Amazon EMR (Elastic MapReduce) vs. Apache Spark

Amazon EMR (Elastic MapReduce)

61 Reviews and Ratings

Apache Spark

161 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Amazon EMR	Score8.1 out of 10	N/A	Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.	N/A
Apache Spark	Score9.2 out of 10	N/A	Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.	N/A

Pricing

Amazon EMR

Apache Spark

No answers on this topic

No answers on this topic

Pricing Offerings
Amazon EMR	Apache Spark
Free Trial
No	No
Free/Freemium Version
No	No
Premium Consulting/Integration Services
No	No

No setup fee

No setup fee

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Community Pulse
Key User Insights
	Amazon EMR	Apache Spark
Considered Both Products	Amazon AWS Nicolas Costa Ossa Director of Customer Operations & Account Management in Engineering at Koombea (Internet, 51-200 employees) Chose Amazon EMR EMR is more suited for developers. Databricks feel more for data science-oriented with its notebooks and customs visualizations. With EMR you can more easily add additional capacity on-damnd on the instance. With others is a more cumbersome process. And then, you can also … Incentivized Helpful? Verified User Chose Amazon EMR The alternatives to EMR are mainly hadoop distributions owned by the 3 companies above. I have not used the other distributions so it is difficult to comment, but the general tradeoff is, at the cost of a longer setup time and more infra management, you get more flexible … Incentivized Helpful?	Apache Yogesh Mhasde Technical Manager in Information Technology at Rishabh Software Private Limited (Information Technology & Services, 501-1000 employees) Chose Apache Spark 1. Apache Spark is almost 100 % faster than Hadoop. 2. Apache Spark is more stable than Amazon EMR. 3. The end to end distributed machine library is more robust in Apache Spark. Incentivized Helpful? Verified User Chose Apache Spark Apache Spark has much more better performance and features if we compare with Hive or map/reduce kind of solutions. Spark has many other features for machine learning, streaming. Incentivized Helpful? Kartik Chavan Data Analyst in Information Technology at The University of Texas at Arlington (Electrical/Electronic Manufacturing, 1001-5000 employees) Chose Apache Spark Even with Python, MapReduce is lengthy coding. Combination of Python with Apache Spark will not only shorten the code, but it will effectively increase the speed of algorithms. Occasionally, I use MapReduce, but Apache Spark will replace MapReduce very soon. It has many … Incentivized Helpful? Verified User Chose Apache Spark There are a few newer frameworks for general processing like Flink, Beam, frameworks for streaming like Samza and Storm, and traditional Map-Reduce. I think Spark is at a sweet spot where its clearly better than Map-Reduce for many workflows yet has gotten a good amount of … Incentivized Helpful?
	Would buy again
	No answers on this topic	No answers on this topic
	Delivers good value for the price
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	Happy with the feature set
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	Lived up to sales and marketing promises
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	Implementation went as expected
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User Ratings
	Amazon EMR	Apache Spark
Likelihood to Recommend	8.0 (19 ratings)	9.0 (24 ratings)
Likelihood to Renew	- (0 ratings)	10.0 (1 ratings)
Usability	7.0 (4 ratings)	8.0 (4 ratings)
Support Rating	9.0 (3 ratings)	8.7 (4 ratings)