Apache Spark Pros and Cons

Apache Spark Pros

reviewer879201
Technical Consultant at a tech services company with 1-10 employees
I feel the streaming is its best feature.
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Karthikeyan R
Principal Architect at a financial services firm with 1,001-5,000 employees
I found the solution stable. We haven't had any problems with it.
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reviewer1046250
Senior Consultant & Training at a tech services company with 51-200 employees
The most valuable feature of this solution is its capacity for processing large amounts of data.
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reviewer1221765
Co-Founder at a tech vendor with 11-50 employees
The features we find most valuable are the machine learning, data learning, and Spark Analytics.
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reviewer1223676
Lead Consultant at a tech services company with 51-200 employees
The main feature that we find valuable is that it is very fast.
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Snrsecengin567
Snr Security Engineer at a tech vendor with 201-500 employees
The scalability has been the most valuable aspect of the solution.
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Mohamed Ghorbel
Director of BigData Offer at IVIDATA
The solution is very stable.
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KamleshKhollam
Consultant at Exusia
The processing time is very much improved over the data warehouse solution that we were using.
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reviewer894894
User
Features include machine learning, real time streaming, and data processing.
The fault tolerant feature is provided.
It provides a scalable machine learning library.
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Apache Spark Cons

reviewer879201
Technical Consultant at a tech services company with 1-10 employees
When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources.
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Karthikeyan R
Principal Architect at a financial services firm with 1,001-5,000 employees
It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.
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reviewer1046250
Senior Consultant & Training at a tech services company with 51-200 employees
When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.
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Find out what your peers are saying about Apache, Informatica, Pivotal and others in Hadoop. Updated: January 2020.
396,515 professionals have used our research since 2012.
reviewer1221765
Co-Founder at a tech vendor with 11-50 employees
We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.
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reviewer1223676
Lead Consultant at a tech services company with 51-200 employees
We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.
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Snrsecengin567
Snr Security Engineer at a tech vendor with 201-500 employees
The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.
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Mohamed Ghorbel
Director of BigData Offer at IVIDATA
The solution needs to optimize shuffling between workers.
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KamleshKhollam
Consultant at Exusia
I would like to see integration with data science platforms to optimize the processing capability for these tasks.
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reviewer894894
User
It should support more programming languages.
Needs to provide an internal schedule to schedule spark jobs with monitoring capability.
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Find out what your peers are saying about Apache, Informatica, Pivotal and others in Hadoop. Updated: January 2020.
396,515 professionals have used our research since 2012.