Sunday, March 26, 2017

Jyotilinga Shlok

मुझे आज भी यह सब याद है क्यों कि रोज सवेरे माँ को यह जपते सुनता रहा
जब तक वह रहीं या मैं  उनके साथ रहा

सौराष्ट्रे सोमनाथे श्रीशैले मल्लिकार्जुनम।
उज्जयिन्याम महाकालं - ओमकारं - अमलेश्वरं ॥

परल्याम वैद्यनाथं च  डाकिन्याम भीमशंकरं ।
सेतुबन्धे तु रामेशं नागेशं  दारुकावने ॥

वाराणस्यां तु विश्वेशं त्रयम्बकं  गोमतीतटे ।
हिमालये तु केदारं घुश्मेशं शिवालये ॥

एतानि  ज्योतिलिंगानि सायंप्राति पठेन्नर: । 
सप्तजन्मकृतं पापं स्मरणं विनश्यति ॥

एतेशां दर्शनादेव पातकं नैव तिष्ठति । 
कर्मक्षयो भवेत्तस्य यस्य  तुष्टो महेश्वराः ॥ 

Thursday, February 2, 2017

buy Stock in Indian Market

here is my reco list

1 YES BANK
2 Infosys
3 HCL Tech
4 LIC Housing Fin
5 Reliance
6 Tata Motors
7 IGL
8 NTPC
9 Ashok Leyland
10 Zee Entertain

Wednesday, February 1, 2017

my favourite quotes



1. “A goal is not always meant to be reached, it often serves simply as something to aim at.” – Bruce Lee


2. “You are never too old to set a new goal or to dream a new dream.” - C.S. Lewis

3. “Never give up. Today is hard, tomorrow will be worse, but the day after tomorrow will be sunshine.” - Jack Ma

4. “You don’t learn to walk by following the rules. You learn by doing, and falling over.” - Richard Branson

5. “The people who are crazy enough to think they can change the world are the ones who do.” - Steve Jobs

6. “If something is important enough, even if the odds are against you, you should still do it.” - Elon Musk

7. "If you're not stubborn, you'll give up on experiments too soon. And if you're not flexible, you'll pound your head against the wall and you won't see a different solution to a problem you're trying to solve." - Jeff Bezos

8. "The question I ask myself like almost every day is, 'Am I doing the most important thing I could be doing?'” - Mark Zuckerberg

9. “You can always find a solution if you try hard enough.” - Lori Greiner

10. “A goal without a timeline is just a dream.” - Robert Herjavec

11. “If you want to live a happy life, tie it to a goal, not to people or things.” - Albert Einstein

12. “I have not failed. I’ve just found 10,000 ways that won’t work.” - Thomas Edison

Tuesday, November 8, 2016

Bee on a flower


An 8 years old photo, wonder how they fly.  I fondly remember those days, when the photographing opportunities were in abundance..



Bee on a flower, originally uploaded by s_raghu20.
It was a photo hunting day for me, June in Switzerland, perfect time of year for spring photography.

And I found this bunch of bees hovering around this plant full of budding flowers. The colors from the flowers were great as it is, and the bees were giving the shots another dimension.

First time for me to shoot insects, though not in the macro sense. Still, I liked it very very much.

Saturday, July 23, 2016

Links to free big-data-sets



Many people who are starting their journey with big data and analytics find it hard to get their hands on the right kind of data to play or experiment with.

Most of the time, people have enthusiasm, they are learning the skill too, but they just don't have the right kind of dataset to apply their newly acquired skills.

Democratising data has been at the forefront of discussions for many data pioneers. Through their efforts and with some re-alignment of technology priorities, some government bodies have opened up their datasets to the public.

