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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Apache Spark

    Score9.2 out of 10
    N/AApache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.N/A

    Scala

    Score6 out of 10
    N/AScala in Malvern, PA offers their digital signage software which provides Designer for content design, Content Manager for content organization and control, and Player for content viewing. Notably the software supports a wide array of digital signage including touchscreen kiosks and service for direct customer engagement and interaction.N/A
    Pricing
    Apache SparkScala
    Editions & Modules
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    Offerings
    Pricing Offerings
    Apache SparkScala
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
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    Community Pulse
    Apache SparkScala
    Considered Both Products
    Apache
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    Scala
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    Key User Insights
    Would buy again
    100%
    Would buy again
    11 Answers
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    Delivers good value for the price
    100%
    Delivers good value for the price
    11 Answers
    No answers on this topic
    Happy with the feature set
    100%
    Happy with the feature set
    11 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    8 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    11 Answers
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    Best Alternatives
    Apache SparkScala
    Small Businesses
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    Medium-sized Companies
    Cloudera Manager
    Score9.9 out of 10
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    Enterprises
    Hadoop
    Score7.9 out of 10
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    All AlternativesView all alternativesView all alternatives
    User Ratings
    Apache SparkScala
    Likelihood to Recommend
    9.0
    (24 ratings)
    6.4
    (2 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    8.0
    (4 ratings)
    -
    (0 ratings)
    Support Rating
    8.7
    (4 ratings)
    8.8
    (2 ratings)
    User Testimonials
    Apache SparkScala
    Likelihood to Recommend
    Apache
    Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
    Incentivized
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    Scala
    If you are in the data science world, Scala is the best language to work with Spark, the defacto data science data store. I think that is really the main likely reason I would ever recommend Scala. Another reason is if you already have a team of programmers familiar with functional programming, e.g. they all have years of Haskell experience. In that case, I definitely think Scala is a superior and faster-growing language than Haskell and that picking up Scala after Haskell should be quick.
    Incentivized
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    Pros
    Apache
    • 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
    Incentivized
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    Scala
    • Organizing different playlists.
    • Coordinating content schedule and running time.
    Incentivized
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    Cons
    Apache
    • 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
    Incentivized
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    Scala
    • The built-in compiler, scalac, is sssssssssslllllooooowwwwww. I mean like, if you thought the Java compiler was slow, try Scala! The default compiler on my 12k line codebase takes 4 minutes to compile from scratch on my i7 quad-core machine. This can be mitigated through the paid solution of Hydra which compiles your code in parallel. Unfortunately, it's quite expensive and your legal department or finance department may not approve of it. But if they do, for me, it reduced my compile time down to 80 seconds, much more manageable.
    • Scala is not going anywhere and support for it is slowly dying. This is the main reason I would not choose Scala for my next company or project. Important Scala libraries such as secure social (which is used for OAuth, a major requirement of every web app) are hardly maintained. Another library that suffers from lack of updates is Slick, the database mapper. There aren't enough engineers working on it to even provide support for the new features that came out in Postgres 9.0 (e.g. JSONb). There is simply not enough of a community to drive Scala forward and keep 3rd party libraries up to date as Java world does it.
    • As a corollary of a stagnant community, hiring Scala developers is hard as well. Of the 30 backend engineers we've hired, only 3 came in already knowing Scala. And as I will mention below, this is a BIG problem because learning Scala is really tough.
    • The learning curve for Scala is very, very steep. Anecdotally, I came into my current company with strong Java experience. Java is the closest language to Scala but it took me 6 months before I stopped needing to pair program on easy tickets. It doesn't help that Scala has some weird syntax like Map[A, +B] and that it forces you to do functional programming.
    Incentivized
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    Likelihood to Renew
    Apache
    Capacity of computing data in cluster and fast speed.
    Read full review
    Scala
    No answers on this topic
    Usability
    Apache
    If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
    Incentivized
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    Scala
    No answers on this topic
    Support Rating
    Apache
    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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    Scala
    The customer service team is very responsive and usually returns calls or emails within a couple of hours of placing a request or inquiry. Just about every rep I've spoken to has been very thorough and helpful, walking me through each problem and explaining the solutions in a way that's easy to understand.
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    Alternatives Considered
    Apache
    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.
    Incentivized
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    Scala
    No answers on this topic
    Return on Investment
    Apache
    • Business leaders are able to take data driven decisions
    • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
    • Business is able come up with new product ideas
    Incentivized
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    Scala
    • Negative: slow engineer onboarding. As I mentioned before, it took me 6 months to get up-to-speed on Scala and didn't need to bother more senior Scala engineers anymore for help with every ticket. That's hundreds of hours I wasted of myself and other engineer's time.
    • Positive: thread safety, no concurrency bug. The ROI on this one is really hard to calculate, but I do believe Scala has saved me hundreds of hours over the past few years by allowing me to never have to worry about deadlocks or race conditions. Scala is simply so safe we've never had race conditions within the JVM before.
    • Negative: third-party libraries aren't maintained so we have to fork and update them ourselves. As I mentioned before, we use Securesocial but it stopped receiving updates and there is simply no alternative to it. So, we forked it and put an engineer on it for a month to get it back up-to-date. What a waste of his time!
    Incentivized
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