Data processing is essential in determining the success and growth of any business entity. Correct interpretation of data into meaningful information leads to informed business decisions and strategic planning. Data processing play a critical role in any organizational setup, especially when it comes to ensuring repeatable business outcomes. It is for these reasons that individuals seeking to become AWS certified Data Engineers should focus on understanding how to maintain and troubleshoot data processing. This article will focus on this topic as it relates to the AWS Certified Data Engineer – Associate (DEA-C01) exam.

Table of Contents

Ingestion and Collection

Data ingestion is the first step in the data processing pipeline. In AWS, data may come from various sources such as transactional data from databases, data streams like AWS Kinesis, or big data from S3 or Hadoop. An AWS Certified Data Engineer should know how to handle different types of data ingestion and how to troubleshoot any issues that may arise during this stage.

For instance, when using AWS Kinesis to ingest streaming data, you may encounter a ProvisionedThroughputExceededException error message. This occurs when the rate of incoming data exceeds the provisioned throughput. A solution to avoid this would be to add more shards to your Kinesis data stream.

import boto3

kinesis = boto3.client('kinesis')

Transformation and Processing

Once the data is ingested, it is then transformed into a format that can be configured for analytical purposes. AWS provides several tools to handle this, like AWS Glue, AWS Data Pipeline, and AWS Lambda.

A potential issue that you might encounter during this stage comes as the Glue job timeout. AWS Glue jobs are configured to run for up to 48 hours by default, and if your Glue job exceeds this time, it will be stopped automatically. To troubleshoot this issue, you may consider partitioning your data or increasing the number of DPUs (Data Processing Units).

Remember, maintaining and improving your transformation and processing pipeline always helps in better data manipulation, leading to better business outcomes.

Storage and Analysis

Storing and analyzing data is the next step in the data processing pipeline. Using AWS, data can be stored in a variety of storage solutions such as Amazon Redshift for large datasets, Amazon RDS for relational database services, or Amazon S3 for object storage.

One common problem in this stage is slow query performance in Amazon Redshift. This could be due to lack of proper distribution of data across all the nodes, resulting in some nodes being overloaded. To troubleshoot this, you can redistribute your data using a suitable distribution style or resize your cluster to accommodate the load.


Finally, the processed and analyzed data needs to be visualized to be comprehended by business stakeholders. AWS provides tools like Amazon Quicksight for straightforward and efficient data visualization.

In terms of troubleshooting, one issue you might encounter is a delay in data refresh. This could result from an inadequate arrangement of SPICE capacity (The in-memory optimized calculation engine of Amazon Quicksight). By managing the SPICE capacity appropriately, you can solve this problem effectively.

As a certified AWS Data Engineer, you should have a thorough understanding of how to maintain and troubleshoot data processing for repeatable business outcomes. It is hoped this discussion has provided the requisite information and practical instances to guide you in optimization and management of data processing, and consequently improve the predictability of your business outcomes.

Practice Test

True or False: Setting alarms based on Amazon CloudWatch metrics is a good practice to maintain data processing systems and to troubleshoot problems.

  • Answer: True

Explanation: Amazon CloudWatch metrics help in monitoring AWS resources and applications in real-time. Setting alarms based on these metrics can alert you in cases of breach of thresholds or anomalies, aiding in quick troubleshooting.

Which of the following AWS services can be used to automate the data processing pipeline?

  • A. AWS Glue
  • B. AWS Lambda
  • C. AWS Batch
  • D. All of the above

Answer: D. All of the above

Explanation: AWS Glue, AWS Lambda, and AWS Batch all provide facilities for automating data processing. This allows more repeatable and reliable business outcomes.

True or False: It’s not important to audit data processing tasks.

  • Answer: False

Explanation: Auditing is essential as it monitors the performance of a data processing system and helps to detect any issues at an early stage.

Which of the following are common metrics to monitor for maintaining and troubleshooting data processing for repeatable business outcomes? (Select all that apply)

  • A. CPU usage
  • B. Network latency
  • C. Input/Output operations per second (IOPS)
  • D. Current AWS region

Answer: A. CPU usage, B. Network latency, C. Input/Output operations per second (IOPS)

Explanation: While current AWS region might be important for location-based services, it’s not relevant for general data processing tasks. All the others can directly impact processing efficiency and should be closely watched.

True or False: AWS Data Pipeline supports fault-tolerance through automatic reruns of failed tasks.

  • Answer: True

Explanation: AWS provides built-in fault tolerance with AWS Data Pipeline by automatically rerunning failed tasks.

Which AWS tool is best suited for visual debugging?

