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ETL (Formerly, ActiveWarehouse-ETL)

Note: This project is a work in progress: gradual modernization of activewarehouse-etl.

ETL is a Ruby Extract-Transform-Load (ETL) tool.

This tool is both usable and used in production under its current form – but be aware the project is under reorganization: a new team is shaping up and we’re working mostly on making it easier for people to contribute first. Up-to-date documentation will only come later.

Usage

The documentation is sparse and not everything is up to date, too, but here are useful bits to get you started:

  • read the Introduction
  • later on, refer to the RDoc (be sure to check out Processor and Transform)
  • read the source

If you’re lost, please ask questions on the Google Group and we’ll take care of it.

One thing to keep in mind is that ActiveWarehouse-ETL is highly hackable: you can pretty much create all you need with extra ruby code, even if it’s not currently supported.

Compatibility

Current code should work with any combination of Rails 2, Rails 3, Ruby 1.8.7, Ruby 1.9.2, MySQL and Postgresql. If you meet any issue, drop a line on the Google Group and/or create an issue on github.

Contributing

Fork on GitHub and after you’ve committed tested patches, send a pull request.

If you meet any error while trying to run the tests, or any failure, please drop a line on the Google Group.

Pre-requisites to running the tests

  • install RVM and Bundler
  • install MySQL and/or Postgresql (you can use brew for that)
  • create test/config/database.mysql.yml and test/config/database.postgresql.yml based on test/config/database.example.yml
  • create databases ‘etl_unittest’ and ‘etl_unittest_execution’ in each database, with access to the user given in the yml files

If you don’t install both MySQL and Postgresql, edit test/config/common.rb to comment out either ‘mysql’ or ‘pg’, or the test task will raise errors.

Run the tests

You can run the tests on a combination of environments using:

  rake test:matrix

Contributors

ActiveWarehouse-ETL is the work of many people since late 2006 – here is a list, in no particular order:

  • Anthony Eden
  • Chris DiMartino
  • Darrell Fuhriman
  • Fabien Carrion
  • Jacob Maine
  • James B. Byrne
  • Jay Zeschin
  • Jeremy Lecour
  • Steve Meyfroidt
  • Seth Ladd
  • Thibaut Barrère
  • Stephen Touset
  • sasikumargn
  • Andrew Kuklewicz
  • Leif Gustafson
  • Andrew Sodt
  • Tyler Kiley
  • Colman Nady
  • Scott Gonyea

If your name should be on the list but isn’t, please leave a comment!

Features

Currently supported features:

  • ETL Domain Specific Language (DSL) – Control files are specified in a Ruby-based DSL
  • Multiple source types. Current supported types:
    • Fixed-width and delimited text files
    • XML files through SAX
    • Apache combined log format
  • Multiple destination types – file and database destinations
  • Support for extracting from multiple sources in a single job
  • Support for writing to multiple destinations in a single job
  • A variety of built-in transformations are included:
    • Date-to-string, string-to-date, string-to-datetime, string-to-timestamp
    • Type transformation supporting strings, integers, floats and big decimals
    • Trim
    • SHA-1
    • Decode from an external decode file
    • Default replacement for empty values
    • Ordinalize
    • Hierarchy lookup
    • Foreign key lookup
    • Ruby blocks
    • Any custom transformation class
  • A variety of build-in row-level processors
    • Check exists processor to determine if the record already exists in the destination database
    • Check unique processor to determine whether a matching record was processed during this job execution
    • Copy field
    • Rename field
    • Hierarchy exploder which takes a tree structure defined through a parent id and explodes it into a hierarchy bridge table
    • Surrogate key generator including support for looking up the last surrogate key from the target table using a custom query
    • Sequence generator including support for context-sensitive sequences where the context can be defined as a combination of fields from the source data
    • New row-level processors can easily be defined and applied
  • Pre-processing
    • Truncate processor
  • Post-processing
    • Bulk import using native RDBMS bulk loader tools
  • Virtual fields – Add a field to the destination data which doesn’t exist in the source data
  • Built in job and record meta data
  • Support for type 1 and type 2 slowly changing dimensions
    • Automated effective date and end date time stamping for type 2
    • CRC checking