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Questions and Answers

Which solution will meet the requirements for synchronizing the AWS Glue Data Catalog with S3 storage with the least latency?

Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call. (correct)

Manually run the AWS Glue CreatePartition API twice each day.

Run the MSCK REPAIR TABLE command from the AWS Glue console.

Schedule an AWS Glue crawler to run every morning.

Which AWS service or feature will meet the requirements for storing data in an Amazon S3 bucket with the least operational overhead?

Amazon Kinesis

AWS Glue Data Catalog

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Amazon AppFlow (correct)

How should the data engineer modify the Athena query to retrieve sales amounts for all products?

Remove the GROUP BY clause.

Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.

Replace sum(sales_amount) with count(*) for the aggregation.

Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023. (correct)

Which solution will meet the requirement to query one column of data in Apache Parquet format with the least operational overhead?

Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects. Signup and view all the answers

Which solution will automate refresh schedules for Amazon Redshift materialized views with the least effort?

Use the query editor v2 in Amazon Redshift to refresh the materialized views. Signup and view all the answers

What is the correct IAM role to associate with the crawler for processing data?

Create an IAM role that includes the AWSGlueServiceRole policy. Signup and view all the answers

How should a data engineer invoke the Lambda function to write load statuses to DynamoDB?

Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Signup and view all the answers

Which AWS service should be used to transfer 5 TB of data from on-premises to S3 in the most operationally efficient way?

AWS DataSync Signup and view all the answers

Which cost-effective solution should a company use to migrate data to AWS with minimal downtime?

AWS Database Migration Service (AWS DMS) Signup and view all the answers

What tasks can a data engineer use to process data from RDS and MongoDB into Amazon Redshift?

Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. Signup and view all the answers

What is necessary to turn on concurrency scaling for an Amazon Redshift cluster?

Turn on concurrency scaling at the WLM queue level in the Redshift cluster. Signup and view all the answers

How can a data engineer orchestrate Amazon Athena queries that run every day?

Create an AWS Step Functions workflow and add two states. Signup and view all the answers

What ETL service can effectively support the migration of on-premises workloads to AWS?

Amazon EMR Signup and view all the answers

Which solution minimizes operational effort when profiling and obfuscating PII data?

Use the Detect PII transform in AWS Glue Studio. Signup and view all the answers

What is the recommended solution for automating existing ETL workflows in AWS?

AWS Step Functions tasks Signup and view all the answers

How should an S3 Lifecycle policy be set for data access patterns identified over time?

Transition objects to S3 Standard-IA after 6 months and Glacier Flexible Retrieval after 2 years. Signup and view all the answers

What is the purpose of the Amazon Redshift Data API?

To enable programmatic interactions with Redshift for data queries and management. Signup and view all the answers

What is the goal of AWS Glue Data Quality?

To ensure the accuracy and integrity of data throughout the ETL process. Signup and view all the answers

Which solutions will resolve the performance bottleneck and reduce Athena query planning time? (Choose two.)

Create an AWS Glue partition index. Enable partition filtering. Signup and view all the answers

Which solution will meet the requirements for real-time analytics with the least operational overhead?

Use Amazon Managed Service for Apache Flink to perform time-based analytics. Signup and view all the answers

What is the least operational overhead solution for upgrading Amazon EBS from gp2 to gp3?

Change the volume type of existing gp2 volumes to gp3 and enter new values. Signup and view all the answers

Which solution is most operationally efficient for migrating database servers to Amazon RDS and exporting large data elements in Parquet format?

Create a view and run an AWS Glue crawler, then transfer data to S3 in Parquet format. Signup and view all the answers

Which system table should a data engineer use in Amazon Redshift to record anomalies caused by the query optimizer?

STL_ALERT_EVENT_LOG Signup and view all the answers

What is the most cost-effective solution for ingesting structured .csv data into an Amazon S3 data lake?

Create an ETL job to write the data in Apache Parquet format. Signup and view all the answers

What combination of steps should the data engineering team take to limit HR department access to employee records?

Register the S3 path as an AWS Lake Formation location. Signup and view all the answers

What is the correct combination of steps to identify why Step Functions state machine cannot run EMR jobs?

Verify IAM permissions for both Step Functions and required S3 access. Signup and view all the answers

Which solution will preserve temporary application data generated by EC2 instances when they are terminated?

Launch new instances using an AMI backed by an instance store volume and attach an EBS volume. Signup and view all the answers

Which AWS resource allows the use of Apache Spark to access Athena?

Athena workgroup Signup and view all the answers

Which solution will meet the requirement to connect the AWS Glue job to the S3 bucket after encountering an error with the Amazon S3 VPC gateway endpoint?

Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket. Signup and view all the answers

Which solution will ensure that data analysts can access data only for customers within the same country as the analysts with the least operational effort?

Register the S3 bucket as a data lake location in AWS Lake Formation. Signup and view all the answers

What solution will incorporate insights from third-party datasets into an analytics platform with the least operational overhead?

Use API calls to access and integrate third-party datasets from AWS Data Exchange. Signup and view all the answers

Which combination of AWS services will implement a data mesh supporting centralized governance and data analysis?

Use Amazon S3 for data storage and Amazon Athena for data analysis. Signup and view all the answers

What is the most efficient solution for a data engineer to update all AWS Lambda functions using custom Python scripts?

Package the custom Python scripts into Lambda layers and apply those layers to the functions. Signup and view all the answers

Which AWS service or feature will most cost-effectively orchestrate an ETL pipeline using AWS Glue?

AWS Glue workflows Signup and view all the answers

Which solution will allow real-time queries on financial data stored in Amazon Redshift with the least operational overhead?

Use the Amazon Redshift Data API. Signup and view all the answers

What solution will implement permission controls for different query processes in Amazon Athena?

Create an Athena workgroup for each use case and apply IAM policies using tags. Signup and view all the answers

What is the most cost-effective way to schedule AWS Glue jobs which do not need to run at a specific time?

Choose the FLEX execution class in the Glue job properties. Signup and view all the answers

What solution will convert data from .csv to Apache Parquet format using AWS Lambda with minimal operational overhead?

Create an S3 event notification for s3:ObjectCreated:* with a notification filter for .csv. Signup and view all the answers

How can a data engineer speed up Athena query performance with uncompressed .csv files?

Change data format from .csv to Apache Parquet and apply Snappy compression. Signup and view all the answers

Which solution will allow a manufacturing company to display real-time operational efficiency data with the lowest latency?

Use Amazon Managed Service for Apache Flink to process data and create a Grafana dashboard. Signup and view all the answers

Which solution will meet these requirements? A. Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing. B. Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster. C. Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster. D. Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week.

Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing. Signup and view all the answers

Which solution will meet this requirement MOST cost-effectively? A. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis. B. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files. C. Use Amazon Athena Federated Query to join the data from all data sources. D. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Use Amazon Athena Federated Query to join the data from all data sources. Signup and view all the answers

Which combination of resources will meet the requirements MOST cost-effectively? A. Use Hadoop Distributed File System (HDFS) as a persistent data store. B. Use Amazon S3 as a persistent data store. C. Use x86-based instances for core nodes and task nodes. D. Use Graviton instances for core nodes and task nodes. E. Use Spot Instances for all primary nodes.

Use Amazon S3 as a persistent data store. Signup and view all the answers

Which solution will meet these requirements with the LEAST operational overhead? A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift. B. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. C. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3.

Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Signup and view all the answers

Which actions should the data engineer take to improve the performance of the AWS Glue jobs? A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day. B. Increase the AWS Glue instance size by scaling up the worker type. C. Convert the AWS Glue schema to the DynamicFrame schema class. D. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.

Increase the AWS Glue instance size by scaling up the worker type. Signup and view all the answers

Which Step Functions state should the data engineer use to meet these requirements? A. Parallel state B. Choice state C. Map state D. Wait state

Map state Signup and view all the answers

Which solution will meet these requirements with the LEAST operational overhead? A. Write a custom extract, transform, and load (ETL) job in Python. B. Write an AWS Glue extract, transform, and load (ETL) job. C. Write a custom ETL job in Python using the dedupe library. D. Write an AWS Glue ETL job using the Python dedupe library.

Write an AWS Glue extract, transform, and load (ETL) job. Signup and view all the answers

Which actions will provide the FASTEST queries? A. Use gzip compression to compress individual files. B. Use a columnar storage file format. C. Partition the data based on the most common query predicates. D. Split the data into files that are less than 10 KB.

Use a columnar storage file format. Signup and view all the answers

Which combination of steps will meet this requirement with the LEAST operational overhead? A. Turn on the public access setting for the DB instance. B. Update the security group of the DB instance to allow only Lambda function invocations on the database port. C. Configure the Lambda function to run in the same subnet that the DB instance uses. D. Attach the same security group to the Lambda function and the DB instance.

Attach the same security group to the Lambda function and the DB instance. Signup and view all the answers

Which solution will meet these requirements? A. Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster. B. Create an AWS Lambda Python function with provisioned concurrency. C. Deploy a custom Python script on Amazon Elastic Kubernetes Service (Amazon EKS). D. Create an AWS Lambda function.