  • A. AWS Glue
  • B. AWS X-Ray
  • C. AWS Lambda
  • D. AWS Batch

Answer: B. AWS X-Ray

Explanation: AWS X-Ray provides an end-to-end view of requests as they travel through your application and shows a map of your application’s underlying components.

As an AWS Data Engineer, what practice would help ensure repeatable business outcomes?

  • A. Automated Backups
  • B. Manual Intervention in Jobs
  • C. Skipping Data Validation
  • D. Ignoring Errors

Answer: A: Automated Backups

Explanation: Regular and automated backups ensure data recovery in case of system failures. It leads to more secure and reliable data processing.

True or False: To maintain a data processing system efficiently, one should avoid version control.

  • Answer: False

Explanation: Version control is important because it helps track changes and you can always go back to a stable version in case of unforeseen issues.

Which AWS service is primarily used to monitor applications and resources?

  • A. AWS X-Ray
  • B. AWS Inspector
  • C. Amazon CloudWatch
  • D. Amazon Route 53

Answer: C: Amazon CloudWatch

Explanation: Amazon CloudWatch is designed to monitor applications, collect and track metrics, collect and monitor log files, and respond to system-wide performance changes.

What Amazon service can be used to monitor network performance?

  • A. Amazon Inspector
  • B. Amazon VPC Flow Logs
  • C. Amazon GuardDuty
  • D. AWS Batch

Answer: B. Amazon VPC Flow Logs

Explanation: Amazon VPC Flow Logs captures information about the IP traffic to and from network interfaces in your VPC, helping you to diagnose and troubleshoot network performance issues.

Interview Questions

What is the primary factor to consider when maintaining data processing for repeatable business outcomes?

The primary factor is to ensure regular system monitoring and proactive management, which allows for the early detection of potential disruptions and their prompt resolution.

What Amazon tool would you recommend for troubleshooting and automating data workflows?

The recommended tool is AWS Step Functions. It lets you coordinate multiple AWS services into serverless workflows so you can build and update applications quickly and troubleshoot any challenges.

Which AWS service enables real-time operational insights to monitor, operate, and scale data processing tasks?

Amazon CloudWatch offers these features, providing a list of all running applications, detailed monitoring metrics and the ability to set alarms on specific behaviors or occurrences.

Can Amazon CloudWatch be integrated with AWS Lambda for maintaining and troubleshooting data processing?

Yes, AWS Lambda can be integrated with Amazon CloudWatch to automatically respond to changes in AWS resources.

What strategy can a data engineer employ to reduce the amount of consumed read/write capacity in DynamoDB?

The data engineer can enable DynamoDB auto scaling to maintain optimal performance and keep costs down.

In the context of maintaining and troubleshooting data, is it possible to reprocess an Amazon Kinesis data stream from a certain point in the past?

No, the Kenesis streams retain data only for a 24-hour period. For historical data capture and reprocessing, consider using Amazon Kinesis Data Firehose coupled with an S3 data lake.

How would one ensure high availability of Amazon Redshift for data processing?

The ideal approach would be to enable automatic backups and have Amazon Redshift configured to automatically replicate the data within the cluster to other nodes.

What can you do if a particular Amazon Redshift query is performing poorly?

The Query Execution Breakdown feature can be used to analyze queries and identify performance issues.

Can you use Amazon Athena to troubleshoot data in S3?

Yes, Amazon Athena can query data directly in S3, helping you quickly identify issues or anomalies in raw data.

How can you ensure the durability of data in S3 for data processing pipelines?

Enabling S3 Versioning to keep multiple variants of an object in the same bucket can ensure data durability.

How can AWS Glue aid in maintaining and troubleshooting data processing tasks for repeatable business outcomes?

AWS Glue’s Data Catalog, data preparation, data transformation, and job scheduling capabilities can greatly simplify the otherwise time-consuming tasks in data processing.

Which AWS service can help monitor the application logs for applications hosted in EC2 instances?

Amazon CloudWatch Logs can be used to monitor, store, and access log files.

How can you minimize the impact of a failure in an AWS data pipeline?

Through the use of Multi-AZ deployments in the data pipeline to ensure an automatic failover to a standby database in case of an infrastructure failure.

What is the role of Amazon Kinesis Data Streams in maintaining and troubleshooting data processing tasks?

Amazon Kinesis Data Streams enables streaming and analyzing data in real-time, which is vital in providing rapid feedback and actionable insights, thus aiding in maintaining and troubleshooting data processing tasks.

How can a Data Engineer monitor application health to ensure data processing is not hindered due to any failures?

A Data Engineer can use AWS CloudTrail to log, continuously monitor, and retain account activity related to actions across AWS infrastructure, reducing potential disruptions to data processing.

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