Sid Anand's guest appearances

Sid Anand

The InfoQ Podcast

THE ARTISTS ( indie filmmakers podcast)

Adrian Cockcroft

Greg Murphy

Grady Booch

Marcus Aurelius

Bryan Liles

Lin Clark

Robert Waggoner

Cathy O'Neil

Buster Keaton

Thomas Edison

Mike Lee

Michael Feathers

Arun Gupta

Anastasiia Voitova

David Wells

OwlTail

Sid Anand Kumar is a Producer with SAREGAMA Yoodlee films.  Some of the pointers of the conversation 1) Creating content/ films in the era of content proliferation 2) Who is the audience? 3) Parameters of structuring your content 4) A filmmaker should have an inherent need to reach a wide audience 5) Parameters of studios like Yoodlee. 6) Where are the films that once were in Cannes etc- could they be monetized? 7) Don't emulate the masters- like Ray... it's a different era. 8) Yoodlee 1.0 to 3.0 ADD YOURSELF TO OUR WHATS APP GROUP- 9324431451 You can follow us and leave us feedback on Facebook, Instagram, and Twitter @eplogmedia, For partnerships/queries send you can send us an email at bonjour@eplog.media. Music- "Hard Boiled" by Kevin MacLeod (https://incompetech.com)Licence: CC BY (http://creativecommons.org/licenses/by/4.0/ Vector Image- FREEPIK.COM DISCLAIMER: The views expressed on all the shows produced and distributed by Ep.Log Media are personal to the host and the guest of the shows respectively and with no intention to harm the sentiments of any individual/organization.See omnystudio.com/listener for privacy information.

Sid Anand Kumar is a Producer with SAREGAMA Yoodlee films.  Some of the pointers of the conversation 1) Creating content/ films in the era of content proliferation 2) Who is the audience? 3) Parameters of structuring your content 4) A filmmaker should have an inherent need to reach a wide audience 5) Parameters of studios like Yoodlee. 6) Where are the films that once were in Cannes etc- could they be monetized? 7) Don't emulate the masters- like Ray... it's a different era. 8) Yoodlee 1.0 to 3.0 ADD YOURSELF TO OUR WHATS APP GROUP- 9324431451 You can follow us and leave us feedback on <a href="https://facebook.com/eplogmedia" rel="nofollow" target="_blank">Facebook</a>, <a href="https://instagram.com/eplogmedia" rel="nofollow" target="_blank">Instagram</a>, and <a href="https://twitter.com/eplogmedia" rel="nofollow" target="_blank">Twitter</a> @eplogmedia, For partnerships/queries send you can send us an email at <a href="mailto:bonjour@eplog.media">bonjour@eplog.media</a>. Music- "Hard Boiled" by Kevin MacLeod (<a href="https://incompetech.com" rel="nofollow" target="_blank">https://incompetech.com</a>) Licence: CC BY (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow" target="_blank">http://creativecommons.org/licenses/by/4.0/</a> Vector Image- <a href="http://FREEPIK.COM" rel="nofollow" target="_blank">FREEPIK.COM</a> DISCLAIMER: The views expressed on all the shows produced and distributed by Ep.Log Media are personal to the host and the guest of the shows respectively and with no intention to harm the sentiments of any individual/organization.See <a href="https://omnystudio.com/listener" rel="nofollow" target="_blank">omnystudio.com/listener</a> for privacy information.

EP 24: Adapt or Die? In the era of content proliferation . FEAT: SID ANAND KUMAR

Stephanie Yee

Matei Zaharia

In this podcast, recorded live at QCon.ai, Principal Technical Advisor &amp; QCon Chair Wes Reisz and InfoQ Editor-in-chief Charles Humble chair a panel discussion with Stephanie Yee, data scientist at StitchFix, Matei Zaharia, professor of computer science at Stanford and chief scientist at Data Bricks, Sid Anand, chief data engineer at PayPal, and Soups Ranjan, director of data science at CoinBase.Why listen to this podcast: - Before you start putting a data science team together make sure you have a business goal or question that you want to answer;  If you have a specific question, like increasing lift on a metric, or understanding  customer usage patterns, you know where you can get the data from, and you can then figure out how to organise that data. - You need to make sure you have the right culture for the team - and find people who are excited about solving the business problems and be interested in it.  Also look at the environment you are going to provide.  - Your first hire shouldn’t be a data scientist (or quant).  You need support to productionise the models - and if you don’t have a colleague to help productionise it then don’t hire the quant first. - Given the scarcity of talent it is worth remembering that Data Scientists come from a variety of different backgrounds - Some people have computer science backgrounds, some may be astrophysicists or neuroscientists who approach problems in different ways. - There are two common ways to structure a data science team: one is a vertical team that does everything, the other, more common in large companies, is when you have a separate data science team and an infrastructure team.More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2Jym1RIYou can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVqSubscribe: www.youtube.com/infoqLike InfoQ on Facebook: bit.ly/2jmlyG8Follow on Twitter: twitter.com/InfoQFollow on LinkedIn: www.linkedin.com/company/infoqCheck the landing page on InfoQ: https://bit.ly/2Jym1RI

