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Answer: Use Amazon Athena for one-time queries. Use Amazon QuickSight to create dashboards for KPIs., Use blueprints in AWS Lake Formation to identify the data that can be ingested into a data lake. Use AWS Glue to crawl the source, extract the data, and load the data into Amazon S3 in Apache Parquet format.
## Explanation **Correct Answers: A and E** **Why A is correct:** - Amazon Athena is a serverless query service that allows running SQL queries directly on data stored in Amazon S3 - It's ideal for one-time queries with minimal operational overhead (no infrastructure to manage) - Amazon QuickSight is AWS's native BI tool that integrates well with Athena for creating dashboards and KPIs **Why E is correct:** - AWS Lake Formation blueprints help automate data ingestion from various sources into a data lake - AWS Glue can crawl, extract, and transform data into efficient formats like Apache Parquet - Parquet format is columnar and optimized for query performance - This approach minimizes operational overhead by using managed services **Why B is incorrect:** - Amazon Kinesis Data Analytics is designed for real-time streaming analytics, not one-time queries - It's overkill for batch processing requirements and would add unnecessary complexity **Why C is incorrect:** - Creating custom Lambda functions requires significant development and maintenance overhead - Moving data to Amazon Redshift adds complexity and cost for data warehousing when S3-based solutions would suffice **Why D is incorrect:** - Loading data into multiple OpenSearch Service clusters creates operational overhead for management - JSON format is less efficient than Parquet for analytics workloads - OpenSearch is better for search and log analytics, not for general business analytics **Key Architecture Principles:** 1. **Serverless approach** minimizes operational overhead 2. **Data lake architecture** on S3 provides flexibility and scalability 3. **Managed services** (Athena, Glue, Lake Formation) reduce maintenance burden 4. **Efficient data formats** (Parquet) optimize query performance and cost 5. **Separation of storage and compute** allows independent scaling
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A company produces batch data that comes from different databases. The company also produces live stream data from network sensors and application APIs. The company needs to consolidate all the data into one place for business analytics. The company needs to process the incoming data and then stage the data in different Amazon S3 buckets. Teams will later run one-time queries and import the data into a business intelligence tool to show key performance indicators (KPIs).
Which combination of steps will meet these requirements with the LEAST operational overhead? (Choose two.)
A
Use Amazon Athena for one-time queries. Use Amazon QuickSight to create dashboards for KPIs.
B
Use Amazon Kinesis Data Analytics for one-time queries. Use Amazon QuickSight to create dashboards for KPIs.
C
Create custom AWS Lambda functions to move the individual records from the databases to an Amazon Redshift cluster.
D
Use an AWS Glue extract, transform, and load (ETL) job to convert the data into JSON format. Load the data into multiple Amazon OpenSearch Service (Amazon Elasticsearch Service) clusters.
E
Use blueprints in AWS Lake Formation to identify the data that can be ingested into a data lake. Use AWS Glue to crawl the source, extract the data, and load the data into Amazon S3 in Apache Parquet format.