GIDS 2016 – Azure Data Lakes and Optimizing MEAN Stack


Last week had a realy good time at GIDS 2016 (Great Indian Developer Summit) at Bangalore, JN Tata Auditorium. I was one of the speakers, but highlight of event was the presence of none other but Douglas crockford himself.

I delivered 2 sessions, the first one was about "Performance optimization of MEAN Stack" , a very dear topic to me as I have spent a lot of time with NodeJS and MongoDB and actually had the issues first hand that I spoke about in my session, Mongo is not a secret sauce that may just help your application perform, its like any other DB when it experiences load and I had some really good tips to share about this. The solution I used is already at the Github. Later I had some pretty hearty disucssion with some people from audience about sharding techniques of MongoDB and how to chose the right sharding keys.

The Next session was about "Leveraging Data Lake for your Big Data", Now Azure Data Lake is still in preview and till 2 weeks back I just knew about the product but didn't had access to it, So I shot an email to Saveen Reddy and Alan Tam and finally got access in just a couple of days. Data lake for me is a product that just demoratizes the big data market, it has all the components that make it good enough to work with hadoop, hdfs, sqoop, pig, hive kind of big data products yet it has the capabilities that a person who knows nothing about big data may yet dump in huge amounts of data to the data lake store and yet run easy to understand U-SQL queries. Most from my audience were just thrilled to see how easy could it be to write an agreegate query just like SQL and yet not need to learn what goes in the background and how much more easier is to control the degree of parallelism of the jobs that you are running. The sample I used is also on My Github Page.

I am sure the ppt decks will be available in a few days at the GIDS 2016 website, until next year...


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