Question 670 of 1,711
Fixing Out of Memory Errors in AWS Glue Streaming
A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and writes Parquet to Amazon S3. Recently, the job started failing with 'Out of Memory' errors. Which change is MOST likely to resolve the issue?
Quick Answer
The correct answer is to increase the number of DPUs allocated to the Glue job. An out of memory error in AWS Glue streaming jobs occurs when the processing workload exceeds the available memory and compute capacity of the current worker configuration, causing the Spark executor to fail. By increasing the number of Data Processing Units (DPUs), you horizontally scale the job’s resources, directly providing more memory to handle the streaming data volume from Kinesis and the transformation overhead of converting JSON to Parquet. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Glue job resource tuning versus code-level fixes—a common trap is trying to optimize the script or reduce batch size when the real issue is insufficient DPUs. Remember the memory tip: “OOM? Boost the DPU count—more workers, more memory, no more errors.”
⚠ Common exam trap
Test-takers frequently confuse 'Out of Memory' with a data format or compression issue, leading them to choose options like A or B, when the real solution is to scale compute resources via DPUs.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of DPUs allocated to the Glue job.
The 'Out of Memory' error in AWS Glue indicates that the job's allocated resources are insufficient for the data volume or processing complexity. Increasing the number of DPUs (Data Processing Units) directly increases the available memory and compute capacity, which is the most straightforward fix for OOM errors in Glue streaming jobs. Option C is correct because it addresses the root cause—resource exhaustion—by scaling the job horizontally.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable compression on the Kinesis stream.
Why it's wrong here
Compression on Kinesis does not affect Glue memory.
- ✗
Change the output format from Parquet to ORC.
Why it's wrong here
ORC is similar to Parquet; format change does not fix OOM.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
More DPUs provide more memory and CPU.
- ✗
Reduce the streaming batch size in the Glue job configuration.
Why it's wrong here
Reducing batch size can help but does not address the root cause of insufficient memory.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Same concept, more angles
2 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError'. What is the MOST likely cause?
medium- ✓ A.The Glue job worker type is too small for the data volume
- B.The Glue job uses too many DynamicFrames
- C.The S3 output bucket is in a different region
- D.The Kinesis stream has insufficient shards
Why A: The 'MemoryError' in AWS Glue indicates that the worker type allocated to the job does not have sufficient memory to process the data volume. Glue workers (Standard, G.1X, G.2X) have fixed memory allocations (e.g., 16 GB for Standard), and if the streaming data from Kinesis exceeds this, the job fails. Increasing the worker type or the number of workers resolves this.
Variation 2. A company is using AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError' when the stream has a sudden spike in data volume. Which configuration change would best prevent this error?
medium- ✓ A.Increase the number of DPUs (Data Processing Units) for the Glue job.
- B.Store intermediate results in Amazon RDS.
- C.Use a batch transformation instead of streaming.
- D.Increase the number of shards in the Kinesis data stream.
Why A: Increasing the number of DPUs in the AWS Glue job provides more memory and compute capacity to handle data spikes. Option B is wrong because storing intermediate results in Amazon RDS does not prevent memory errors in Glue; it introduces a database dependency and does not increase Glue's memory. Option C is wrong because switching to batch transformation is not a solution for a streaming job; the job is designed for streaming and batch does not address the memory issue. Option D is wrong because increasing the number of shards in Kinesis increases throughput but does not directly solve memory errors in Glue; it may even increase the data volume per unit time and worsen the problem.
Last reviewed: Jun 11, 2026
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