Data Operations and Support

A data engineering team runs a nightly reporting pipeline that executes several Amazon Athena queries orchestrated by AWS Step Functions. Recently, some queries intermittently fail, and the team needs to programmatically detect which specific queries failed and capture the error reason so they can automatically retry or alert. What is the MOST effective way to identify the exact failure cause for each Athena query execution?

A data engineering team runs ad hoc Athena queries against a large S3 data lake. Finance reports that Athena costs have spiked because analysts frequently run SELECT * queries that scan entire multi-terabyte tables. The team wants to enforce a hard limit on how much data any single query can scan, and automatically cancel queries that exceed that limit, without modifying the underlying tables or query patterns. Which approach meets this requirement with the least operational effort?

A data engineer maintains a nightly Athena CREATE TABLE AS SELECT (CTAS) job that transforms raw event logs into a curated Parquet dataset in an S3 prefix, registered as a table in the Glue Data Catalog. The job is orchestrated by Step Functions. Occasionally the workflow retries a failed step, and the engineer notices that the CTAS statement fails on rerun with an error indicating the target location already exists. The team wants the pipeline to be safely re-runnable without manual cleanup. Which approach best resolves this while keeping the transformation logic in Athena?

A data engineer manages an Athena-based reporting pipeline that queries a QuickSight dashboard backed by S3 data in Parquet format. New data arrives hourly into date-partitioned S3 prefixes (s3://logs/year=/month=/day=/hour=/). Analysts complain that queries frequently miss the most recent hour of data even though the files exist in S3. The team currently runs a Glue crawler once per day to update partitions. The engineer needs the most operationally efficient way to ensure Athena always recognizes newly arrived partitions without manual intervention or frequent crawler runs. What should the engineer do?

A data engineering team runs ad-hoc analytics on a data lake using Amazon Athena. Analysts have complained about unexpectedly high monthly Athena costs. The team needs to identify which specific queries and users are scanning the most data so they can take corrective action, without deploying additional infrastructure. What is the MOST operationally efficient way to gain this visibility?

A data engineering team maintains a daily reporting pipeline. A Glue job writes curated Parquet data to Amazon S3 each morning at 06:00 UTC, and a Glue crawler updates the Data Catalog afterward. Business analysts use an Amazon QuickSight dashboard backed by a SPICE dataset built from an Athena query. Analysts report that the dashboard shows yesterday's numbers even though the morning pipeline completed successfully. The team wants the dashboard to reflect the latest data automatically each day with minimal ongoing management. What should the team do?

A data engineering team runs a nightly AWS Glue ETL job that populates a reporting table. Recently, the job has intermittently failed, but the team only discovers the failures the next morning when business users complain about missing dashboard data. The team wants to be notified automatically within minutes whenever the Glue job enters a failed state, with the least operational overhead. Which approach should they implement?

A data engineering team runs a nightly pipeline that has three sequential stages: a Glue ingestion job, a Glue transform job, and a Redshift COPY load. Each stage currently has its own CloudWatch alarm that sends an SNS notification on failure. On-call engineers complain that a single upstream failure produces a cascade of three separate pages, because when the ingestion job fails the downstream stages also alarm. The team wants a single actionable notification that fires only when the pipeline as a whole is in a failed state, while keeping the individual alarms for dashboards. What is the MOST operationally efficient way to achieve this?

A data engineering team runs a nightly Glue ETL pipeline orchestrated by AWS Step Functions. Business rules require that after the transformation step completes and Glue Data Quality checks pass, a data steward must manually review a summary report before the curated data is published to the production S3 prefix that feeds Athena and QuickSight. The team wants the workflow to pause and wait for the steward's explicit approval, then continue automatically, without polling or wasting compute. What is the MOST appropriate way to implement this pause-and-approve behavior?

A data engineering team runs a nightly AWS Glue ETL job that normally completes in about 25 minutes. Over the past two weeks, the job's runtime has gradually crept up to over 90 minutes, though it still succeeds. Leadership wants proactive alerting when future jobs deviate significantly from their historical baseline, without hard-coding a fixed threshold that must be manually adjusted as data volume grows. Which approach best meets this requirement?

