1. AWS Athena Practical Demonstration AWS Data Engineering with Data Analytics involves leveraging Amazon Web Services (AWS) to design, implement, and optimize Data pipelines for efficient data processing and analytics. This comprehensive approach encompasses the extraction, transformation, and loading (ETL) of data from diverse sources, storage in scalable and secure data repositories, and the application of analytics to derive valuable insights.Amazon Athena is an interactive query service that enables you to analyse data stored in Amazon S3 using SQL. Here's a practical demonstration of how you can use Athena AWS Data Engineering Online Training Prerequisites: AWS Account: Ensure you have an AWS account with the necessary permissions to access Amazon Athena. Data in Amazon S3: Make sure you have data stored in Amazon S3 that you want to query with Athena. Steps: Navigate to Athena in AWS Console: Go to the [AWS Management Console](https://console.aws.amazon.com/). In the "Find Services" search bar, type "Athena" and select it.

2. Create a Database: In the Athena console, click on "Query Editor" on the left side. On the right side, click on "Settings" and then choose "Data" tab. In the "Data sources" section, click "Connect to a data source." Follow the instructions to create a new database and table, specifying the S3 location of your data. AWS Data Engineering Training Run a Sample Query: After creating a database and table, you can run SQL queries in the "Query Editor." For example, if you have a table named "mytable," you can run a simple query like: ```sql SELECT * FROM mytable LIMIT 10; ``` Click "Run Query" to execute the query. Query Optimization: Athena automatically optimizes your query by converting it into a series of low- level queries that run against the data stored in S3. You can review the query execution details and optimize queries if needed. Data Engineer Training in Hyderabad Save and Export Results: If you want to save or export query results, you can do so from the "Query Editor." Partitioning and Performance: If your data in S3 is partitioned, you can leverage partitioning to improve query performance. Ensure that your table is appropriately partitioned. Accessing Athena Using the AWS CLI:

3. Alternatively, you can use the AWS CLI to execute Athena queries. Install the AWS CLI and configure it with your AWS credentials. Example AWS CLI command to run a query: ```bash aws Athena start-query-execution --query-string "SELECT * FROM mytable LIMIT 10" --result-configuration "OutputLocation=s3://your-query-results-bucket/" ``` Replace `mytable` with your table name and adjust the S3 output location. AWS Data Engineering Training in Hyderabad Explore Advanced Use Cases: Athena supports complex queries, joins, and integration with other AWS services. Explore more advanced use cases based on your specific requirements. Cost Considerations: Be mindful of Athena's pricing, which is based on the amount of data scanned by your queries. Clean-up Resources: After completing your demonstration, consider cleaning up resources to avoid incurring unnecessary costs. Data Engineer Course in Ameerpet Remember documentation](https://docs.aws.amazon.com/athena/latest/ug/what- is.html) for more detailed information and guidance on using Athena for your specific use case. to refer to the [Amazon Athena Visualpath is the Leading and Best Institute for AWS Data Engineering Online Training, Hyderabad. We AWS Data Engineering Training provide you will get the best course at an affordable cost. Attend Free Demo Call on - +91-9989971070.

4. Visit : https://www.visualpath.in/aws-data-engineering- with-data-analytics-training.html