What users are saying about

Amazon EMR

24 Ratings
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Score 8.3 out of 101

Data Science Workbench

10 Ratings
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Score 7.4 out of 101

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Likelihood to Recommend

Amazon EMR

If you don't have big data ..i.e petabytes of data with terabytes of data generating every day, then don't use Hadoop. Relational databases are enough for terabytes of data. Hadoop is not well suited for transactional systems or data.
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Data Science Workbench

  • If you already have a Cloudera partnership and a cluster, having this is a no brainer.
  • It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
  • With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.
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Pros

  • Distributed computing
  • Fault tolerant
  • Uptime
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  • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
  • For larger organizations/teams, it lets you be self reliant
  • As it sits on your cluster, it has very easy access of all the data on the HDFS
  • Linking with Github is a very good way to keep the code versions intact
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Cons

  • Providing user friendly tools for hdfs access
  • More simpler apis for easy access and processsing
  • Memory requirenent
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  • Not as great as RStudio; lacks some features when compared with it
  • It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product
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Alternatives Considered

Having one of these enterprise edition license comes at its own costs. But, the flexibility to have the cluster spin up with the workbenches and code snippets on the same is really beneficial. Especially, if one had to move out of EMR and consider an option which reduces the debugging time in establishing connections to AWS resources, I would love to used the mentioned three resources on EC2. This would definitely make the processing time to reduce as there is a flexibility to test real time and execute the code snippet and look at the performance and monitor the snippet in real time.
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Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
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Return on Investment

  • It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
  • It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
  • It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.
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  • As the tool itself can access all the HDFS, Spark data easily, the wait time between teams has reduced
  • Installation was a breeze, and ramp up time was fairly easy
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Pricing Details

Amazon EMR

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

Data Science Workbench

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