Impala Open Distribution vs. Presto

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Impala Open Distribution
Score 0.0 out of 10
N/A
Open Distribution reimagines how hotels and room sellers work with each other to open the door to new opportunities and build their businesses together. Powered by Impala’s tech, it allows hotels and room sellers to work directly with each other to negotiate deals and reach new markets without all the usual headaches. Open Distribution is designed so that hotels can start offering rooms - and room sellers can start selling them - in just a few minutes. Anyone can join without the…N/A
Presto
Score 10.0 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
Impala Open DistributionPresto
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Impala Open DistributionPresto
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
Impala Open DistributionPresto
Best Alternatives
Impala Open DistributionPresto
Small Businesses
Rithum
Rithum
Score 6.9 out of 10
InterSystems IRIS
InterSystems IRIS
Score 7.8 out of 10
Medium-sized Companies

No answers on this topic

InterSystems IRIS
InterSystems IRIS
Score 7.8 out of 10
Enterprises
Vendavo
Vendavo
Score 8.0 out of 10
SAP IQ
SAP IQ
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Impala Open DistributionPresto
Likelihood to Recommend
-
(0 ratings)
7.8
(2 ratings)
User Testimonials
Impala Open DistributionPresto
Likelihood to Recommend
Impala
No answers on this topic
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
Impala
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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
Impala
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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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Alternatives Considered
Impala
No answers on this topic
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
Impala
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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