IBM Analytics Engine vs. Presto

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM Analytics Engine
Score 8.9 out of 10
N/A
IBM BigInsights is an analytics and data visualization tool leveraging hadoop.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
IBM Analytics EnginePresto
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
IBM Analytics EnginePresto
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
IBM Analytics EnginePresto
Top Pros
Top Cons
Best Alternatives
IBM Analytics EnginePresto
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
Apache Spark
Apache Spark
Score 8.7 out of 10
SingleStore
SingleStore
Score 9.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM Analytics EnginePresto
Likelihood to Recommend
9.5
(9 ratings)
7.8
(2 ratings)
User Testimonials
IBM Analytics EnginePresto
Likelihood to Recommend
IBM
  • Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
  • But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.
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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
IBM
  • Jobs with Spark, Hadoop, or Hive queries are rapidly attained
  • Can collect, organize and analyze your data accurately
  • You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.
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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
IBM
  • Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
  • Bundling of the Cloud Object Storage should be included with the Analytics Engine.
  • The inability to add your own Hadoop stack components has made some transfers a little more complex.
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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
IBM
We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
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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
IBM
  • This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
  • IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
  • The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.
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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