Amazon EMR

377
350
+ 1
53
Apache Kudu

48
130
+ 1
8
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Amazon EMR vs Kudu: What are the differences?

Developers describe Amazon EMR as "Distribute your data and processing across a Amazon EC2 instances using Hadoop". Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year. On the other hand, Kudu is detailed as "Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform". A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

Amazon EMR belongs to "Big Data as a Service" category of the tech stack, while Kudu can be primarily classified under "Big Data Tools".

"On demand processing power" is the top reason why over 13 developers like Amazon EMR, while over 2 developers mention "Realtime Analytics" as the leading cause for choosing Kudu.

Kudu is an open source tool with 789 GitHub stars and 263 GitHub forks. Here's a link to Kudu's open source repository on GitHub.

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    What is Amazon EMR?

    It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

    What is Apache Kudu?

    A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
    What companies use Amazon EMR?
    What companies use Apache Kudu?

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    What tools integrate with Amazon EMR?
    What tools integrate with Apache Kudu?
    What are some alternatives to Amazon EMR and Apache Kudu?
    Amazon EC2
    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Azure HDInsight
    It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
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