1010data vs. Apache Spark

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
1010data
Score 8.7 out of 10
N/A
N/AN/A
Apache Spark
Score 8.8 out of 10
N/A
N/AN/A
Pricing
1010dataApache Spark
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
1010dataApache Spark
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
Best Alternatives
1010dataApache Spark
Small Businesses
Google BigQuery
Google BigQuery
Score 8.7 out of 10

No answers on this topic

Medium-sized Companies
Cloudera Enterprise Data Hub
Cloudera Enterprise Data Hub
Score 9.0 out of 10
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Enterprises
Oracle Exadata
Oracle Exadata
Score 8.5 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 8.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
1010dataApache Spark
Likelihood to Recommend
10.0
(2 ratings)
10.0
(23 ratings)
Likelihood to Renew
-
(0 ratings)
10.0
(1 ratings)
Usability
8.0
(1 ratings)
10.0
(3 ratings)
Support Rating
-
(0 ratings)
8.7
(4 ratings)
User Testimonials
1010dataApache Spark
Likelihood to Recommend
1010data
The software is excellent for any application which is too large for Excel. The visual interface surpasses that of most SQL platforms. It is quite useful for data mining in an exploratory way but less useful in statistical and regression analysis.
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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.
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Pros
1010data
  • To "pool" their data for market analysis
  • Very large data analysis
  • Performance and scalability
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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
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Cons
1010data
  • The ten.do interface could use more detailed documentation
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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
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Likelihood to Renew
1010data
No answers on this topic
Apache
Capacity of computing data in cluster and fast speed.
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Usability
1010data
That's votes by our team.
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Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Support Rating
1010data
No answers on this topic
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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Alternatives Considered
1010data
While we have used SQL, 1010data is really the only industry standard product available for our use.
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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.
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Return on Investment
1010data
  • Positive impact on help business make a data-drive decision
  • Positive impact on big data analysis
  • Negative impact on user friendly
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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
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