Create an AWS Lambda Python function with provisioned concurrency. Signup and view all the answers

Which solution will capture the changed data MOST cost-effectively? A. Create an AWS Lambda function to identify the changes. B. Ingest the data into Amazon RDS for MySQL. C. Use an open-source data lake format to merge the data source with the S3 data lake. D. Ingest the data into an Amazon Aurora MySQL DB instance.

Use an open-source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data. Signup and view all the answers

Study Notes

Troubleshooting AWS Glue Jobs

An error message indicating problems with the Amazon S3 VPC gateway endpoint can be resolved by verifying that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint

Data Governance and Access Control

A retail company can ensure that data analysts only access customer data from their country by using AWS Lake Formation, registering their S3 bucket as a data lake location, and implementing row-level security features.

Third-Party Data Integration

A media company can minimize the effort and time required to integrate third-party datasets into their analytics platform by using AWS Data Exchange and API calls.

Data Mesh Implementation

A financial company implementing a data mesh should use AWS Glue for data catalogs, S3 for data storage, and Athena for data analysis.

Updating Lambda Functions with Custom Scripts

A data engineer can update multiple Lambda functions with custom Python scripts by packaging them into Lambda layers and applying those layers to the functions.

ETL Data Pipeline Orchestration

A company using a data pipeline in AWS Glue can extract, transform, and load data from a Microsoft SQL Server table to an S3 bucket using AWS Glue workflows.

Real-Time Queries on Amazon Redshift Data

A financial services company can run real-time queries on Amazon Redshift data from a web-based trading application using the Amazon Redshift Data API.

Query History Access Control

A company using Amazon Athena can implement permission controls for query access history by creating an Athena workgroup for each use case, applying tags to the workgroup, and establishing an IAM policy that uses the tags to apply permissions.

Cost-Effective Scheduling of AWS Glue Jobs

Scheduling a workflow that runs AWS Glue jobs every day can be done in a cost-effective way by choosing the FLEX execution class in the Glue job properties.

Triggering Lambda Functions with S3 File Uploads.

An AWS Lambda function to convert CSV files to Apache Parquet can be triggered automatically when a user uploads a CSV file to a specific S3 bucket through S3 event notifications with an event type of s3:ObjectCreated:* and a filter rule for the suffix ".csv".

Optimizing Query Performance with Data Format and Compression

To speed up the Athena query performance, it's advised to change the data format from .csv to Apache Parquet and use Snappy compression.

Real-Time Data Visualization

A company collecting sensor data and publishing to Amazon Kinesis Data Streams can display a real-time view of operational efficiency on a large screen using Amazon Managed Service for Apache Flink to process the data, write it to an Amazon Timestream database, and use Grafana to build a dashboard.

Automating Data Catalog Updates

A data engineer can automate daily updates to the AWS Glue Data Catalog for data stored in an S3 bucket by using AWS Glue crawlers, associating the crawler with an IAM role that includes the AWSGlueServiceRole policy, and configuring a daily schedule to run the crawler.

Question #14

Scenario: A company loads transaction data into Amazon Redshift tables daily.
Objective: Track which tables have been loaded and which haven't.
Solution: The data engineer should use Amazon EventBridge to publish load status details to DynamoDB via an AWS Lambda function.
Why: This approach leverages Redshift Data API and EventBridge for efficient, scalable event-driven architecture.

Question #15

Scenario: A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket, with 5% daily data changes.
Objective: Automate data transfer with regular updates, handling multiple file formats.
Solution: AWS DataSync is the optimal choice for this scenario.
Why: DataSync offers reliable, automated data transfer, handles various formats, and ensures efficient updates.

Question #16

Scenario: A company migrates financial transaction data from an on-premises Microsoft SQL Server database to AWS at the end of each month.
Objective: Find a cost-effective and low-impact solution for data migration.
Solution: AWS Database Migration Service (DMS) is the most suitable service.
Why: DMS provides cost-efficient and efficient data migration from on-premises databases to AWS, minimizing downtime.

Question #17

Scenario: A data engineer is building a data pipeline on AWS using AWS Glue ETL jobs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift.
Objective: Minimize operational overhead for hourly data updates.
Solutions:
- Configure AWS Glue triggers to run the ETL jobs every hour.
- Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
Why: Glue triggers automate ETL execution, while Glue connections streamline data flow, minimizing operational burden.

