Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - Logstash and Simple Storage Service (S3)

The video tutorial explains how to use AWS S3 for storing data and demonstrates the process of importing data from an S3 bucket into Elasticsearch using Logstash. It covers setting up an S3 bucket, creating an IAM user for access, configuring Logstash to read from the S3 bucket, and verifying the data import into Elasticsearch. The tutorial emphasizes the ease and cost-effectiveness of using AWS services for data storage and processing.

What is one of the main advantages of using AWS S3 for data storage?

• It provides unlimited storage for free.

• It only supports small data sets.

• It automatically distributes and backs up data across the cloud.

• It requires no access credentials for data retrieval.

What is a necessary step before importing data from S3 into Elasticsearch?

• Disabling public access to the S3 bucket.

• Installing Elasticsearch on AWS.

• Setting up a globally unique S3 bucket.

• Creating a new AWS account.

Which AWS service is used to manage access credentials for S3?

• Amazon EC2

• AWS Lambda

• Amazon RDS

• AWS IAM

What is the purpose of the Logstash configuration file in this process?

• To store data locally.

• To create a new S3 bucket.

• To specify the S3 bucket and access credentials.

• To delete existing data in Elasticsearch.

What should be done after modifying the Logstash configuration file?

• Restart the AWS S3 service.

• Delete the S3 bucket.

• Create a new IAM user.

• Run Logstash to process the data.

How can you verify that data has been successfully imported into Elasticsearch?

• By checking the S3 bucket for new files.

• By querying the Elasticsearch index.

• By logging into the AWS console.

• By restarting the Elasticsearch server.

What is an alternative to using Logstash for importing data into Elasticsearch?

• Using Amazon EC2.

• Using AWS's built-in mechanisms.

• Using AWS RDS.

• Using AWS Lambda.