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65 Ratings
25 Ratings
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Score 8.2 out of 101
65 Ratings
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Score 8.1 out of 101

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Likelihood to Recommend

Amazon EMR

EMR is suited if the jobs are long running and doesn't really need much monitoring. EMR is really flexible in processing the data on s3 as a developer doesn't need to spend time on debugging the connections to s3 from a big data framework as most of the configuration is taken care of by Amazon. Very cheap when compared to most of the solutions on the market and the ready to go configuration at the launch time reduces the amount of time required for admin tasks. So, considering the cheap cost, processing options on s3 and scalability via adding task nodes, EMR serves a better purpose for startups considering open source and cost efficient options. However, EMR comes with its own disadvantages. There is no proper UI to track real time jobs which is however possible with Enterprise editions like Cloudera, Hortonworks etc. EMR could provide an interface to add workbooks and code snippets in the cluster as it would reduce the time to submit the tasks. EMR also lags the potential to automatically replace unhealthy nodes.
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Apache Hive

This is best suited for data analysts and scientists, it's not a programmers tool. You may still need an RDBMS to read data from as updates and deletes can get a bit more complicated, you can run batch jobs, this will have to be facilitated by additional tools.Its good for fast query processing, for storing large amounts of data.
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Pros

  • Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce.
  • MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects.
  • Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time.
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  • Can query on large sets of data and fast when compared to RDBMS
  • Can use SQL for data access and no need to learn new language
  • Can write custom functions (UDF) with python and also Java
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Cons

  • The analytical processes generally run quicker with the standalone tools of Hadoop, Spark, and others. If you only use one big data tool and don't really need things simplified, then Elastic MapReduce is more of an overhead tool that doesn't add much value.
  • The analytical capabilities of Elastic MapReduce are nowhere near as complex or broad as non-big data tools. I would suggest not using the tool unless your data really is big data.
  • The machine learning capabilities of Elastic MapReduce (using the big data tools of Hadoop/Spark) are good but are not as easy to use as other machine learning tools.
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  • Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
  • Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
  • Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.
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Likelihood to Renew

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Apache Hive10.0
Based on 1 answer
Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.
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Usability

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Apache Hive9.0
Based on 1 answer
Hive's support SQL like queries improves its usability since almost every potential user of Hive would have had experience with SQL.
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Alternatives Considered

Perhaps the biggest advantage Amazon Elastic MapReduce has over competing big data management software is the user base. Elastic MapReduce, compliments of its connection with Amazon, has a large user base to whom questions about functionality can be addressed. The software also has a very nice user interface. Additionally, Elastic MapReduce runs fairly quickly and the results are generally easier to manipulate. With this said, Elastic MapReduce is definitely not the easiest nor quickest tool for big data analytics.
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Hive is SQL compliant which makes it easy for the data folks compared to Pig
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Return on Investment

  • Amazon Elastic MapReduce has had a positive ROI in the sense that it saved time managing big data projects where analysts were using different big data tools. Essentially, an increase in employee productivity.
  • Elastic MapReduce is not worth it in cases where you're just trying things out. You'll likely lose money unless you're sure that using MapReduce is a good idea.
  • Elastic MapReduce takes some time learning, although not too much. If the employee is less well-versed in big data analytics, the software is a high hill to climb that eats up employee time.
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  • Hive Metastore is great as all other query engines plug into it. I'd tell the hive community to invest more into the metastore as it's one of the strong points of hive.
  • Overall, we first started with Hadoop, then Hive and then Presto. These are all core components of data in our business and it's highly critical for our business.
  • We use Hive extensively to compute daily/weekly reports which are essential to run the business.
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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

Apache Hive

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