Question #18

Scenario: A company uses an Amazon Redshift cluster running on RA3 nodes, aiming to scale read and write capacity to meet demand.
Objective: Enable concurrency scaling in the Redshift cluster.
Solution: Turn on concurrency scaling at the workload management (WLM) queue level within the Redshift cluster.
Why: Concurrency scaling at the WLM queue level allows for efficient scaling of both read and write operations.

Question #19

Scenario: A data engineer needs to orchestrate daily execution of a series of Amazon Athena queries, some running beyond 15 minutes.
Objective: Cost-effectively manage the orchestration of these long-running Athena queries.
Solutions:
- Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
- Create an AWS Step Functions workflow with states to periodically check if the Athena query is finished using the Athena Boto3 get_query_execution API call.
Why: Lambda offers flexible execution, while Step Functions ensures orchestration and query status checks.

Question #20

Scenario: A company migrating on-premises workloads to AWS using Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink.
Objective: Reduce operational overhead, explore serverless options, and maintain or improve data processing performance.
Solution: Amazon EMR is the best fit for this migration.
Why: Amazon EMR provides a managed Hadoop framework, enabling efficient processing of petabytes of data and offering a scalable serverless solution.

Question #21

Scenario: A data engineer ingests a dataset containing PII into an Amazon S3 data lake.
Objective: Profile the dataset, identify PII, and obfuscate it with minimal operational effort.
Solution: Use the Detect PII transform in AWS Glue Studio to identify PII and obfuscate it.
Why: AWS Glue Studio's Detect PII transform offers simple and efficient PII identification and obfuscation for data lake ingestion, minimizing operational overhead.

Question #22

Scenario: A company utilizes multiple ETL workflows using AWS Glue and Amazon EMR to ingest data into an S3-based data lake.
Objective: Improve the architecture for automated orchestration and minimize manual effort.
Solution: AWS Step Functions tasks deliver the best solution for this.
Why: Step Functions provides a robust and flexible framework for orchestrating ETL workflows, minimizing manual effort and increasing automation.

Question #23

Scenario: A company stores all its data in Amazon S3 using the S3 Standard storage class.
Objective: Implement S3 Lifecycle policies for cost-effective storage optimization, maintaining high availability.
Solution: Use the following storage class transitions:
- Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months.
- Transfer objects to S3 Glacier Deep Archive after 2 years.
Why: This strategy provides a cost-effective approach, balancing data accessibility with storage cost savings.

Question #24

Scenario: A company uses separate Amazon Redshift clusters for ETL operations and business intelligence (BI), with the sales team requiring access to ETL data.
Objective: Share data from the ETL cluster with the sales team without impacting critical analysis tasks and minimizing resource usage.
Solution: Set up the sales team BI cluster as a consumer of the ETL cluster using Redshift data sharing.
Why: Redshift data sharing allows seamless and secure access to data across clusters, without affecting performance, and minimizes data replication.

Question #25

Scenario: A data engineer needs to perform a one-time analysis by joining data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Objective: Complete the one-time analysis job most cost-effectively.
Solution: Use Amazon Athena Federated Query to join data from all data sources.
Why: Athena Federated Query enables cost-effective, ad-hoc querying across multiple AWS data sources without needing to move data, making it ideal for one-time analyses.

Question #26

Scenario: A company uses a provisioned Amazon EMR cluster to perform big data analysis using Apache Spark jobs, requiring high reliability and cost optimization.
Objective: Maintain performance while optimizing costs for long-running workloads on Amazon EMR.
Solutions:
- Use Amazon S3 as a persistent data store.
- Use Graviton instances for core nodes and task nodes.
Why: Amazon S3 provides a reliable and cost-effective data store, while Graviton instances offer a balance of performance and cost efficiency for long-running workloads.

Data Ingestion and Processing

A company wants real-time analytics using Kinesis Data Streams and Redshift.
Using Kinesis Data Firehose to stage data in S3 and then load it into Redshift with the COPY command offers the lowest operational overhead.
Redshift Spectrum can query data stored in S3 for fast analytics.
Using a columnar storage format and partitioning data based on common query predicates optimizes query speeds.

AWS Glue Job Optimization

Slowing AWS Glue job performance can be addressed by partitioning data in S3 and increasing the Glue instance size.

Step Functions for Parallel Processing

The Map state in Step Functions allows parallel processing of data files, applying specific transformations to each file.

Data Deduplication

AWS Glue with the FindMatches machine learning transform effectively identifies and removes duplicate information from legacy application data, minimizing operational overhead.

Accessing RDS from Lambda

To access a private RDS instance from a Lambda function, configure the Lambda to run in the same subnet as the RDS instance and attach the same security group to both, allowing access through the database port.

