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Question 11 - MLS-C01 discussion

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A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.

Which combination of AWS services will meet these requirements?

A.
Amazon EMR for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
Answers
A.
Amazon EMR for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
B.
Amazon Kinesis Data Analytics for data ingestion Amazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3
Answers
B.
Amazon Kinesis Data Analytics for data ingestion Amazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3
C.
AWS Glue for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
Answers
C.
AWS Glue for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
D.
AWS Data Pipeline for data transfer AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
Answers
D.
AWS Data Pipeline for data transfer AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
Suggested answer: C

Explanation:

The best combination of AWS services to meet the requirements of data discovery, enrichment, transformation, querying, analysis, and reporting with the least coding and infrastructure management is AWS Glue, Amazon Athena, and Amazon QuickSight. These services are:

AWS Glue for data discovery, enrichment, and transformation. AWS Glue is a serverless data integration service that automatically crawls, catalogs, and prepares data from various sources and formats.It also provides a visual interface called AWS Glue DataBrew that allows users to apply over 250 transformations to clean, normalize, and enrich data without writing code1

Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon Athena is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. It supports a variety of data formats, such as CSV, JSON, ORC, Parquet, and Avro.It also integrates with AWS Glue Data Catalog to provide a unified view of the data sources and schemas2

Amazon QuickSight for reporting and getting insights. Amazon QuickSight is a serverless business intelligence service that allows users to create and share interactive dashboards and reports.It also provides ML-powered features, such as anomaly detection, forecasting, and natural language queries, to help users discover hidden insights from their data3

The other options are not suitable because they either require more coding effort, more infrastructure management, or do not support the desired use cases. For example:

Option A uses Amazon EMR for data discovery, enrichment, and transformation. Amazon EMR is a managed cluster platform that runs Apache Spark, Apache Hive, and other open-source frameworks for big data processing. It requires users to write code in languages such as Python, Scala, or SQL to perform data integration tasks.It also requires users to provision, configure, and scale the clusters according to their needs4

Option B uses Amazon Kinesis Data Analytics for data ingestion. Amazon Kinesis Data Analytics is a service that allows users to process streaming data in real time using SQL or Apache Flink. It is not suitable for data discovery, enrichment, and transformation, which are typically batch-oriented tasks.It also requires users to write code to define the data processing logic and the output destination5

Option D uses AWS Data Pipeline for data transfer and AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation. AWS Data Pipeline is a service that helps users move data between AWS services and on-premises data sources. AWS Step Functions is a service that helps users coordinate multiple AWS services into workflows. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services require users to write code to define the data sources, destinations, transformations, and workflows. They also require users to manage the scalability, performance, and reliability of the data pipelines.

References:

1:AWS Glue - Data Integration Service - Amazon Web Services

2:Amazon Athena -- Interactive SQL Query Service - AWS

3:Amazon QuickSight - Business Intelligence Service - AWS

4:Amazon EMR - Amazon Web Services

5: Amazon Kinesis Data Analytics - Amazon Web Services

: AWS Data Pipeline - Amazon Web Services

: AWS Step Functions - Amazon Web Services

: AWS Lambda - Amazon Web Services

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asked 16/09/2024
Jeff Benson
44 questions
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