Amazon EMR vs Apache Spark

Likelihood to Recommend

Amazon Elastic MapReduce is useful in cases where two conditions are met. First, that you are planning on using multiple big data tools simultaneously to analyze big data sets. And second, that you need a tool that simplifies managing big data tools. If these two conditions are met, MapReduce does a great job. The user interface is simple. The program eliminates some programming requirements. The software also makes setting up big data analyses much easier. With these benefits acknowledged, MapReduce is not a good tool for "small" data analyses, given that there are other tools that do the job quicker and much more professional output. If you're on the fence, try out MapReduce with competing "small" data tools and see if you really need big data software.

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Thomas Young

Owner, previous CEO

Econometric StudiosFinancial Services, 11-50 employees

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The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.

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Thomas Young

Owner, previous CEO

Econometric StudiosFinancial Services, 11-50 employees

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Pros

Easier to implement than older on-premise solutions
Works with open source technologies.
Keeps processing cost low.
It is flexible and works also for short term workloads and the pricing changes to that model.

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Nicolas Costa Ossa

Director of Customer Operations & Account Management

KoombeaInternet, 51-200 employees

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Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
Faster in execution times compare to Hadoop and PIG Latin
Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
Interoperability between SQL and Scala / Python style of munging data

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Nitin Pasumarthy

Software Engineer

LinkedInInternet, 5001-10,000 employees

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Cons

It could have been more matured with machine learning capabilities.
The support material available online on Elastic MapReduce is limited and we might end up spending more time in understanding/researching the tool.

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Verified User

Analyst in Research & Development

Information Technology & Services Company, 1001-5000 employees

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Memory management. Very weak on that.
PySpark not as robust as scala with spark.
spark master HA is needed. Not as HA as it should be.
Locality should not be a necessity, but does help improvement. But would prefer no locality

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Anson Abraham

Data Czar

Envisagenics, Inc.Marketing and Advertising, 51-200 employees

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Usability

Amazon EMR 8.2

Based on 4 answers

I give Amazon EMR this rating because while it is great at simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data, it can be run slow, freeze and glitch sometimes. So overall Amazon EMR is pretty good to use other than some basic issues.

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Verified User

Team Lead in Information Technology

Information Technology & Services Company, 11-50 employees

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Apache Spark 8.7

Based on 3 answers

Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.

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Partha Protim Pegu

Domain Consultant

InfosysInformation Technology & Services, 10,001+ employees

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Support Rating

Amazon EMR 9.3

Based on 4 answers

AWS and EMR support are on par with the best out there. You pay a premium for the support but they can save you time and money by quickly resolving issues or helping you get your problem taken care of. They are competing with Google and MS, and it shows in their support.

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Verified User

Administrator in Information Technology

Government Administration Company, 501-1000 employees

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Apache Spark 8.3

Based on 6 answers

1. It integrates very well with scala or python.2. It's very easy to understand SQL interoperability.3. Apache is way faster than the other competitive technologies.4. The support from the Apache community is very huge for Spark.5. Execution times are faster as compared to others.6. There are a large number of forums available for Apache Spark.7. The code availability for Apache Spark is simpler and easy to gain access to.8. Many organizations use Apache Spark, so many solutions are available for existing applications.

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Yogesh Mhasde

Technical Manager

Rishabh Software Private LimitedInformation Technology & Services, 501-1000 employees

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Alternatives Considered

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 versioning and potentially faster access to newer versions of some frameworks such as Spark.

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Verified User

Director in Engineering

Computer Software Company, 10,001+ employees

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Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

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Verified User

Engineer in Engineering

Computer Software Company, 51-200 employees

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Return on Investment

It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.

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Verified User

Engineer in Information Technology

Automotive Company, 1001-5000 employees

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It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.

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Carla Borges

Consultor Tecnico - Java Developer and Php Developer.

Consultec-TIComputer Software, 51-200 employees

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Pricing Details

General

Free Trial

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Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

General

Free Trial

—

Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Amazon EMR

Apache Spark

Likelihood to Recommend

Amazon EMR

Apache Spark

Pros

Amazon EMR

Apache Spark

Cons

Amazon EMR

Apache Spark

Usability

Amazon EMR

Apache Spark

Support Rating

Amazon EMR

Apache Spark

Alternatives Considered

Amazon EMR

Apache Spark

Return on Investment

Amazon EMR

Apache Spark

Pricing Details

General

Free Trial

Free/Freemium Version

Premium Consulting/Integration Services

Entry-level set up fee?

General

Free Trial

Free/Freemium Version

Premium Consulting/Integration Services

Entry-level set up fee?

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