AWS Certified Data Analytics Specialty 2021 - Hands-On! - [Exercise] Amazon Elasticsearch Service

This tutorial covers the integration of Amazon Kinesis Data Firehose with Elasticsearch for real-time data processing and visualization. It includes setting up an EC2 instance, configuring Apache logs, creating an Elasticsearch cluster, and using AWS Lambda for log transformation. The video demonstrates how to visualize data in Kibana and analyze server errors, concluding with steps to shut down AWS resources to avoid unnecessary costs.

What is the primary purpose of integrating Kinesis Data Firehose with Elasticsearch in this tutorial?

• To enhance data security

• To store data permanently

• To replace traditional databases

• To create interactive dashboards and visualizations

What tool is used to log into the EC2 instance to access Apache server logs?

• RDP

• FTP

• Putty

• SSH

Why is a development and testing cluster chosen over a production cluster for Elasticsearch?

• To enhance security

• To increase performance

• To reduce costs

• To ensure high availability

Why is it important to lock down access to the Elasticsearch domain to a specific IP address?

• To allow multiple users to access

• To improve data processing speed

• To enhance security by restricting access

• To reduce data storage costs

What is the role of AWS Lambda in the Kinesis Data Firehose setup?

• To encrypt data

• To store data

• To monitor data flow

• To transform Apache logs into JSON format

What is the purpose of setting the index rotation to every day in Elasticsearch?

• To improve data security

• To manage data storage efficiently

• To enhance data retrieval speed

• To reduce data redundancy

What is the significance of setting the initial position to 'start_of_file' in the Kinesis agent configuration?

• To ensure all log data, including old entries, is streamed

• To skip the first few entries

• To prioritize recent data

• To process only new log entries

What tool is used to visualize the data in Elasticsearch?

• Power BI

• Grafana

• Tableau

• Kibana

How can you identify the source of 500 server errors using Kibana?

• By using a third-party monitoring tool

• By creating a vertical bar visualization with a filter for 500 errors

• By checking the server logs manually

• By analyzing the raw data in Elasticsearch

What should be done with the resources after completing the exercise to avoid unnecessary costs?

• Convert them to a different service

• Transfer them to another account

• Delete or terminate them

• Leave them running for future use