DataFlows is a simple and intuitive way of building data processing flows.
- It's built for small-to-medium-data processing - data that fits on your hard drive, but is too big to load in Excel or as-is into Python, and not big enough to require spinning up a Hadoop cluster...
- It's built upon the foundation of the Frictionless Data project - which means that all data produced by these flows is easily reusable by others.
- It's a pattern not a heavy-weight framework: if you already have a bunch of download and extract scripts this will be a natural fit
Read more in the Features section below.
Install dataflows via pip install.
(If you are using minimal UNIX OS, run first sudo apt install build-essential)
Then use the command-line interface to bootstrap a basic processing script for any remote data file:
# Install from PyPi $ pip install dataflows # Inspect a remote CSV file $ dataflows init https://raw.githubusercontent.com/datahq/dataflows/master/data/academy.csv Writing processing code into academy_csv.py Running academy_csv.py academy: # Year Ceremony Award Winner Name Film (string) (integer) (string) (string) (string) (string) ---- ---------- ----------- -------------------------------- ---------- ------------------------------ ------------------- 1 1927/1928 1 Actor Richard Barthelmess The Noose 2 1927/1928 1 Actor 1 Emil Jannings The Last Command 3 1927/1928 1 Actress Louise Dresser A Ship Comes In 4 1927/1928 1 Actress 1 Janet Gaynor 7th Heaven 5 1927/1928 1 Actress Gloria Swanson Sadie Thompson 6 1927/1928 1 Art Direction Rochus Gliese Sunrise 7 1927/1928 1 Art Direction 1 William Cameron Menzies The Dove; Tempest ... # dataflows create a local package of the data and a reusable processing script which you can tinker with $ tree . ├── academy_csv │ ├── academy.csv │ └── datapackage.json └── academy_csv.py 1 directory, 3 files # Resulting 'Data Package' is super easy to use in Python [adam] ~/code/budgetkey-apps/budgetkey-app-main-page/tmp (master=) $ python Python 3.6.1 (default, Mar 27 2017, 00:25:54) [GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)] on darwin Type "help", "copyright", "credits" or "license"for more information. >>> from datapackage import Package >>> pkg = Package('academy_csv/datapackage.json') >>> it = pkg.resources[0].iter(keyed=True) >>> next(it){'Year': '1927/1928', 'Ceremony': 1, 'Award': 'Actor', 'Winner': None, 'Name': 'Richard Barthelmess', 'Film': 'The Noose'} >>> next(it){'Year': '1927/1928', 'Ceremony': 1, 'Award': 'Actor', 'Winner': '1', 'Name': 'Emil Jannings', 'Film': 'The Last Command'} # You now run `academy_csv.py` to repeat the process# And obviously modify it to add data modification steps- Trivial to get started and easy to scale up
- Set up and run from command line in seconds ...
dataflows init=>flow.pypython flow.py
- Validate input (and esp source) quickly (non-zero length, right structure, etc.)
- Supports caching data from source and even between steps
- so that we can run and test quickly (retrieving is slow)
- Immediate test is run: and look at output ...
- Log, debug, rerun
- Degrades to simple python
- Conventions over configuration
- Log exceptions and / or terminate
- The input to each stage is a Data Package or Data Resource (not a previous task)
- Data package based and compatible
- Processors can be a function (or a class) processing row-by-row, resource-by-resource or a full package
- A pre-existing decent contrib library of Readers (Collectors) and Processors and Writers
Dive into the Tutorial to get a deeper glimpse into everything that dataflows can do. Also review this list of Built-in Processors, which also includes an API reference for each one of them.
