Walter Menendez's guest appearances

Walter Menendez

MIT Comparative Media Studies/Writing

Data Engineering Podcast

Kevin Young

Cameron Esposito

Jane McGonigal

Lynn Nottage

Toni Morrison

John Jennings

Chris Claremont

Scott McCloud

John Hodgman

Scott Nicholson

Nikita Shamgunov

Christopher Ryan

Denis Dyack

Aparna Nancherla

James Gleick

OwlTail

Summary Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions. Preamble  Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed  Interview  Introduction How did you get involved in the area of data management? How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for? What are some of the types of data inputs and outputs that you work with at Buzzfeed? Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision? What does the architecture of your data platform look like and what are some of the most significant areas of technical debt? Which platforms and languages are most widely leveraged in your team and what are some of the outliers? What are some of the most significant challenges that you face, both technically and organizationally? What are some of the dead ends that you have run into or failed projects that you have tried? What has been the most successful project that you have completed and how do you measure that success?  Contact Info  @hackwalter on Twitter walterm on GitHub  Links  Data Literacy MIT Media Lab Tumblr Data Capital Data Infrastructure Google Analytics Datadog Python Numpy SciPy NLTK Go Language NSQ Tornado PySpark AWS EMR Redshift Tracking Pixel Google Cloud Don’t try to be google Stop Hiring DevOps Engineers and Start Growing Them  The intro and outro music is from The Hug by The Freak Fandango Orchestra  / CC BY-SASupport Data Engineering Podcast

<h2>Summary</h2> <p>Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.</p> <h2>Preamble</h2> <ul> <li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li> <li>When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at <a href="https://www.dataengineeringpodcast.com/linode?utm_source=rss&amp;utm_medium=rss" rel="nofollow" target="_blank">dataengineeringpodcast.com/linode</a> and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.</li> <li>Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to <a href="https://www.dataengineeringpodcast.com/gocd?utm_source=rss&amp;utm_medium=rss" rel="nofollow" target="_blank">dataengineeringpodcast.com/gocd</a> to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind.</li> <li>Go to <a href="https://www.dataengineeringpodcast.com?utm_source=rss&amp;utm_medium=rss" rel="nofollow" target="_blank">dataengineeringpodcast.com</a> to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.</li> <li>You can help support the show by checking out the <a href="https://www.patreon.com/dataengineeringpodcast?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Patreon page</a> which is linked from the site.</li> <li>To help other people find the show you can leave a review on <a href="https://itunes.apple.com/us/podcast/data-engineering-podcast/id1193040557?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">iTunes</a>, or <a href="https://goo.gl/app/playmusic?ibi=com.google.PlayMusic&amp;isi=691797987&amp;ius=googleplaymusic&amp;link=https://play.google.com/music/m/Ivucmwlkrxbgfe5dhj562xgev4u?t%3DData_Engineering_Podcast%26pcampaignid%3DMKT-na-all-co-pr-mu-pod-16&amp;utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Google Play Music</a>, and tell your friends and co-workers</li> <li>Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed</li> </ul> <h2>Interview</h2> <ul> <li>Introduction</li> <li>How did you get involved in the area of data management?</li> <li>How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for?</li> <li>What are some of the types of data inputs and outputs that you work with at Buzzfeed?</li> <li>Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision?</li> <li>What does the architecture of your data platform look like and what are some of the most significant areas of technical debt?</li> <li>Which platforms and languages are most widely leveraged in your team and what are some of the outliers?</li> <li>What are some of the most significant challenges that you face, both technically and organizationally?</li> <li>What are some of the dead ends that you have run into or failed projects that you have tried?</li> <li>What has been the most successful project that you have completed and how do you measure that success?</li> </ul> <h2>Contact Info</h2> <ul> <li><a href="https://twitter.com/hackwalter?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">@hackwalter</a> on Twitter</li> <li><a href="https://github.com/walterm?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">walterm</a> on GitHub</li> </ul> <h2>Links</h2> <ul> <li><a href="https://en.wikipedia.org/wiki/Data_literacy?wprov=sfla1&amp;utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Data Literacy</a></li> <li><a href="https://www.media.mit.edu?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">MIT Media Lab</a></li> <li><a href="http://tumblr.com/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Tumblr</a></li> <li><a href="http://files.technologyreview.com/whitepapers/MIT_Oracle+Report-The_Rise_of_Data_Capital.pdf?_ga=2.254527562.318320580.1510665568-821581318.1510665568&amp;utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Data Capital</a></li> <li><a href="https://theodi.org/what-is-data-infrastructure?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Data Infrastructure</a></li> <li><a href="https://www.google.com/analytics/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Google Analytics</a></li> <li><a href="https://www.datadoghq.com/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Datadog</a></li> <li><a href="https://www.python.org/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Python</a></li> <li><a href="http://www.numpy.org/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Numpy</a></li> <li><a href="https://scipy.org/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">SciPy</a></li> <li><a href="http://www.nltk.org/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">NLTK</a></li> <li><a href="http://golang.org/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Go Language</a></li> <li><a href="https://github.com/nsqio/nsq?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">NSQ</a></li> <li><a href="http://www.tornadoweb.org/en/stable/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Tornado</a></li> <li><a href="http://spark.apache.org/docs/2.2.0/api/python/pyspark.html?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">PySpark</a></li> <li><a href="https://aws.amazon.com/emr/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">AWS EMR</a></li> <li><a href="https://aws.amazon.com/redshift/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Redshift</a></li> <li><a href="https://en.wikipedia.org/wiki/Web_beacon?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Tracking Pixel</a></li> <li><a href="https://cloud.google.com/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Google Cloud</a></li> <li><a href="https://www.linkedin.com/pulse/dont-try-google-just-deliver-what-you-promised-jamie-notter/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Don’t try to be google</a></li> <li><a href="https://www.youtube.com/watch?v=6m9nCtyn6kE&amp;utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">Stop Hiring DevOps Engineers and Start Growing Them</a></li> </ul> <p>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">The Freak Fandango Orchestra</a>  / <a href="http://creativecommons.org/licenses/by-sa/3.0/?utm_source=rss&amp;utm_medium=rss" rel="noopener nofollow" target="_blank">CC BY-SA</a></p><p><a href="https://dataengineering.supercast.com/" rel="payment nofollow" target="_blank">Support Data Engineering Podcast</a></p>

