What users are saying about
245 Ratings
127 Ratings
245 Ratings
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Score 8.4 out of 100
127 Ratings
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Score 8.7 out of 100

Likelihood to Recommend

Hadoop

Apache Hadoop (and its subsequent add-ons) are well-suited to larger, unstructured data flows, such as aggregation of web traffic or advertising. Geospatial algorithms and their outputs are well-suited for this kind of aggregation as structuring that data is challenging, but leaving it unstructured and performing queries as-needed is a better fit for most business models. With the advent of data science, I would expect Hadoop fits a LOT of their initial outputs quite well.
Joe Hughes | TrustRadius Reviewer

Apache Spark

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
Thomas Young | TrustRadius Reviewer

Pros

Hadoop

  • HDFS is reliable and solid, and in my experience with it, there are very few problems using it
  • Enterprise support from different vendors makes it easier to 'sell' inside an enterprise
  • It provides High Scalability and Redundancy
  • Horizontal scaling and distributed architecture
Bharadwaj (Brad) Chivukula | TrustRadius Reviewer

Apache Spark

  • 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
Nitin Pasumarthy | TrustRadius Reviewer

Cons

Hadoop

  • Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
  • Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
  • Hadoop cannot be used for running interactive jobs or analytics.
Mrugen Deshmukh | TrustRadius Reviewer

Apache Spark

  • 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
Anson Abraham | TrustRadius Reviewer

Likelihood to Renew

Hadoop

Hadoop 9.6
Based on 8 answers
Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
Bhushan Lakhe | TrustRadius Reviewer

Apache Spark

No score
No answers yet
No answers on this topic

Usability

Hadoop

Hadoop 8.5
Based on 5 answers
Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.
Blake Baron | TrustRadius Reviewer

Apache Spark

Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.
Partha Protim Pegu | TrustRadius Reviewer

Support Rating

Hadoop

Hadoop 6.9
Based on 6 answers
We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.
Gene Baker | TrustRadius Reviewer

Apache Spark

Apache Spark 8.2
Based on 6 answers
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.
Yogesh Mhasde | TrustRadius Reviewer

Online Training

Hadoop

Hadoop 6.1
Based on 2 answers
Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.
Bhushan Lakhe | TrustRadius Reviewer

Apache Spark

No score
No answers yet
No answers on this topic

Alternatives Considered

Hadoop

Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which provides very high bandwidth to support MapReduce workloads.
Anonymous | TrustRadius Reviewer

Apache Spark

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.
Anonymous | TrustRadius Reviewer

Return on Investment

Hadoop

  • Hadoop has allowed us to scale out a few of our tier-1, customer facing applications to provide very fast access to reports and analytics.
  • Hadoop was easily implemented by our Linux team and onboarded by our Hadoop Admins.
  • Hadoop has been a very stable platform and only goes down due to server patching or other maintenance.
Mark McCully | TrustRadius Reviewer

Apache Spark

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Carla Borges | TrustRadius Reviewer

Pricing Details

Hadoop

General

Free Trial
Free/Freemium Version
Yes
Premium Consulting/Integration Services
Entry-level set up fee?
No

Apache Spark

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

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