September 2016
Beginner to intermediate
344 pages
7h 44m
English
Content preview from Spark for Data ScienceBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Evolution of big data analytics
The previous section outlined how the computational and data analytics challenges were addressed for big data requirements. It was possible because of the convergence of several related trends such as low-cost commodity hardware, accessibility to big data, and improved data analytics techniques. Hadoop became a cornerstone in many large, distributed data processing infrastructures.
However, people soon started realizing the limitations of Hadoop. Hadoop solutions were best suited for only specific types of big data requirements such as ETL; it gained popularity for such requirements only.
There were scenarios when data engineers or analysts had to perform ad hoc queries on the data sets for interactive data analysis. ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.