Amazon EMR (Elastic MapReduce) vs. Presto

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon EMR
Score 8.6 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
Presto
Score 3.0 out of 10
N/A
Presto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases. Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.N/A
Pricing
Amazon EMR (Elastic MapReduce)Presto
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Amazon EMRPresto
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon EMR (Elastic MapReduce)Presto
Top Pros
Top Cons
Best Alternatives
Amazon EMR (Elastic MapReduce)Presto
Small Businesses

No answers on this topic

SingleStore
SingleStore
Score 9.7 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
SingleStore
SingleStore
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.9 out of 10
SingleStore
SingleStore
Score 9.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon EMR (Elastic MapReduce)Presto
Likelihood to Recommend
8.4
(19 ratings)
7.8
(2 ratings)
Usability
8.3
(3 ratings)
-
(0 ratings)
Support Rating
9.0
(3 ratings)
-
(0 ratings)
User Testimonials
Amazon EMR (Elastic MapReduce)Presto
Likelihood to Recommend
Amazon AWS
We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
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Open Source
Presto is for interactive simple queries, where Hive is for reliable processing. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like Vertica
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Pros
Amazon AWS
  • Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce.
  • MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects.
  • Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time.
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Open Source
  • Linking, embedding links and adding images is easy enough.
  • Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
  • Organizing & design is fairly simple with click & drag parameters.
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Cons
Amazon AWS
  • Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
  • Like some of the enterprise editions EMR does not provide on premises options.
  • No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.
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Open Source
  • Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
  • Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
  • UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.
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Usability
Amazon AWS
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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Open Source
No answers on this topic
Support Rating
Amazon AWS
There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
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Open Source
No answers on this topic
Alternatives Considered
Amazon AWS
Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
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Open Source
Presto is good for a templated design appeal. You cannot be too creative via this interface - but, the layout and options make the finalized visual product appealing to customers. The other design products I use are for different purposes and not really comparable to Presto.
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Return on Investment
Amazon AWS
  • Positive: Helped process the jobs amazingly fast.
  • Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time.
  • Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house.
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Open Source
  • Presto has helped scale Uber's interactive data needs. We have migrated a lot out of proprietary tech like Vertica.
  • Presto has helped build data driven applications on its stack than maintain a separate online/offline stack.
  • Presto has helped us build data exploration tools by leveraging it's power of interactive and is immensely valuable for data scientists.
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ScreenShots