In this podcast, recorded live at <a href="http://QCon.ai" rel="nofollow" target="_blank">QCon.ai</a>, Principal Technical Advisor &amp; QCon Chair Wes Reisz and InfoQ Editor-in-chief Charles Humble chair a panel discussion with Stephanie Yee, data scientist at StitchFix, Matei Zaharia, professor of computer science at Stanford and chief scientist at Data Bricks, Sid Anand, chief data engineer at PayPal, and Soups Ranjan, director of data science at CoinBase. Why listen to this podcast: - Before you start putting a data science team together make sure you have a business goal or question that you want to answer;  If you have a specific question, like increasing lift on a metric, or understanding  customer usage patterns, you know where you can get the data from, and you can then figure out how to organise that data. - You need to make sure you have the right culture for the team - and find people who are excited about solving the business problems and be interested in it.  Also look at the environment you are going to provide.  - Your first hire shouldn’t be a data scientist (or quant).  You need support to productionise the models - and if you don’t have a colleague to help productionise it then don’t hire the quant first. - Given the scarcity of talent it is worth remembering that Data Scientists come from a variety of different backgrounds - Some people have computer science backgrounds, some may be astrophysicists or neuroscientists who approach problems in different ways. - There are two common ways to structure a data science team: one is a vertical team that does everything, the other, more common in large companies, is when you have a separate data science team and an infrastructure team. More on this: Quick scan our curated show notes on InfoQ <a href="https://bit.ly/2Jym1RI" rel="nofollow" target="_blank">https://bit.ly/2Jym1RI</a> You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. <a href="http://bit.ly/24x3IVq" rel="nofollow" target="_blank">bit.ly/24x3IVq</a> Subscribe: <a href="http://www.youtube.com/infoq" rel="nofollow" target="_blank">www.youtube.com/infoq</a> Like InfoQ on Facebook: <a href="http://bit.ly/2jmlyG8" rel="nofollow" target="_blank">bit.ly/2jmlyG8</a> Follow on Twitter: <a href="http://twitter.com/InfoQ" rel="nofollow" target="_blank">twitter.com/InfoQ</a> Follow on LinkedIn: <a href="http://www.linkedin.com/company/infoq" rel="nofollow" target="_blank">www.linkedin.com/company/infoq</a> Check the landing page on InfoQ: <a href="https://bit.ly/2Jym1RI" rel="nofollow" target="_blank">https://bit.ly/2Jym1RI</a>

Building a Data Science Capability with Stephanie Yee, Matei Zaharia, Sid Anand and Soups Ranjan

Wesley Reisz talks to Sid Anand, a data architect at cybersecurity company Agari, about building cloud-native data pipelines. The focus of their discussion is around a solution Agari uses that is built from Amazon Kinesis Streams, serverless functions, and auto scaling groups.Sid Anand is an architect at Agari, and a former technical architect at eBay, Netflix, and LinkedIn.  He has 15 years of data infrastructure experience at scale, is a PMC for Apache Airflow, and is also a program committee chair for QCon San Francisco and QCon London.Why listen to this podcast - Real-time data pipeline processing is very latency sensitive - Micro-batching allows much smaller amounts of data to be processed - Use the appropriate data store (or stores) to support the use of the dataIngesting data quickly into a clean database with minimal indexes can be fast - Communicate using a messaging system that supports schema evolutionMore on this: Quick scan our curated show notes on InfoQ http://bit.ly/2rJU9nBYou can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVqSubscribe: www.youtube.com/infoqLike InfoQ on Facebook: bit.ly/2jmlyG8Follow on Twitter: twitter.com/InfoQFollow on LinkedIn: www.linkedin.com/company/infoqWant to see extented shownotes? Check the landing page on InfoQ: http://bit.ly/2rJU9nB

Wesley Reisz talks to Sid Anand, a data architect at cybersecurity company Agari, about building cloud-native data pipelines. The focus of their discussion is around a solution Agari uses that is built from Amazon Kinesis Streams, serverless functions, and auto scaling groups. Sid Anand is an architect at Agari, and a former technical architect at eBay, Netflix, and LinkedIn. He has 15 years of data infrastructure experience at scale, is a PMC for Apache Airflow, and is also a program committee chair for QCon San Francisco and QCon London. Why listen to this podcast - Real-time data pipeline processing is very latency sensitive - Micro-batching allows much smaller amounts of data to be processed - Use the appropriate data store (or stores) to support the use of the dataIngesting data quickly into a clean database with minimal indexes can be fast - Communicate using a messaging system that supports schema evolution More on this: Quick scan our curated show notes on InfoQ <a href="http://bit.ly/2rJU9nB" rel="nofollow" target="_blank">http://bit.ly/2rJU9nB</a> You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. <a href="http://bit.ly/24x3IVq" rel="nofollow" target="_blank">bit.ly/24x3IVq</a> Subscribe: <a href="http://www.youtube.com/infoq" rel="nofollow" target="_blank">www.youtube.com/infoq</a> Like InfoQ on Facebook: <a href="http://bit.ly/2jmlyG8" rel="nofollow" target="_blank">bit.ly/2jmlyG8</a> Follow on Twitter: <a href="http://twitter.com/InfoQ" rel="nofollow" target="_blank">twitter.com/InfoQ</a> Follow on LinkedIn: <a href="http://www.linkedin.com/company/infoq" rel="nofollow" target="_blank">www.linkedin.com/company/infoq</a> Want to see extented shownotes? Check the landing page on InfoQ: <a href="http://bit.ly/2rJU9nB" rel="nofollow" target="_blank">http://bit.ly/2rJU9nB</a>

Sid Anand on Building Agari’s Cloud-native Data Pipelines with AWS Kinesis and Serverless