Screenshots update, Duplicate section repair and Athena first time setup

.gitignore

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+.idea
+.DS_Store

Lab1/README.md

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 ## Architectural Diagram
 ![architecture-overview-lab1.png](https://s3.amazonaws.com/us-east-1.data-analytics/labcontent/reinvent2017content-abd313/lab1/Screen+Shot+2017-11-17+at+1.11.18+AM.png)
 
+### Setting up Athena (first time users)
+
+If you’re a first time Athena user, you might need to configure an S3 bucket, where Athena will store the query results.
+
+![image](../Lab1/img/athena-setup.png)
+
+You can use an already existing bucket with a dedicated folder or you can create a new, dedicated bucket.
+
+<b>NOTE:</b> Make sure you have forward slash at the end of the S3 path
+
 ## Creating Amazon Athena Database and Table 
 
 Amazon Athena uses Apache Hive to define tables and create databases. Databases are a logical grouping of tables. When you create a database and table in Athena, you are simply describing the schema and location of the table data in Amazon S3\. In case of Hive, databases and tables don’t store the data along with the schema definition unlike traditional relational database systems. The data is read from Amazon S3 only when you query the table. The other benefit of using Hive is that the metastore found in Hive can be used in many other big data applications such as Spark, Hadoop, and Presto. With Athena catalog, you can now have Hive-compatible metastore in the cloud without the need for provisioning a Hadoop cluster or RDS instance. For guidance on databases and tables creation refer [Apache Hive documentation](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL). The following steps provide guidance specifically for Amazon Athena.