Master the Amazon Web Services Data-Engineer-Associate Exam: Essential Study Tips and Strategies

 The error message indicates that the AWS Glue job cannot access the Amazon S3 bucket through the VPC endpoint. This could be because the VPC’s route table does not have the necessary routes to direct the traffic to the endpoint. To fix this, the data engineer must verify that the route table has an entry for the Amazon S3 service prefix (com.amazonaws.region.s3) with the target as the VPC endpoint ID. This will allow the AWS Glue job to use the VPC endpoint to access the S3 bucket without going through the internet or a NAT gateway. For more information, see Gateway endpoints. References:

• Troubleshoot the AWS Glue error “VPC S3 endpoint validation failed”
• Amazon VPC endpoints for Amazon S3
• [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

AWS Secrets Manager is a service that allows you to securely store and manage secrets, such as database credentials, API keys, passwords, etc. You can use Secrets Manager to encrypt, rotate, and audit your secrets, as well as to control access to them using fine-grained policies. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API).

Storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials will meet the requirements, as it will remediate the security vulnerability in the AWS Glue job and securely store the credentials. By using AWS Secrets Manager, you can avoid hard coding the credentials in the job script, which is a bad practice that exposes the credentials to unauthorized access or leakage. Instead, you can store the credentials as a secret in Secrets Manager and reference the secret name or ARN in the job script. You can also use Secrets Manager to encrypt thecredentials using AWS Key Management Service (AWS KMS), rotate the credentials automatically or on demand, and monitor the access to the credentials using AWS CloudTrail. By granting the AWS Glue job 1AM role access to the stored credentials, you can use the principle of least privilege to ensure that only the AWS Glue job can retrieve the credentials from Secrets Manager. You can also use resource-based or tag-based policies to further restrict the access to the credentials.

The other options are not as secure as storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials. Storing the credentials in the AWS Glue job parameters will not remediate the security vulnerability, as the job parameters are still visible in the AWS Glue console and API. Storing the credentials in a configuration file that is in an Amazon S3 bucket and accessing the credentials from the configuration file by using the AWS Glue job will not be as secure as using Secrets Manager, as the configuration file may not be encrypted or rotated, and the access to the file may not be audited or controlled. References:

• AWS Secrets Manager
• AWS Glue
• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.

The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations andcoordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. References:

• AWS Glue
• Amazon EventBridge
• Using AWS Glue to run ETL jobs against non-native JDBC data sources
• [AWS Lambda quotas]
• [Amazon Kinesis Data Firehose quotas]

AWS Database Migration Service (AWS DMS) is a service that helps you migrate databases to AWS quickly and securely. You can use AWS DMS to migrate the Hive metastore from the on-premises Hadoop clusters into Amazon S3, which is a highlyscalable, durable, and cost-effective object storage service. AWS Glue Data Catalog is a serverless, managed service that acts as a central metadata repository for your data assets. You can use AWS Glue Data Catalog to scan the Amazon S3 bucket that contains the migrated Hive metastore and create a data catalog that is compatible with Apache Hive and other AWS services. This solution meets the requirements of migrating the data catalog into a persistent storage solution and using a serverless solution. This solution is also the most cost-effective, as it does not incur any additional charges for running Amazon EMR or Amazon Aurora MySQL clusters. The other options are either not feasible or not optimal. Configuring a Hive metastore in Amazon EMR (option B) or an external Hive metastore in Amazon EMR (option C) would require running and maintaining Amazon EMR clusters, which would incur additional costs and complexity. Using Amazon Aurora MySQL to store the company’s data catalog (option C) would also incur additional costs and complexity, as well as introduce compatibility issues with Apache Hive. Configuring a new Hive metastore in Amazon EMR (option D) would not migrate the existing data catalog, but create a new one, which would result in data loss and inconsistency. References:

• Using AWS Database Migration Service
• Populating the AWS Glue Data Catalog
• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.2: AWS Glue Data Catalog

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.

References:

• 1: AWS Glue Data Catalog
• 2: AWS Glue Crawlers
• : Amazon Aurora
• : AWS Lambda
• : Amazon DynamoDB

Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.

Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns. S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.

Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However,Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.

Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage. Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.

References:

• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
• What Is AWS Lake Formation? - AWS Lake Formation
• Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
• Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation
• Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation
• Using Bucket Policies and User Policies - Amazon Simple Storage Service
• Apache Ranger
• Apache Pig
• What Is Amazon Redshift? - Amazon Redshift

 Using an S3 bucket that is in the same AWS Region where the company runs Athena queries can improve query performance by reducing data transfer latency and costs. Preprocessing the .csv data to Apache Parquet format can also improve query performance by enabling columnar storage, compression, and partitioning, which can reduce the amount of data scanned and fetched by the query. These solutions can optimize the storage solution for the POC test without requiring much effort or changes to the existing data pipeline. The other solutions are not optimal or relevant for this requirement. Adding a randomized string to the beginning of the keys in Amazon S3 can improve the throughput across partitions, but it can also make the data harder to query and manage. Using an S3 bucket that is in the same account that uses Athena to query the data does not have any significant impact on query performance, as long as the proper permissions are granted. Preprocessing the .csv data to JSON format does not offer any benefits over the .csv format, as both are row-based and verbose formats that require more data scanning and fetching than columnar formats like Parquet. References:

• Best Practices When Using Athena with AWS Glue
• Optimizing Amazon S3 Performance
• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost-effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance. To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.

The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewritesthe table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows. This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.

The other options are not optimal for the following reasons:

• A. VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.
• B. VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.
• D. VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

References:

• 1: Amazon Redshift VACUUM
• 2: Amazon Redshift Interleaved Sorting
• 3: Amazon Redshift ANALYZE

This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. References:

• Amazon Managed Service for Apache Flink
• Apache Flink
• Window Aggregations in Flink

 Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and AmazonRedshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. References:

• Amazon AppFlow
• Amazon AppFlow | SaaS Integrations List
• Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions