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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

    MariaDB MaxScale

    Score8 out of 10
    N/AN/AN/A
    Pricing
    Apache SparkMariaDB MaxScale
    Editions & Modules
    No answers on this topic
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    Offerings
    Pricing Offerings
    Apache SparkMariaDB MaxScale
    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
    Apache SparkMariaDB MaxScale
    Considered Both Products
    Apache
    No answer on this topic
    MariaDB
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    11 Answers
    No answers on this topic
    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
    No answers on this topic
    Best Alternatives
    Apache SparkMariaDB MaxScale
    Small Businesses
    No answers on this topic
    No answers on this topic
    Medium-sized Companies
    Cloudera Manager
    Score9.9 out of 10
    No answers on this topic
    Enterprises
    Hadoop
    Score7.9 out of 10
    No answers on this topic
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Apache SparkMariaDB MaxScale
    Likelihood to Recommend
    9.0
    (24 ratings)
    8.0
    (1 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    8.0
    (4 ratings)
    10.0
    (1 ratings)
    Support Rating
    8.7
    (4 ratings)
    10.0
    (1 ratings)
    User Testimonials
    Apache SparkMariaDB MaxScale
    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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    MariaDB
    If you have a cluster of nodes with MariaDB MaxScale and you want all the
    nodes of the cluster to have a similar load and not to be be
    penalized for queries or writes to the database, you can mount the MaxScale product in front of the MariaDB cluster. MaxScale will balance the requests based on what is being sent to each
    node to have an equitable load and will cache the queries that it
    sends to each one. This optimizes the response time to
    database queries and spreads the load out among all nodes in a
    similar way.
    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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    MariaDB
    • Open source proxy server
    • Great performance
    • Offers load balancing
    • Modular architecture
    Incentivized
    Read full review
    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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    MariaDB
    • Improve balancing based on SQL queries
    • Improve swap memory consumption
    • It has a slight delay
    Incentivized
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    Likelihood to Renew
    Apache
    Capacity of computing data in cluster and fast speed.
    Read full review
    MariaDB
    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
    Read full review
    MariaDB
    MariaDB MaxScale is a powerful tool and easy to use. It has helped us a lot to improve the performance of our database queries. It implements a security layer that acts as a firewall for the databases, masks the data, or limits the results of the queries. It also integrates easily with Kafka.
    Incentivized
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    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.
    Read full review
    MariaDB
    We have launched several inquiries to MariaDB MaxScale support, and they have always responded very quickly. They also want to hold frequent meetings with the client to get their opinion understand how they can help. I see a very human support that is concerned about the customer.
    Incentivized
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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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    MariaDB
    ProxySLQ allows many simultaneous connections and allows the cache of queries in memory but it does not have high availability or scalability natively, only through external tools. HAProxy is not able to perform load balancing in an optimal way.
    Instead, MariaDB MaxScale allows high availability, scalability, and data replication to external systems such as Kafka. In addition, MaxScale has a monitor that allows you to see the status of the set of databases.
    Incentivized
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    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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    MariaDB
    • Reduce the number of database nodes
    • Improve the performance of the applications that use the databases
    • A new layer is added in the service architecture
    Incentivized
    Read full review
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