AWS Certified Data Analytics Specialty 2021 – Hands-On - Lambda Integration - Part 2

The video tutorial explains how AWS Lambda can be integrated with various AWS services like Elasticsearch, Data Pipeline, Redshift, and Kinesis. It covers the use of Lambda to automate data processing and indexing in Elasticsearch, trigger data pipelines on demand, load data into Redshift, and handle Kinesis streaming data. The tutorial also highlights potential challenges and best practices, such as managing batch sizes and handling errors effectively.

What is a common use case for integrating AWS Lambda with Elasticsearch?

• Video streaming

• Log processing

• Image processing

• Data encryption

How does AWS Lambda enhance the functionality of AWS Data Pipeline?

• By reducing data transfer costs

• By increasing storage capacity

• By enabling on-demand data processing

• By providing a user interface

What is a challenge when using AWS Lambda with Redshift?

• Lambda's stateful nature

• Lambda's stateless nature

• Redshift's limited storage

• Redshift's slow processing speed

Which AWS service can be used to track the progress of data imports into Redshift?

• Amazon S3

• Amazon RDS

• Amazon Kinesis

• Amazon DynamoDB

What is a potential issue when processing streaming data with AWS Lambda and Kinesis?

• Slow network speed

• Exceeding batch size limits

• High data transfer costs

• Insufficient storage

What is the maximum default runtime for an AWS Lambda function?

• 1200 seconds

• 600 seconds

• 900 seconds

• 300 seconds

How can you prevent a Lambda function from timing out when processing large batches of streaming data?

• Disable retries

• Decrease the memory allocation

• Use more shards

• Increase the batch size