Revision as of 10:06, 25 June 2013

Data: How to surf, rather than drown!

Introduction

Data on Disk

BCp2$ wget https://www.kernel.org/pub/linux/kernel/v3.x/testing/linux-3.10-rc7.tar.xz
BCp2$ time cp linux-3.10-rc7.tar.xz linux-3.10-rc7.tar.xz.copy

real	0m3.530s
user	0m0.000s
sys	0m0.068s

BCp2$ tar --use-compress-program=xz -xf linux-3.10-rc7.tar.xz
BCp2$ time cp

These timings were taken at ~10:45 on the 25 Jun 2013. Your mileage may vary!

Data over the Network

Filling the pipe.

<gallery widths=250px heights=200px> File:Pipeline.png|We can think of networks as pipelines for data. File:Skater.png|Unless we fill the pipeline we won't achieve it's throughput capacity. <gallery>

Data when Writing your own Code

Memory Hierarchy

Files & File Formats

Data Analytics

Some common operations you may want to perform on your data:

Cleaning
Filtering
Calculating summary statics (means, medians, variances)
Creating plots & graphics
Tests of statistical significance
Sorting and searching

Selecting the right tools.

Databases

GUI. Accessing from a program or script. Enterprise Grade The data haven.

Numerical Packages

Such as R, MATLAB & Python.

Bespoke Applications

Rolling Your Own

Principles: Sort & binary search.

Tools: Languages, libraries and packages.

When Data gets Big

Quotas.

Local Disks.

Swapping.

Data the Google way - Map-Reduce.

Hadoop & Friends.

Summary

Use large files whenever possible.
Disks are poor at servicing a large number of seek requests.
Check that you're making best use of a computer's memory hierarchy, i.e.:
- Think about locality of reference.
- Go to main memory as infrequently as possible.
- Go to disk as infrequently as possible as possible.
Check that your are still using the right tools if your data grows.

Difference between revisions of "Data"