Learn how to leverage AWS Step Functions to manage and automate complex data flows with minimal maintenance. Find out why Step Functions is the best choice for scalable data management solutions.
Table of Contents
Question
A company is building a scalable data management solution by using AWS services to improve the speed and agility of development. The solution will ingest large volumes of data from various sources and will process this data through multiple business rules and transformations.
The solution requires business rules to run in sequence and to handle reprocessing of data if errors occur when the business rules run. The company needs the solution to be scalable and to require the least possible maintenance.
Which AWS service should the company use to manage and automate the orchestration of the data flows to meet these requirements?
A. AWS Batch
B. AWS Step Functions
C. AWS Glue
D. AWS Lambda
Answer
B. AWS Step Functions
Explanation
The correct answer to the question is B. AWS Step Functions.
The reason for this answer is that:
- Option B: AWS Step Functions is a service that allows you to create and manage serverless workflows that can orchestrate multiple AWS services, such as AWS Lambda, Amazon S3, Amazon DynamoDB, Amazon SQS, and more. You can use Step Functions to define the sequence and logic of your business rules and transformations using a JSON-based state machine language or a visual interface. You can also use Step Functions to handle errors and retries, monitor the execution status and history, and trigger workflows based on events or schedules. Step Functions can scale automatically and reliably to handle large volumes of data and complex workflows, without requiring any infrastructure management or maintenance. Therefore, Step Functions is the best choice for managing and automating the orchestration of the data flows in the solution.
- Option A: AWS Batch is a service that enables you to run batch computing workloads on AWS. You can use AWS Batch to submit jobs that run on EC2 instances or Fargate containers, and AWS Batch will take care of provisioning, scaling, scheduling, and monitoring the resources for you. However, AWS Batch is not designed for orchestrating workflows that involve multiple AWS services or complex business logic. AWS Batch does not provide any built-in features for defining the sequence and logic of your jobs, handling errors and retries, or triggering workflows based on events or schedules. Therefore, AWS Batch is not a suitable choice for managing and automating the orchestration of the data flows in the solution.
- Option C: AWS Glue is a fully managed extract, transform, and load (ETL) service that allows you to prepare and load data for analytics. You can use AWS Glue to discover, catalog, clean, enrich, and transform data from various sources and formats, such as S3, RDS, DynamoDB, Redshift, etc. You can also use AWS Glue to run ETL jobs on a serverless Spark environment that scales automatically based on your data volume and complexity. However, AWS Glue is not optimized for orchestrating workflows that involve multiple business rules and transformations that need to run in sequence and handle reprocessing of data if errors occur. AWS Glue does not provide any native features for defining the sequence and logic of your ETL jobs, handling errors and retries, or triggering workflows based on events or schedules. Therefore, AWS Glue is not an ideal choice for managing and automating the orchestration of the data flows in the solution.
- Option D: AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. You can use Lambda to execute code in response to events from various sources, such as S3, API Gateway, SQS, SNS, etc., or on a schedule using EventBridge or CloudWatch Events. Lambda can also integrate with other AWS services to perform various tasks, such as accessing S3 objects, querying DynamoDB tables, invoking other Lambda functions, etc. However, Lambda is not a workflow orchestration service by itself. Lambda does not provide any native features for defining the sequence and logic of your functions, handling errors and retries, or monitoring the execution status and history of your workflows. Therefore, Lambda is not a complete choice for managing and automating the orchestration of the data flows in the solution.
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