Is Python going to be better than R for Big Data Analytics and Data Science? #rstats #python

Uptil now the R ecosystem of package developers has mostly shrugged away the Big Data question. In   a fascinating insight Hadley Wickham said this in a recent interview- shockingly it mimicks the FUD you know who has been accused of ( source

5. How do you respond when you hear the phrase ‘big data’? Big data is extremely overhyped and not terribly well defined. Many people think they have big data, when they actually don’t.

I think there are two particularly important transition points:

* From in-memory to disk. If your data fits in memory, it’s small data. And these days you can get 1 TB of ram, so even small data is big!

* From one computer to many computers.

R is a fantastic environment for the rapid exploration of in-memory data, but there’s no elegant way to scale it to much larger datasets. Hadoop works well when you have thousands of computers, but is incredible slow on just one machine. Fortunately, I don’t think one system needs to solve all big data problems.

To me there are three main classes of problem:

1. Big data problems that are actually small data problems, once you have the right subset/sample/summary.

2. Big data problems that are actually lots and lots of small data problems

3. Finally, there are irretrievably big problems where you do need all the data, perhaps because you fitting a complex model. An example of this type of problem is recommender systems

Ajay- One of the reasons of non development of R Big Data packages is- it takes money. The private sector in R ecosystem is a duopoly ( Revolution Analytics ( acquired by Microsoft) and RStudio (created by Microsoft Alum JJ Allaire). Since RStudio actively tries as a company to NOT step into areas Revolution Analytics works in- it has not ventured into Big Data in my opinion for strategic reasons.

Revolution Analytics project on RHadoop is actually just one consultant working on it here and it has not been updated since six months

We interviewed the creator of R Hadoop here

However Python developers have been trying to actually develop systems for Big Data actively. The Hadoop ecosystem and the Python ecosystem are much more FOSS friendly even in enterprise solutions.

This is where Python is innovating over R in Big Data-

  • Blaze: Translates NumPy/Pandas-like syntax to systems like databases.

    Blaze presents a pleasant and familiar interface to us regardless of what computational solution or database we use. It mediates our interaction with files, data structures, and databases, optimizing and translating our query as appropriate to provide a smooth and interactive session.

  • Odo: Migrates data between formats.

    Odo moves data between formats (CSV, JSON, databases) and locations (local, remote, HDFS) efficiently and robustly with a dead-simple interface by leveraging a sophisticated and extensible network of conversions.

    odo takes two arguments, a target and a source for a data transfer.

    >>> from odo import odo
    >>> odo(source, target)  # load source into target 
  • Dask.array: Multi-core / on-disk NumPy arrays

    Dask.arrays provide blocked algorithms on top of NumPy to handle larger-than-memory arrays and to leverage multiple cores. They are a drop-in replacement for a commonly used subset of NumPy algorithms.

  • DyND: In-memory dynamic arrays

    DyND is a dynamic ND-array library like NumPy. It supports variable length strings, ragged arrays, and GPUs. It is a standalone C++ codebase with Python bindings. Generally it is more extensible than NumPy but also less mature.

    The core DyND developer team consists of Mark Wiebe and Irwin Zaid. Much of the funding that made this project possible came through Continuum Analytics and DARPA-BAA-12-38, part of XDATA.

    LibDyND, a component of the Blaze project, is a C++ library for dynamic, multidimensional arrays. It is inspired by NumPy, the Python array programming library at the core of the scientific Python stack, but tries to address a number of obstacles encountered by some of its users. Examples of this are support for variable-sized string and ragged array types. The library is in a preview development state, and can be thought of as a sandbox where features are being tried and tweaked to gain experience with them.

    C++ is a first-class target of the library, the intent is that all its features should be easily usable in the language. This has many benefits, such as that development within LibDyND using its own components is more natural than in a library designed primarily for embedding in another language.

    This library is being actively developed together with its Python bindings,

On a single machine dask increases the scale of comfortable data from fits-in-memory to fits-on-diskby intelligently streaming data from disk and by leveraging all the cores of a modern CPU.

Users interact with dask either by making graphs directly or through the dask collections which provide larger-than-memory counterparts to existing popular libraries:

  • dask.array = numpy + threading
  • dask.bag = map, filter, toolz + multiprocessing
  • dask.dataframe = pandas + threading

Dask primarily targets parallel computations that run on a single machine. It integrates nicely with the existing PyData ecosystem and is trivial to setup and use:

conda install dask
pip install dask

When open source fights- closed source wins. When the Jedi fight the Sith Lords will win

So will R people rise to the Big Data challenge or will they bury their heads in sands like an ostrich or a kiwi. Will Python people learn from R design philosophies and try and incorporate more of it without redesigning the wheel

Converting code from one language to another automatically?

How I wish there was some kind of automated conversion tool – that would convert a CRAN R package into a standard Python package which is pip installable

Machine learning for more machine learning anyone?

Things I wonder about

  1. Why humans need one set of accommodation to live in, another to work in, and a third to relax in. It seems we are using three times the number of buildings we should be using.
  2. Why can’t analytics measure the cost to environment (not just carbon output) in any product and service?
  3. What prevents a global effort for analytics  against corruption ?
  4. Why open source software underestimates the need of marketing and why proprietary software companies underestimate the need for open sourcing at least a small part of their extensive portfolio?
  5. Why is education and training still so expensive in the era of MOOCs and Internet and Skype?
  6. Why are expensive textbooks (and books and newspapers) still being printed on paper?
  7. Why does it take 15 minutes to set up the projector before any presentation despite the advances in technology?
  8. Why can’t I just 3D print most of my wardrobe and my gadgets?
  9. When will we have virtual reality movies?
  10. Why software companies focus on creating more and more languages, rather than use machine learning to create a language 1 to language 2 translator. How about a Google/Bing Translate for Computer Languages?
  11. Why they do a lot of checking for giving me a credit card but not so much checking for giving me a gun in the USA? Why do 2 billion Indians and Chinese put up with corruption ? Why do Europeans work so few hours and Asians so many?
  12. Why people who write packages in open source make less money than people who write apps for mobiles?
  13. When can software startups  focus on job search and dating search as the real problems humans care for- not just website search?
  14. Why is there a digital divide and what a donation of 1000,000 phablets in poor countries to kids can do for the future?
  15. When will we start consuming smarter rather than just less or more to heal climate change?

But mostly I am thinking of this?  aYpYmWV_700b1-560x559Happy New Year. Stay Awesome and Classy

Writing for

I have been writing freelance for

Its a great learning for me to be a better writer especially for analytics and programming

These are a list of articles -interviews are in bold and I will keep updating this list when there are new additions

  1. Interview: Ingo Mierswa, RapidMiner CEO on “Predaction” and Key Turning Points June 2014

  2. Guide to Data Science Cheat Sheets 2014/05/12

  3. Book Review: Data Just Right 2014/04/03
  4. Exclusive Interview: Richard Socher, founder of etcML, Easy Text Classification Startup 2014/03/31
  5. Trifacta – Tackling Data Wrangling with Automation and Machine Learning 2014/03/17
  6. Paxata automates Data Preparation for Big Data Analytics 2014/03/07
  7. etcML Promises to Make Text Classification Easy  2014/03/05
  8. Wolfram Breakthrough Knowledge-based Programming Language – what it means for Data Science? 2014/03/02