Apache Spark vs. Hydrograph

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.9 out of 10
N/A
N/AN/A
Hydrograph
Score 8.0 out of 10
N/A
Bitwise offers Hydrograph, a data integration tool with provides ETL functionality on Hadoop and Spark.N/A
Pricing
Apache SparkHydrograph
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkHydrograph
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 SparkHydrograph
Top Pros
Top Cons
Features
Apache SparkHydrograph
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
Hydrograph
6.0
1 Ratings
33% below category average
Connect to traditional data sources00 Ratings5.01 Ratings
Connecto to Big Data and NoSQL00 Ratings7.01 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
Hydrograph
6.5
1 Ratings
24% below category average
Simple transformations00 Ratings5.01 Ratings
Complex transformations00 Ratings8.01 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
Hydrograph
5.4
1 Ratings
39% below category average
Data model creation00 Ratings7.01 Ratings
Business rules and workflow00 Ratings4.01 Ratings
Collaboration00 Ratings5.01 Ratings
Testing and debugging00 Ratings6.01 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
Hydrograph
6.5
1 Ratings
24% below category average
Integration with data quality tools00 Ratings6.01 Ratings
Integration with MDM tools00 Ratings7.01 Ratings
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Apache SparkHydrograph
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User Ratings
Apache SparkHydrograph
Likelihood to Recommend
10.0
(23 ratings)
8.0
(1 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
-
(0 ratings)
User Testimonials
Apache SparkHydrograph
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.
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Bitwise
hydrograph is very usefull when we need to analyze big data. in our scenario it helped a lot with rdms databases
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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
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Bitwise
  • Coupling between complex model and MapReduce framework without reducer procedure was simplified.
  • The ability to reduce execution time and handle partial failure
  • The framework adapts to higherned complex model
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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
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Bitwise
  • Microsoft azure is recently joined in 2020 that can be improved.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Bitwise
No answers on this topic
Usability
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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Bitwise
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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Bitwise
No answers on this topic
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.
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Bitwise
Snap logic fits good for small/medium whereas hydrograph suits even for Enterprise grade.
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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
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Bitwise
  • Their is no tangible ROI for us Management of bid data is easy.
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