How Long to Read On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science

How Long Does it Take to Read On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science?

It takes the average reader and 31 minutes to read On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science by Bilal Akil

Assuming a reading speed of 250 words per minute. Learn more

Description

Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level, requiring many implementation steps even for simple analysis tasks. This has led to the development of advanced dataflow oriented platforms, most prominently Apache Spark and Apache Flink. Those platforms not only aim to improve performance through improved in-memory processing, but in particular provide built-in high-level data processing functionality, such as filtering and join operators, which should make data analysis tasks easier to develop than with plain Hadoop MapReduce. But is this indeed the case? This paper compares three prominent distributed data processing platforms: Apache Hadoop MapReduce; Apache Spark; and Apache Flink, from a usability perspective. We report on the design, execution and results of a usability study with a cohort of masters students, who were learning and working with all three platforms in order to solve different use cases set in a data science context. Our findings show that Spark and Flink are preferred platforms over MapReduce. Among participants, there was no significant difference in perceived preference or development time between both Spark and Flink as platforms for batch-oriented big data analysis. This study starts an exploration of the factors that make big data platforms more - or less - effective for users in data science.

How long is On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science?

On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science by Bilal Akil is 31 pages long, and a total of 7,781 words.

This makes it 10% the length of the average book. It also has 10% more words than the average book.

How Long Does it Take to Read On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science Aloud?

The average oral reading speed is 183 words per minute. This means it takes and 42 minutes to read On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science aloud.

What Reading Level is On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science?

On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science is suitable for students ages 8 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science?

On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science by Bilal Akil is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

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