Chapter 3 of 4 · AWS DEA-C01
Data Operations and Support
Automation, analysis with Athena and QuickSight, monitoring, troubleshooting and data quality.
- 6topics
- 28 minfast track
- 1diagrams and exhibits
- 22%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1Automating Data ProcessingAutomate data processing on AWS with Lambda event source mappings, EventBridge schedules, Glue triggers, the Redshift Data API…5 min
- 2Analyzing Data with AthenaUse Amazon Athena for serverless SQL on S3 with partitions, CTAS, views, workgroups and federated queries, and compare it with…4 min
- 3QuickSight and DataBrewPrepare data visually with Glue DataBrew and publish dashboards in Amazon QuickSight, covering SPICE, direct query, refresh and…Diagram5 min
- 4Monitoring Data PipelinesMonitor AWS data pipelines with CloudWatch metrics, alarms and logs, EventBridge events, SNS and CloudTrail, and learn which…4 min
- 5Troubleshooting Data PipelinesDiagnose common AWS data pipeline failures such as throttling, lag, skew, small files, permissions and network paths using logs…5 min
- 6Ensuring Data QualityDefine and enforce data quality on AWS with Glue Data Quality DQDL rules, DataBrew profiling, validation checks, quarantine and…5 min
About this chapter
What this chapter covers
This chapter is about running data systems after they are built. It covers automating the work, analyzing the results, watching pipelines and keeping the data trustworthy.
It has four outline topics: automating processing, analyzing data, maintaining and monitoring pipelines, and ensuring data quality. Analysis and monitoring each need more than one page, so six pages cover the four topics.
The chapter follows the third domain of the AWS Certified Data Engineer Associate DEA-C01 exam guide.
How the topics fit together
Operations is a loop. Automated pipelines produce data, people analyze it, monitoring watches the system, and quality checks protect the content.
- Automation uses Lambda event source mappings, EventBridge schedules and service APIs.
- Athena gives SQL on the data lake, and QuickSight and DataBrew prepare and present data.
- Monitoring uses CloudWatch metrics, logs, alarms, EventBridge events and CloudTrail.
- Troubleshooting turns a symptom into a cause using a fixed diagnostic order.
- Data quality adds rules, profiling and quarantine so bad data is caught early.
Many questions in this chapter give a symptom. Name the metric or log that proves the cause before choosing a fix.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with diagrams and examples.
- What to remember for the exam: the facts to memorise.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
The troubleshooting page works as a lookup table, so use it after you have read the service pages in the earlier chapters.
Ready for more practice?
The full AWS DEA-C01 question bank has 845 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by AWS. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.