Amazon EMR
Reviews
Star rating
Rank in Big Data Processing and Distribution
Pros and cons
Pros
- Ease of Use
- Large Datasets
- Data Integration
Cons
- Performance Issues
- Poor Performance
- Slow Performance
Traffic & LLM citations
Source: G2 review data
Winning from
Losing to
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 21.4% |
| Mid-Market | 20.0% |
| Enterprise | 58.6% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|---|---|
| 2024 Q4 | 1 | 4.5 |
| 2026 Q1 | 1 | 5.0 |
| 2026 Q2 | 5 | 4.6 |
Satisfaction ratings
What reviewers say
AWS EMR: Efficient, Auto-Scaling Big Data Processing with Spark and ETL
“I currently use Amazon EMR to run Spark ETL workloads and orchestrate large-scale data processing pipelines. EMR helps me execute jobs, optimize Spark performance, and analyze execution time. The best part is that it seamlessly integrates with S3 and Airflow, which I like the mos”
Tackles Big Data Effortlessly But Needs Better Cost Management
“What I like best about Amazon EMR is its ability to process large scale data workloads using frameworks like Spark, Hadoop, and Hive without manually setting up complex big data infrastructure. It provides flexibility while integrating well with the Amazon AWS ecosystem. The flex”
Simplifies Spark Jobs with Robust Performance
“I like that Amazon EMR is easy to use, robust, and reliable. It's great that I don't have to worry about the infrastructure and the servers that my distributed load runs on. It's easy to copy my PySpark files onto the EMR cluster and run them. The auto-scaling feature is very hel”