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NEW QUESTION # 112
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?
Answer: A
Explanation:
Step 1: Understanding the Problem
The company collects clickstream data via Amazon Kinesis Data Streams and stores it in JSON format in Amazon S3 using Kinesis Data Firehose. They use Amazon Athena to query the data, but they want to reduce Athena costs while maintaining the same data pipeline.
Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format like Apache Parquet) is a key solution to lowering costs.
Step 2: Why Option A is Correct
Option A provides a straightforward way to reduce costs with minimal management overhead:
Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.
Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.
AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one-time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.
ALTER TABLE ADD PARTITION: This command updates Athena's table metadata to reflect the new partitions, ensuring that future queries target only the required data.
Step 3: Why Other Options Are Not Ideal
Option B (Apache Spark on EMR) introduces higher management effort by requiring the setup of Apache Spark jobs and an Amazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.
Option C (Kinesis and Apache Flink) is a more complex solution involving Apache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.
Option D (AWS Lambda with Firehose) suggests using AWS Lambda to convert records in real time. While Lambda can work in some cases, it's generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations. Additionally, running parallel Glue jobs further complicates the setup.
Step 4: How Option A Minimizes Costs
By using Apache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.
Firehose natively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.
The AWS Glue ETL job for historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.
Conclusion:
Option A meets the requirement to reduce Athena costs without recreating the data pipeline, using Firehose's native support for Apache Parquet and a simple one-time AWS Glue ETL job for existing data. This approach involves minimal management effort compared to the other solutions.
NEW QUESTION # 113
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming dat
a. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day's data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. Reference:
Using streaming ingestion with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion
NEW QUESTION # 114
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.
Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, andscale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.
Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.
The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:
* It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.
* It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.
* It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.
Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster.
This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.
Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.
Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:
* It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.
* It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.
:
1: AWS Lambda - Features
2: Amazon Elastic Container Service - Features
3: Amazon Elastic Kubernetes Service - Features
[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway
[5]: Improving latency with Provisioned Concurrency - AWS Lambda
[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service
[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service
[8]: Managing concurrency for a Lambda function - AWS Lambda
NEW QUESTION # 115
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between 100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
NEW QUESTION # 116
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?
Answer: B
Explanation:
Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient.
Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code.
References:
AWS Lambda layers
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda
NEW QUESTION # 117
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