Apache Spark Should Spark Your Interest
December 13, 2017

Apache Spark Should Spark Your Interest

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Apache Spark

At my current company, we are using Spark in a variety of ways ranging from batch processing to data analysis to machine learning techniques. It has become our main driver for any distributed processing applications. It has gained quick adoption across the organization for its ease of use, integration into the Hadoop stack, and for its support in a variety of languages.
  • Ease of use, the Spark API allows for minimal boilerplate and can be written in a variety of languages including Python, Scala, and Java.
  • Performance, for most applications we have found that jobs are more performant running via Spark than other distributed processing technologies like Map-Reduce, Hive, and Pig.
  • Flexibility, the frameworks comes with support for streaming, batch processing, sql queries, machine learning, etc. It can be used in a variety of applications without needing to integrate a lot of other distributed processing technologies.
  • Resource heavy, jobs, in general, can be very memory intensive and you will want the nodes in your cluster to reflect that.
  • Debugging, it has gotten better with every release but sometimes it can be difficult to debug an error due to ambiguous or misleading exceptions and stack traces.
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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.
If you are running a distributed environment and are running applications that make use of batch processing, analytics, streaming, machine learning, or graphing then I cannot recommend Spark enough. It is easy to get going, simple to learn (relative to similar technologies), and can be used in a variety of use cases. All while giving you great performance.