A data engineering team runs a nightly ingestion pipeline. A custom application writes structured log lines to Amazon CloudWatch Logs, including a line formatted as "REJECTED_RECORDS count=<n>" whenever malformed records are skipped. The team wants to be automatically alerted whenever the number of rejected records exceeds 500 in any single run, without modifying the application or building a separate log-processing service. What is the most operationally efficient way to achieve this?

A data engineering team runs a nightly AWS Glue ETL job triggered by an Amazon EventBridge cron rule at 02:00 UTC. Due to a multi-hour AWS maintenance window in a downstream dependency, the source data was not available for three consecutive nights, so the job ran but produced empty output files that were written to the curated S3 zone. Analysts querying the data with Athena now see gaps and empty partitions. The team wants to prevent the pipeline from writing output when the expected upstream data has not landed, while keeping the automated schedule. Which approach BEST addresses this requirement?

A data engineering team maintains an inventory system backed by a DynamoDB table. Whenever an item's stock level changes, they must trigger a downstream data enrichment process running as an AWS Lambda function, while also optionally filtering out changes that are not stock-related to avoid unnecessary invocations. The solution should be event-driven, require minimal custom code, and support built-in filtering without polling logic. Which approach best meets these requirements?

A data engineering team runs 40 independent Glue ETL jobs that must each start at specific times throughout the day. They currently manage schedules using cron expressions in individual Glue triggers, but they want a single, centralized service to manage all schedules, support one-time and recurring runs, apply flexible time windows, and automatically retry failed invocations with a dead-letter queue for troubleshooting. Which approach best meets these requirements with the least operational overhead?

A data engineering team recently onboarded a new upstream vendor feed that lands as CSV files in Amazon S3 and is processed by an AWS Glue ETL job. Because the vendor's schema and value ranges are not well documented, the team wants a fast way to establish an initial baseline set of data quality checks (such as completeness, uniqueness, and column value ranges) without hand-authoring every rule from scratch. Which approach requires the least manual effort to produce a starting DQDL ruleset?

A data engineering team runs a nightly AWS Glue ETL job that loads customer transaction data into an S3-based data lake. Recently, downstream analysts reported that some batches contained duplicate transaction IDs and null values in the required 'amount' column. The team wants to automatically validate incoming data against defined rules (such as uniqueness and completeness) and halt the pipeline before publishing bad data, while capturing quality scores in CloudWatch for monitoring. Which approach best meets these requirements with the least custom code?

A data engineering team runs a daily AWS Glue ETL job that includes an AWS Glue Data Quality ruleset. Business stakeholders want an ongoing, automated way to observe whether the overall data quality of the pipeline is degrading over several weeks, without failing the job or blocking downstream processing. They want to visualize the trend and receive a notification only when quality drops below a defined threshold. Which approach best meets these requirements?

A data engineering team runs a daily AWS Glue ETL job that ingests data from a third-party REST API. Roughly once a week the job fails because the API returns an intermittent HTTP 503 error during the first few minutes of execution; re-running the job manually always succeeds. The team wants to reduce operational overhead and recover automatically from these transient failures without rewriting the job logic. What is the MOST operationally efficient solution?

A data engineer runs a Glue Spark ETL job that processes a few large, unsplittable gzip files. The job takes several hours and often finishes with the maximum number of DPUs allocated, yet costs are high. In the Glue job metrics (CloudWatch), the engineer observes that only one or two executors show high CPU while glue.driver.aggregate.numCompletedStages stays low and most executors show near-zero activity for long stretches. What is the MOST likely cause and the best corrective action?

A data engineering team schedules an AWS Glue ETL job to run every 15 minutes to process incoming files. During heavy traffic periods, upstream events sometimes trigger the job while a previous run is still executing. The team notices that some job runs fail immediately with a 'ConcurrentRunsExceededException' error, and downstream data is occasionally incomplete. They want to allow overlapping runs when needed but prevent runaway parallelism, without rewriting the pipeline logic. What is the MOST appropriate action?