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  1. Interview with Apache Pig: Theory and Practical Guide
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A Companion to Ethics, Blackwell an irreplaceable resource. Utilitarianism: For and Against, CUP : one of the most informative discussions of utilitarianism — its structure, its motives, and its problems. Stewart, N. Philosophy of Religion, 4th ed. Reason and Religious Belief , 4 th ed.

Is God to Blame? The Puzzle of God, 3rd ed. Elements of Mind, OUP : an excellent, but occasionally challenging, introduction to intentionality, the mind-body problem, consciousness, propositional attitudes and perception Crane, T. The Mechanical Mind, Penguin : an excellent introduction to topics on the border between philosophy of mind and philosophy of cognitive science Graham, G. Philosophy of Mind, Blackwell : a very approachable and engaging introduction to many topics on the syllabus Guttenplan, S.

Companion to Philosophy of Mind, Blackwell : an indispensable reference work with entries on many of the concepts and topics covered in the syllabus Heil, J. Philosophy of Mind: a Contemporary Introduction 3 rd ed. Philosophy of Mind 3 rd ed. An Introduction to Philosophy of Mind 2nd ed. How Free Are You? Free Will, Acumen Pink, T.

Interview with Apache Pig: Theory and Practical Guide

Free Will 2nd ed. Metaphysics, Routledge , Ch. What Philosophy Is esp. Problems of Philosophy various editions, e. Plato and the Republic, Routledge , esp. Descartes and the Meditations, Routledge Jones, G. About this Item: Continuum, Condition: Good. Seller Inventory mon More information about this seller Contact this seller 1.

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This is where I come in! It turns out that my language, Pig Latin, is much easier to write than MapReduce one estimate points out that 10 lines of Pig Latin are equivalent to lines of MapReduce code in Java! When I run on MapReduce as the execution engine, under the hood, all these Pig Latin codes are converted into their MapReduce programs. Pretty neat, huh? Not quite! The MapReduce paradigm requires that a program be broken down into a series of mapping and reducing tasks. This requirement can sometimes lead to a convoluted program that consists of a long linear series of mappers and reducers.

Doing Philosophy: A Practical Guide for Students

Here is an illustration of DAG:. When creating a DAG, I will ensure that the steps are ordered in a way that optimizes computation. In my experience, people use me for two things: extract-transform-load ETL pipelines and ad-hoc queries. A typical ETL pipeline loads in a data from a source, clean it, transform it and store it somewhere.

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An example is the processing of log data of a particular website. Using my FILTER function, one can clean these logs by filtering out data coming from automatic bots and internal views. I can also perform transformations that enrich the original log data. For instance, I can group these log data by the countries that the visitors hail from to provide more fine-grained view of the data.

My other common use case is research and ad-hoc queries.

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Traditionally, these are done using SQL. My flexibility and extendability lend themselves well for such cases. This is a question I receive a lot. Like myself, Hive is a data analysis tool that is part of the Hadoop ecosystem and can run on either MapReduce, Tez or Spark. If both Hive and I can perform data analysis, why is there a need to have both of us? In other words, what are the differences between us? Here are my answer to that:. Well let me be frank — I do have some limitations that might make me unsuitable in some cases.

Here are my main limitations:.

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Sadly, this is true. Spark is quickly gaining ground in the industry. In the rapidly evolving Big Data sector, it is only natural that the community regularly phases out older technologies in favor of newer, more powerful tools. Let me quickly tell you why Spark is in many ways superior to myself. While my language Pig Latin provides several high-level operators and is extendable to a certain extent, Spark is a full-blown programming language. All these allow you to transform your data in almost any way you like! On top of that, Spark also has several libraries that can perform specialized procedures on massive data sets.

With these abilities, Spark serves as a unified platform for the processing and analysis of Big Data. These are all things that are out of my reach! The HDP Sandbox is a pre-configured learning environment that contains the latest developments from Apache Hadoop, including myself! The Sandbox comes packaged in a virtual environment that one can run in their personal machine, allowing them to get a hold of me in no time.


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Once you download the Sandbox, you can run me through a couple of ways:. Pig Latin is a relatively simple scripting language. Here is an example of Pig Latin. The script below is the Pig Latin equivalent of the MapReduce program we saw earlier, which counts the occurrence of each distinct word in a text file.

Take a look:. Although there are certain similarities, there are also some salient differences. SQL is a descriptive language. It lets users describe the information i. Pig Latin, on the other hand, is a procedural language. In Pig Latin, users have to specify the steps that will lead them to their desired table or relation , in Pig Latin terminology.