A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?
Answer : A
Problem Analysis:
Input Requirements: Gaming app generates approximately 20 KB data records, which must be ingested and made available to three internal consumers with dedicated throughput.
Key Requirements:
High throughput for ingestion from each device.
Dedicated processing bandwidth for each consumer.
Key Considerations:
Amazon Kinesis Data Streams supports high-throughput ingestion with PutRecords API for batch writes.
The Enhanced Fan-Out feature provides dedicated throughput to each consumer, avoiding bandwidth contention.
This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.
Solution Analysis:
Option A: Kinesis Data Streams + Enhanced Fan-Out
PutRecords API is designed for batch writes, improving ingestion performance.
Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.
Option B: Data Firehose + Dedicated Throughput Request
Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.
Option C: Data Firehose + Enhanced Fan-Out
Firehose does not support enhanced fan-out. This option is invalid.
Option D: Kinesis Data Streams + EC2 Instances
Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.
Final Recommendation:
Use Kinesis Data Streams with Enhanced Fan-Out for high-throughput ingestion and dedicated consumer bandwidth.
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?
Answer : B
Problem Analysis:
The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.
If a file is incomplete or empty, the previous day's file should be retained.
Need a solution to validate files before overwriting the existing file.
Key Considerations:
Automate data validation with minimal human intervention.
Use built-in AWS Glue capabilities for ease of integration.
Ensure robust validation for missing or incomplete data.
Solution Analysis:
Option A: Lambda Function for Validation
Lambda can validate files, but it would require custom code.
Does not leverage AWS Glue's built-in features, adding operational complexity.
Option B: AWS Glue Data Quality Rules
AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.
Rules can validate if required fields are missing or if the file is empty.
Automatically integrates into the existing ETL pipeline.
If validation fails, retain the previous day's file.
Option C: AWS Glue Studio with Filling Missing Values
Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.
Does not handle empty files effectively.
Option D: Athena Query for Validation
Athena can drop rows with missing values, but this is a post-hoc solution.
Requires manual intervention to copy the corrected file to S3, increasing complexity.
Final Recommendation:
Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.
This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.
Implementation Steps:
Enable AWS Glue Data Quality in the existing ETL pipeline.
Define DQDL Rules, such as:
Check if a file is empty.
Verify required fields are present and non-null.
Configure the pipeline to proceed with overwriting only if the file passes validation.
In case of failure, retain the previous day's file.
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?
Answer : A
Problem Analysis:
The company requires a centralized data warehouse for consolidating data from various sources.
They use Amazon QuickSight in direct query mode, necessitating fast response times for analytical queries.
Users query the data intermittently, with unpredictable spikes during the day.
Operational overhead should be minimal.
Key Considerations:
The solution must support fast, SQL-based analytics.
It must handle unpredictable spikes efficiently.
Must integrate seamlessly with QuickSight for direct querying.
Minimize operational complexity and scaling concerns.
Solution Analysis:
Option A: Amazon Redshift Serverless
Redshift Serverless eliminates the need for provisioning and managing clusters.
Automatically scales compute capacity up or down based on query demand.
Reduces operational overhead by handling performance optimization.
Fully integrates with Amazon QuickSight, ensuring low-latency analytics.
Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.
Option B: Amazon Athena with S3 (Apache Parquet)
Athena supports querying data directly from S3 in Parquet format.
While it's cost-effective, performance depends on the size and complexity of the data.
It is not optimized for high-speed analytics needed by QuickSight in direct query mode.
Option C: Amazon Redshift Provisioned Clusters
Requires manual cluster provisioning, scaling, and maintenance.
Higher operational overhead compared to Redshift Serverless.
Option D: Amazon Aurora PostgreSQL
Aurora is optimized for transactional databases, not data warehousing or analytics.
Does not meet the requirement for fast analytics queries.
Final Recommendation:
Amazon Redshift Serverless is the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake. Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?
Answer : C
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.
A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
Which solution will meet this requirement with the LEAST manual intervention?
Answer : A
Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party too in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
Answer : B
The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the most suitable solution for this scenario.
Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA) MWAA is a managed orchestration service that supports Python and Bash scripts via Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.
Other options:
AWS Lambda (Option A) is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.
AWS Step Functions (Option C) is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.
AWS Glue (Option D) is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.
Amazon Managed Workflows for Apache Airflow (MWAA) Documentation
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?
Answer : D
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
Option D: YourEnvironmentName-Task Task logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails. These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.