A company has data stores both on their on-premise and AWS Environments. They need to first create a data lake in AWS and then orchestrate several ETL jobs. Which of the following can be used to fulfil this requirement? Choose 2 answers from the options given below

Answer options:

A.Use AWS S3 for storage of the data lake
B.Use AWS EMR for storage of the data lake
C.Use a combination of AWS Lambda and Step Functions
D.Use SQS for the ETL jobs

Answer – A and C
The AWS Documentation mentions the following
Extract, transform, and load (ETL) operations collectively form the backbone of any modern enterprise data lake. It transforms raw data into useful datasets and, ultimately, into actionable insight. An ETL job typically reads data from one or more data sources, applies various transformations to the data, and then writes the results to a target where data is ready for consumption. The sources and targets of an ETL job could be relational databases in Amazon Relational Database Service (Amazon RDS) or on-premises, a data warehouse such as Amazon Redshift, or object storage such as Amazon Simple Storage Service (Amazon S3) buckets. Amazon S3 as a target is especially commonplace in the context of building a data lake in AWS.
You can also use AWS Step Functions and AWS Lambda for orchestrating multiple ETL jobs involving a diverse set of technologies in an arbitrarily-complex ETL workflow
Option B is incorrect since AWS S3 will be a more viable option for a data lake
Option D is incorrect since this is a messaging service
For more information on a use case for this, please refer to the below URL
https://aws.amazon.com/blogs/big-data/orchestrate-multiple-etl-jobs-using-aws-step-functions-and-aws-lambda/