Amazon Web Services Data-Engineer-Associate - AWS Certified Data Engineer - Associate (DEA-C01)

AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation—all with minimal code and operational overhead.

“Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.”

Problem Analysis:

Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.

Processing time is increasing due to the additional testing facilities.

The goal is to reduce processing time while using the existing AWS Glue framework.

Key Considerations:

AWS Glue offers the dynamic frame file-grouping feature, which consolidates small files into larger, more efficient datasets during processing.

Grouping smaller files reduces overhead and speeds up processing.

Solution Analysis:

Option A: Lambda for File Grouping

Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.

Option B: AWS Glue Dynamic Frame File-Grouping

This option directly addresses the issue by grouping small files during Glue job execution.

Minimizes data processing time with no extra overhead.

Option C: Redshift COPY Command

COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).

Option D: Amazon EMR

While EMR is powerful, replacing Glue with EMR increases operational complexity.

Final Recommendation:

Use AWS Glue dynamic frame file-grouping for optimized data ingestion and processing.

AWS Glue Dynamic Frames

Optimizing Glue Performance

For near real-time processing of new files in S3, event-driven ingestion is optimal. S3 Event Notifications can trigger AWS Lambda to immediately load data into Amazon Redshift, eliminating latency associated with batch scheduling.

“Event-based triggers using S3 notifications and Lambda functions are effective for near real-time ingestion pipelines into Amazon Redshift.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C (Glue bookmarks) is best for batch jobs, and zero-ETL applies to Aurora to Redshift, not S3.

Step 1: Understand the Data Access Requirements

The question presents distinct access needs for three teams:

Marketing team: Needs full access to customer contact info but only obfuscated claim information.

Claims team: Needs access to customer information relevant to the claims they process.

Analytics team: Needs only obfuscated PII data.

These teams require different levels of access, and the solution needs to enforce data security while keeping administrative overhead low.

Step 2: Why Option B is Correct

Option B (Creating Views) is a common best practice in Amazon Redshift to restrict access to specific data without duplicating data or managing multiple clusters. By creating views:

You can define customized views of the data with obfuscated fields for the analytics team and marketing team while still providing full access where necessary.

Views provide a logical separation of data and allow Redshift administrators to grant access permissions based on roles or groups, ensuring that each team sees only what they are allowed to.

Obfuscation or masking of PII can be easily applied to the views by transforming or hiding sensitive data fields.

This approach avoids the complexity of managing multiple Redshift clusters or S3-based data lakes, which introduces higher operational and administrative overhead.

Step 3: Why Other Options Are Not Ideal

Option A (Separate Redshift Clusters) introduces unnecessary administrative overhead by managing multiple clusters. Maintaining several clusters for each team is costly, redundant, and inefficient.

Option C (Separate Redshift Roles) involves creating multiple roles and managing complex masking policies, which adds to administrative burden and complexity. While Redshift does support column-level access control, it's still more overhead than managing simple views.

Option D (Move to S3 and Lake Formation) is a more complex and heavy-handed solution, especially when the data is already stored in Redshift. Migrating the data to S3 and setting up a data lake with Lake Formation introduces significant operational complexity that isn't needed for this specific requirement.

Conclusion:

Creating views in Amazon Redshift allows for flexible, fine-grained access control with minimal overhead, making it the optimal solution to meet the data access requirements of the marketing, claims, and analytics teams.

 AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:

AWS Glue

[AWS Glue Data Catalog]

[AWS Glue Developer Guide]

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on-premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes. AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog. You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.

The other options are not optimal for the following reasons:

A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.

C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.

D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers’ ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.

1: AWS Glue Data Catalog

2: AWS Glue Crawlers

Amazon Aurora

AWS Lambda

Amazon DynamoDB

The DatasetMatch rule in DQDL checks for full value equivalence between mapped fields. A value of 1.0 indicates a 100% match. The correct syntax and metric for an exact match scenario are:

“Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Options with “100” use incorrect syntax since DQDL uses floating-point scores (e.g., 1.0, 0.95), not percentages.

Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.

The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. References:

Using Apache Spark in Amazon Athena

Athena JDBC Driver

Spark SQL

Athena query settings

[Athena workgroups]

[Athena query editor]

AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.

The other options are not optimal for the following reasons:

B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.

C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .

D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .

1: AWS Data Exchange User Guide

2: AWS Data Exchange FAQs

3: AWS Glue Developer Guide

AWS CodeCommit User Guide

Amazon Kinesis Data Streams Developer Guide

Amazon Elastic Container Registry User Guide

Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source

The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead. Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:

Query editor V2

Working with materialized views

Scheduling queries

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]