Download 101 Ready-To-Use Excel Macros by Michael Alexander PDF

By Michael Alexander

Store time and be extra effective with this beneficial advisor to Excel macros!

While such a lot books approximately Excel macros provide in simple terms minor examples, frequently geared toward illustrating a selected subject, this worthy source will give you the instruments had to successfully and successfully application Excel macros instantly. step by step directions provide help to create VBA macros and clarify easy methods to customise your functions to seem and paintings precisely as you will want them to. by way of the tip of the e-book, you'll know how each one featured macro works, manage to reuse the macros incorporated within the e-book and on-line, and alter the macro for private use. exhibits you ways to resolve universal issues of the featured macros, whether you lack vast programming wisdom Outlines an issue that should be solved and gives the particular Excel macro, in addition to the downloadable code, to unravel the matter presents an evidence of the way every one macro works and the place to take advantage of the macro.

With "101 Ready-to-Use Excel Macros," Microsoft MVP Michael Alexander is helping you keep time, automate initiatives, and finally be extra effective.

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MapReduce is a batch query processor, and the ability to run an ad hoc query against your whole dataset and get the results in a reasonable time is transformative. It changes the way you think about data and unlocks data that was previously archived on tape or disk. It gives people the opportunity to innovate with data. Questions that took too long to get answered before can now be answered, which in turn leads to new questions and new insights. For example, Mailtrust, Rackspace’s mail division, used Hadoop for processing email logs.

So, although it’s feasible to parallelize the processing, in practice it’s messy. Using a framework like Hadoop to take care of these issues is a great help. Analyzing the Data with Hadoop To take advantage of the parallel processing that Hadoop provides, we need to express our query as a MapReduce job. After some local, small-scale testing, we will be able to run it on a cluster of machines. Map and Reduce MapReduce works by breaking the processing into two phases: the map phase and the reduce phase.

For example, we can follow the number of records that went through the system: five map input records produced five map output records (since the mapper emitted one output record for each valid input record), then five reduce input records in two groups (one for each unique key) produced two reduce output records. The output was written to the output directory, which contains one output file per reducer. The job had a single reducer, so we find a single file, named part-r-00000: % cat output/part-r-00000 1949 111 1950 22 This result is the same as when we went through it by hand earlier.

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