Buzzfeed Data Infrastructure with Walter Menendez - Episode 7

If you’ve heard of BuzzFeed, you probably think about our famous articles and quizzes, such as The Dress and Which State Are You Actually From?, as well as our video escapades, such as The Try Guys Try Sexy Halloween Costumes and our famous Watermelon Explosion experiment on Facebook Live. The success of our content might seem accidental, but as a result of BuzzFeed’s experimental approach to producing content, the virality of these posts is actually a very scientific and calculated effort. This talk details how BuzzFeed thinks about and creates content, highlighting our paradigms for the function and role of our content. Menendez also discusses the software stack that supports this experimental loop, as BuzzFeed also employs a variety of technologies to build an analytics layer. Included in that tech discussion is also an overview of the metrics and signals BuzzFeed is interested in once content is live. Along the way, Walter highlights some of the Comparative Media Studies learnings he employs on a daily basis to thrive in the BuzzFeed content ecosystem.Walter Menendez is a Senior Data Infrastructure Engineer at BuzzFeed, based in New York. He is an MIT alum of the class of 2015, having majored in Computer Science and Engineering (Course 6-3). While at MIT, he concentrated in Comparative Media Studies, as well as having done undergraduate research in various Media Lab groups (Fluid Interfaces, Laboratory for Social Machines). At BuzzFeed, he is responsible for the development and maintanence of all of BuzzFeed’s data collection, from on-site impression collection to data warehousing solutions, empowering the analytical approach that BuzzFeed uses for the content creation cycle.

If you’ve heard of BuzzFeed, you probably think about our famous articles and quizzes, such as The Dress and Which State Are You Actually From?, as well as our video escapades, such as The Try Guys Try Sexy Halloween Costumes and our famous Watermelon Explosion experiment on Facebook Live. The success of our content might seem accidental, but as a result of BuzzFeed’s experimental approach to producing content, the virality of these posts is actually a very scientific and calculated effort. This talk details how BuzzFeed thinks about and creates content, highlighting our paradigms for the function and role of our content. Menendez also discusses the software stack that supports this experimental loop, as BuzzFeed also employs a variety of technologies to build an analytics layer. Included in that tech discussion is also an overview of the metrics and signals BuzzFeed is interested in once content is live. Along the way, Walter highlights some of the Comparative Media Studies learnings he employs on a daily basis to thrive in the BuzzFeed content ecosystem.<br><br>Walter Menendez is a Senior Data Infrastructure Engineer at BuzzFeed, based in New York. He is an MIT alum of the class of 2015, having majored in Computer Science and Engineering (Course 6-3). While at MIT, he concentrated in Comparative Media Studies, as well as having done undergraduate research in various Media Lab groups (Fluid Interfaces, Laboratory for Social Machines). At BuzzFeed, he is responsible for the development and maintanence of all of BuzzFeed’s data collection, from on-site impression collection to data warehousing solutions, empowering the analytical approach that BuzzFeed uses for the content creation cycle.

Walter Menendez: "Engineering Virality: BuzzFeed’s Scientific Approach To Creating Content"