As a result, here is a set of links (reproduced) to some of the free sources.
  1. Data.gov http://data.gov The US Government pledged last year to make all government data available freely online. This site is the first stage and acts as a portal to all sorts of amazing information on everything from climate to crime. 
  2. US Census Bureau http://www.census.gov/data.html A wealth of information on the lives of US citizens covering population data, geographic data and education. 
  3. Socrata is another interesting place to explore government-related data, with some visualisation tools built-in. 
  4. European Union Open Data Portal http://open-data.europa.eu/en/data/ As the above, but based on data from European Union institutions. 
  5. Data.gov.uk http://data.gov.uk/ Data from the UK Government, including the British National Bibliography – metadata on all UK books and publications since 1950. 
  6. Canada Open Data is a pilot project with many government and geospatial datasets. 
  7. Datacatalogs.org offers open government data from US, EU, Canada, CKAN, and more. 
  8. The CIA World Factbook https://www.cia.gov/library/publications/the-world-factbook/Information on history, population, economy, government, infrastructure and military of 267 countries. 
  9. Healthdata.gov https://www.healthdata.gov/ 125 years of US healthcare data including claim-level Medicare data, epidemiology and population statistics. 
  10. NHS Health and Social Care Information Centre http://www.hscic.gov.uk/home Health data sets from the UK National Health Service. 
  11. UNICEF offers statistics on the situation of women and children worldwide. 
  12. World Health Organization offers world hunger, health, and disease statistics. 
  13. Amazon Web Services public datasets http://aws.amazon.com/datasets Huge resource of public data, including the 1000 Genome Project, an attempt to build the most comprehensive database of human genetic information and NASA ’s database of satellite imagery of Earth. 
  14. Facebook FB +0.32% Graph https://developers.facebook.com/docs/graph-api Although much of the information on users’ Facebook profile is private, a lot isn’t – Facebook provide the Graph API as a way of querying the huge amount of information that its users are happy to share with the world (or can’t hide because they haven’t worked out how the privacy settings work). 
  15. Face.com: A fascinating tool for facial recognition data. 
  16. UCLA makes some of the data from its courses public. 
  17. Data Market is a place to check out data related to economics, healthcare, food and agriculture, and the automotive industry. 
  18. Google Public data explorer includes data from world development indicators, OECD, and human development indicators, mostly related to economics data and the world. 
  19. Junar is a data scraping service that also includes data feeds. 
  20. Buzzdata is a social data sharing service that allows you to upload your own data and connect with others who are uploading their data. 
  21. Gapminder http://www.gapminder.org/data/ Compilation of data from sources including the World Health Organization and World Bank covering economic, medical and social statistics from around the world. 
  22. Google GOOGL +0.66% Trends http://www.google.com/trends/explore Statistics on search volume (as a proportion of total search) for any given term, since 2004. 
  23. Google Finance https://www.google.com/finance 40 years’ worth of stock market data, updated in real time. 
  24. Google Books Ngrams http://storage.googleapis.com/books/ngrams/books/datasetsv2.htmlSearch and analyze the full text of any of the millions of books digitised as part of the Google Books project. 
  25. National Climatic Data Center http://www.ncdc.noaa.gov/data-access/quick-links#loc-clim Huge collection of environmental, meteorological and climate data sets from the US National Climatic Data Center. The world’s largest archive of weather data. 
  26. DBPedia http://wiki.dbpedia.org Wikipedia is comprised of millions of pieces of data, structured and unstructured on every subject under the sun. DBPedia is an ambitious project to catalogue and create a public, freely distributable database allowing anyone to analyze this data. 
  27. New York Times http://developer.nytimes.com/docs  Searchable, indexed archive of news articles going back to 1851. 
  28. Freebase http://www.freebase.com/ A community-compiled database of structured data about people, places and things, with over 45 million entries. 
  29. Million Song Data Set http://aws.amazon.com/datasets/6468931156960467 Metadata on over a million songs and pieces of music. Part of Amazon Web Services. 
  30. UCI Machine Learning Repository is a dataset specifically pre-processed for machine learning. 
  31. Financial Data Finder at OSU offers a large catalog of financial data sets. 
  32. Pew Research Center offers its raw data from its fascinating research into American life. 
  33. The BROAD Institute offers a number of cancer-related datasets. 

