in the Console -> Create Pipeline or used in the aws datapipeline cli.<br/>
 The Pipeline definition would copy an example xml from s3://data-pipeline-samples/dynamodbxml/input/serde.xml to local. This step is required for creating a temporary xml table using hive. The hive script is configured for running on a DynamoDB table with keys as "customer_id, financial, income, demographics". It finally performs an import from the temporary xml table to dynamodb<br/>
 The data from the xml file is parsed using hive xml serde. The parsing functionality is similar to parsing in xpath<br/>

samples/DynamoDBImportCSV/README.md

@@ -1,9 +1,9 @@
-#DynamoDB to CSV import
+# DynamoDB to CSV import
 
-##About the sample
+## About the sample
 The pipeline definition is used to import DynamoDB data to a CSV format.
 
-##Running the pipeline 
+## Running the pipeline 
 
 Example DynamoDB table with keys: id
 

samples/DynamoDBToRedshiftConvertDataUsingHive/README.md

@@ -1,4 +1,4 @@
-#DynamoDBToRedshiftConvertDataUsingHive Sample 
+# DynamoDBToRedshiftConvertDataUsingHive Sample 
 
 This sample demonstrates how you can use Data Pipeline's HiveActivity and RedshiftCopyActivity to copy data from a DynamoDB table to a Redshift table while performing data conversion using Hive (for data transformation) and S3 (for staging).  This sample was motivated by a use case where one wishes to convert the data type of one column to another data type.  In this sample, we will be converting a column from binary to base64 string.  To make this sample to work, you must ensure you have the following:
 
@@ -14,7 +14,7 @@ We will use the [Handling Binary Type Attributes Using the AWS SDK for Java Docu
 
 The column mappings used in this sample are meant to match the table definition used in the above example.
 
-##Hive queries
+## Hive queries
 The following queries will be used to convert the ExtendedMessage column from binary to base64 string.
 ```sql
 # tempHiveTable will receive the data from DynamoDB as-is
@@ -38,7 +38,7 @@ INSERT OVERWRITE TABLE s3TempTable SELECT Id,ReplyDateTime,Message,base64(Extend
 
 You will need to provide the above information in the "put-pipeline-definition" command below.
 
-##Before running the sample
+## Before running the sample
 To simplify the example, the pipeline uses the following EMR cluster configuration:
 * Release label: emr-4.4.0
 * Master instance type: m3.xlarge
@@ -47,7 +47,7 @@ To simplify the example, the pipeline uses the following EMR cluster configurati
 
 Please feel free to modify this configuration to suite your needs.
 
-##Running this sample
+## Running this sample
 
 ```sh
 $> aws datapipeline create-pipeline --name data_conversion_using_hive --unique-id data_conversion_using_hive 
@@ -111,7 +111,7 @@ $> aws datapipeline list-runs --pipeline-id df-0554887H4KXKTY59MRJ
 #       @TableBackupActivity_2016-03-31T23:38:34            2016-03-31T23:38:38                     
 ```
 
-##Related documentation
+## Related documentation
 * [HiveActivity](http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-hiveactivity.html)
 * [RedshiftCopyActivity](https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-redshiftcopyactivity.html)
 

samples/EFSBackup/README.md

@@ -1,6 +1,6 @@
 # EFSBackup
 
-#####A collection of AWS Data Pipeline templates and scripts used to backup & restore Amazon EFS file systems
+##### A collection of AWS Data Pipeline templates and scripts used to backup & restore Amazon EFS file systems
 
 If you need to be able to recover from unintended changes or deletions in your Amazon EFS file systems, you'll need to implement a backup solution. Once such backup solution is presented in the EFS documentation, and can be found here: http://docs.aws.amazon.com/efs/latest/ug/efs-backup.html.
 

samples/LoadTsvFilesInS3ToRedshift/README.md

@@ -1,9 +1,9 @@
-#Data Pipeline  Load Tab Separated Files in S3 to Redshift 
+# Data Pipeline  Load Tab Separated Files in S3 to Redshift 
 
-##About the sample
+## About the sample
 This pipeline definition when imported would instruct Redshift to load TSV files under the specified S3 Path into a specified Redshift Table. Table insert mode is OVERWRITE_EXISTING.
 
-##Running this sample
+## Running this sample
 The pipeline requires the following user input point:
 
 1. The S3 folder where the input TSV files are located.