Efficient Script Execution with API Gateway

Creating an AWS Lambda Python function with provisioned concurrency is the most efficient way to execute a Python script periodically via API Gateway.

CloudWatch Logs Delivery to Separate AWS Account

To deliver CloudWatch Logs from a production AWS account to a security account, create a destination stream in the security account.
Grant CloudWatch Logs permission to put data into the stream using an IAM role and trust policy.
Configure a subscription filter in the production account.

Cost-Effective Change Data Capture

Using an open source data lake format to merge data source with S3 data lake ensures cost-effective change data capture.

Optimizing Athena Query Performance

Creating an AWS Glue partition index and enabling partition filtering can reduce Athena query planning time when dealing with large numbers of partitions.

Real-Time Streaming Analytics

Amazon Managed Service for Apache Flink (formerly known as Amazon Kinesis Data Analytics) provides a fault-tolerant solution for real-time streaming analytics, performing time-based aggregations over windows up to 30 minutes.

Upgrading EBS Storage

Modifying the volume type of existing gp2 volumes to gp3 while adjusting volume size, IOPS, and throughput directly provides the least operational overhead for upgrading EBS storage without interrupting EC2 instances.

Efficient Data Export During Migration

Using AWS Glue to export large data elements in Apache Parquet format to S3 during a database migration from EC2 to RDS for Microsoft SQL Server provides an efficient and operationally effective solution.

SQL Server Data Transfer to S3

Objective: Transfer data from an EC2 instance-based SQL Server database to an S3 bucket.
Solution: Create a view in SQL Server and use an AWS Glue job to retrieve the view's data and store it in Parquet format in the S3 bucket.
Scheduling: Schedule the AWS Glue job to run daily.

Amazon Redshift Query Performance Monitoring

Objective: Monitor query performance for potential issues in an Amazon Redshift data warehouse.
Solution: Use the STL_ALERT_EVENT_LOG system table to record anomalies when the query optimizer detects performance issues.

Ingesting CSV Data into an S3 Data Lake

Objective: Ingest structured CSV data into an S3 data lake for efficient access by Amazon Athena.
Solution: Create an AWS Glue ETL job to read the CSV data and write it in Apache Parquet format into the S3 data lake.
Rationale: Parquet format minimizes data retrieval for commonly queried columns, improving cost-effectiveness.

Limiting Access to Employee Records in an S3 Data Lake

Objective: Restrict access to employee records in an S3 data lake based on HR department's location.
Solution:
- Register the S3 path as an AWS Lake Formation location.
- Enable fine-grained access control in AWS Lake Formation and add data filters for each location.

Troubleshooting AWS Step Functions and EMR Jobs

Objective: Identify why an AWS Step Functions state machine cannot run EMR jobs.
Solution:
- Verify the Step Functions state machine has the necessary IAM permissions to create and run EMR jobs and access the required S3 buckets.
- Check the VPC flow logs to ensure traffic from the EMR cluster can access the data providers and check for any security group restrictions.

Data Persistence with EC2 Instances

Objective: Ensure data persistence for an application running on EC2 instances even if the EC2 instances are terminated.
Solution: Launch new EC2 instances using an AMI that is backed by an EC2 instance store volume. Attach an Amazon EBS volume to the instance to store the application data.

Using Apache Spark with Amazon Athena

Objective: Allow Apache Spark to access data stored in Amazon Athena.
Solution: Use Athena workgroups to enable Apache Spark access to Athena data.

Synchronizing AWS Glue Data Catalog with S3 Partitions

Objective: Ensure the AWS Glue Data Catalog synchronizes with S3 storage when new partitions are added.
Solution: Use code that writes data to S3 to call the Boto3 AWS Glue create_partition API.

Data Ingestion from SaaS Applications

Objective: Ingest data from third-party SaaS applications into an S3 bucket for analysis using Amazon Redshift.
Solution: Use Amazon AppFlow to transfer data from SaaS applications to the S3 bucket.

Troubleshooting Athena Queries

Objective: Resolve issues retrieving data from an Athena table using a query.
Solution: When working with date fields, use extract(year FROM sales_data) = 2023 instead of year = 2023 to correctly retrieve data.

Querying Single Column in Parquet Data

Objective: Query a single column from data stored in Apache Parquet format in an S3 bucket.
Solution: Use S3 Select with a SQL SELECT statement to retrieve the desired column from the S3 objects with minimal operational overhead.

Automating Amazon Redshift Materialized View Refresh

Objective: Automate refresh schedules for materialized views in Amazon Redshift.
Solution: Use the query editor v2 in Amazon Redshift to refresh the materialized views.

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