Credit to Forbes article at

http://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#5b2a54cf6796

Friday, June 19, 2015

Teradata Data type abbreviation - described

Teradata data types (as reported in DBC.Columns.ColumnType can be cryptic and not always easy to remember.  Here's a ready reckoner - 

Abbreviation
Equivalent English :)
A1
ARRAY  
AN
MULTI-DIMENSIONAL ARRAY 
AT
TIME  
BF
BYTE  
BO
BLOB  
BV
VARBYTE  
CF
CHARACTER  
CO
CLOB  
CV
VARCHAR  
D
DECIMAL  
DA
DATE  
DH
INTERVAL DAY TO HOUR
DM
INTERVAL DAY TO MINUTE
DS
INTERVAL DAY TO SECOND
DY
INTERVAL DAY 
F
FLOAT  
HM
INTERVAL HOUR TO MINUTE
HS
INTERVAL HOUR TO SECOND
HR
INTERVAL HOUR 
I
INTEGER  
I1
BYTEINT  
I2
SMALLINT  
I8
BIGINT  
JN
JSON  
MI
INTERVAL MINUTE 
MO
INTERVAL MONTH 
MS
INTERVAL MINUTE TO SECOND
N
NUMBER  
PD
PERIOD(DATE)  
PM
PERIOD(TIMESTAMP WITH TIME ZONE)
PS
PERIOD(TIMESTAMP)  
PT
PERIOD(TIME)  
PZ
PERIOD(TIME WITH TIME ZONE)
SC
INTERVAL SECOND 
SZ
TIMESTAMP WITH TIME ZONE
TS
TIMESTAMP   
TZ
TIME WITH TIME ZONE
UT
UDT Type 
XM
XML  
YM
INTERVAL YEAR TO MONTH
YR
INTERVAL YEAR 
=++
TD_ANYTYPE  

Wednesday, May 6, 2015

Hadoop Meetup on the sidelines of Strata Hadoop Conference - Part 2

Read part 1 of this here

Day 2 of the meetup was equally exciting, if not better.  Lined up were talks from Qubit and Google, William Hill (a surprise for me - more later on that) and then PostCodeAnywhere, all very exciting from the synopsis.

Google & Qubit showcased basically a stream processing engine, with pluggable components, many of them can be written in different technologies and programming languages.

Of course Google Cloud Data flow is much more than just a stream processing engine, however, for real time data ingestion perspective, that feature is pretty significant.  

A completely managed system, it woks on the publish-subscribe (pub-sub) model.  As Reza put it, “pub-sub is not just data delivery mechanism, its used as a glue to hold the complete system together”.  Pluggable components is another differentiator for Google’s offering, in today’s demo they showcased bigtable as one of the consumers at the end.

From my own knowledge of stream processing, which is not significant in anyway, i could relate to many similarities with IBM’s info sphere streams and some with apache kafka.  However, a question around comparisons with these sites remained unanswered from Google (though in very good spirit, in a chat with the speaker Reza later on, it came out as more of a philosophical question avoidance than anything else).

The william hill talk (by Peter Morgan, their head of engineering), was a genuine surprise, at least for me.  Perhaps due to my ignorance, due to which i didn't realize, their systems are far more sophisticated and load bearing than I would have imagined.  As an example, they process 160TB of data through their systems on a daily basis.

Including many complexities managed through their system are their main components, the betting engine, the settlement engine among others. 

William Hill supports an open API as well, enabling app developers to pick up data elements and innovate. However, for obvious reasons, very limited data is thrown open in the public domain.  Would that be a deterrent for app developers ? not having enough data ?   For example, if i would want to report in an app, who’s betting on a  certain game, cross referenced with geo location data .. I cant do that, since William hill doesn't publish demographic data.  I personally feel alright with it, there are possibilities that many of those data elements can be used in ways to influence the betting system itself, becoming counter-productive.

I would imagine their IT systems to be one of the top notch systems around the place, to be able to manage such data volumes, with such speeds and accuracy. Commendable job.  I would probably write exclusively on their architecture once i get my hands on the presentation slides (couple of days may be).

The talk from PostCodeAnywhere was more educative to me, personally.  Got to understand a bit about Markov Models, chains and how they can be used for machine Learning.  Very interesting stuff there too.

Apache Spark is being seen more and more as the tool to be perform analytics on the fly, specially on large volumes of data.  It would be very interesting to see how R and python analytical capabilities compare with what spark offers.

Speaking to another attendee today, it came out the people prefer to use R more and more for massaging and cleansing purposes, however, its not seen as fit for heavy lifting required for performing real analytic and/or predictive pieces. For these areas, people still prefer to use Python.


IBM’s bigR is a possible contender for the job, where they talk about having optimised R for a hadoop cluster and have enabled it to work on top of hdfs.  However, bigR is not open source and that could be its biggest challenge in adoption.