Environment setup for big data analytics

This articles covers basic tools and technologies to use when conducting the first steps on big data analysis.

Linux as the base OS
For basic data processing:
- Bash shell: environment for running multiple command-line Linux tools for data manipulation
  - Comes pre-installed with Linux; check Reference manual for usage
  - Bash might be not the default Linux shell, see how to switch to it
  - Learn Bash by examples
  - Most important Linux Commands and phenomenons to master for data manipulation:
    - cat - output/concetanate a stream / file
    - head, tail - show the head or the tail of a stream / file
    - grep - to filter streams / lines of a file
    - sed - to manipulate streams / lines of a file
    - Regular expressions - used in a wide set of Linux tools
      - Explanation and tutorials
- AWK - simple data reformatter with compact coding features
  - The GNU Awk User’s Guide
  - AWK Tutorials
- Python - easy to learn, effective programming language with a huge amount of libraries available for various tasks. Great for data manipulation used from the command line.
And the big data analysis framework chosen based on the type of data analyzed. For the first step tutorials our suggestion would be:
- Hadoop, single cluster setup (can be downloaded pre-installed to a virtual appliance)
- Java based MapReduce programs
- Pig MapReduce query language