Category All Episodes

Cloud and Edge with Steve Herrod

http://traffic.libsyn.com/sedaily/2018_02_23_VMWareCTO.mp3Podcast: Play in new window | Download Steve Herrod led engineering at VMWare as the company scaled from 30 engineers to 3,000 engineers. After 11 years, he left to become a managing director for General Catalyst, a venture capital firm. Since he has both operating experience and a wide view of the technology landscape as an investor, he is well-equipped to discuss a topic that we have been covering on

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Serverless Systems with Eduardo Laureano

http://traffic.libsyn.com/sedaily/2018_02_22_MicrosoftServerless.mp3Podcast: Play in new window | Download On Software Engineering Daily, we have been covering the “serverless” movement in detail. For people who don’t use serverless functions, it seems like a niche. Serverless functions are stateless, auto-scaling, event driven blobs of code. You might say “serverless sounds kind of cool, but why don’t I just use a server? It’s a paradigm I’m used to.” Serverless is exciting not because of

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Cloud Foundry Overview with Mike Dalessio

http://traffic.libsyn.com/sedaily/2018_02_21_MikeDaLessio.mp3Podcast: Play in new window | Download Earlier this year we did several shows about Cloud Foundry, followed by several shows about Kubernetes. Both of these projects allow you to build scalable, multi-node applications–but they serve different types of users. Cloud Foundry encompasses a larger scope of the application experience than Kubernetes. Kubernetes is lower level, and is actually being used within newer versions of Cloud Foundry to give Cloud

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Kafka Design Patterns with Gwen Shapira

http://traffic.libsyn.com/sedaily/2018_02_20_GwenShapiro.mp3Podcast: Play in new window | Download Kafka is at the center of modern streaming systems. Kafka serves as a database, a pubsub system, a buffer, and a data recovery tool. It’s an extremely flexible tool, and that flexibility has led to its use as a platform for a wide variety of data intensive applications. Today’s guest is Gwen Shapira, a product manager at Confluent. Confluent is a company that

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Streaming Architecture with Ted Dunning

http://traffic.libsyn.com/sedaily/2018_02_19_TedDunning.mp3Podcast: Play in new window | Download Streaming architecture defines how large volumes of data make their way through an organization. Data is created at a user’s smartphone, or on a sensor inside of a conveyor belt at a factory. That data is sent to a set of backend services that aggregate the data, organizing it and making it available to business analysts, application developers, and machine learning algorithms. The

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Streaming Analytics with Scott Kidder

http://traffic.libsyn.com/sedaily/2018_02_16_FlinkandVideo.mp3Podcast: Play in new window | Download When you go to a website where a video is playing, and your video lags, how does the website know that you are having a bad experience? Problems with video are often not complete failures–maybe part of the video loads, and plays just fine, and then the rest of the video is buffering. You have probably experienced sitting in front of a video,

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Streaming Architecture with Tugdual Grall

http://traffic.libsyn.com/sedaily/2018_02_15_TugdualGraal.mp3Podcast: Play in new window | Download At a big enough scale, every software product produces lots of data. Whether you are building an advertising technology company, a social network, or a system for IoT devices, you have thousands of events coming in at a fast pace that you want to aggregate, study and act upon. For the last decade, engineers have been learning to store and process these vast

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Machine Learning Deployments with Kinnary Jangla

http://traffic.libsyn.com/sedaily/2018_02_14_ProductionMLSystems.mp3Podcast: Play in new window | Download Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests. Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices and Dockerized, which make the system easier to

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Box Kubernetes Migration with Sam Ghods

http://traffic.libsyn.com/sedaily/2018_02_13_BoxKubernetes.mp3Podcast: Play in new window | Download Over 12 years of engineering, Box has developed a complex architecture of services. Whenever a user uploads a file to Box, that upload might cause 5 or 6 different services to react to the event. Each of these services is managed by a set of servers, and managing all of these different servers is a challenge. Sam Ghods is the cofounder and services

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Scaling Box with Jeff Quiesser

http://traffic.libsyn.com/sedaily/2018_02_12_BoxEngineering.mp3Podcast: Play in new window | Download When Box started in 2006, the small engineering team had a lot to learn. Box was one of the earliest cloud storage companies, with a product that allowed companies to securely upload files to remote storage. This was two years before Amazon Web Services introduced on-demand infrastructure, so the Box team managed their own servers, which they learned how to do as they

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Google BeyondCorp with Max Saltonstall

http://traffic.libsyn.com/sedaily/2018_02_09_GoogleBeyondCorp.mp3Podcast: Play in new window | Download Employees often find themselves needing to do work outside of the office. Depending on the sensitivity of your task, accessing internal systems from a remote location may or may not be OK. If you are using a corporate application that shows the menu of your company’s cafe on your smartphone, your workload is less sensitive. If you are accessing the proprietary codebase of

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Load Testing Mobile Applications with Paulo Costa and Rodrigo Coutinho

http://traffic.libsyn.com/sedaily/2018_02_08_OutSystems.mp3Podcast: Play in new window | Download Applications need to be ready to scale in response to high-load events. With mobile applications, this can be even more important. People rely on mobile applications such as banking, ride sharing, and GPS. During Black Friday, a popular ecommerce application could be bombarded by user requests–you might not be able to complete a request to buy an item at the Black Friday discount.

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Tether, Ripple, and Blockchain Reporting with Matt Leising

http://traffic.libsyn.com/sedaily/2018_02_07_MattLeising.mp3Podcast: Play in new window | Download Your friends from college are asking you how to buy Bitcoin. Your mom is emailing you articles about the benefits of decentralized peer-to-peer networks. Your shoe shiner is telling you to buy XRP. It is 2018, and cryptocurrencies have become a daily part of news headlines. The general public may not understand how this technology works, but everyone knows that changes are on

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Serverless at the Edge with Kenton Varda

http://traffic.libsyn.com/sedaily/2018_02_06_CloudFlareWorkers.mp3Podcast: Play in new window | Download Over the last decade, computation and storage has moved from on-premise hardware into the cloud data center. Instead of having large servers “on premise,” companies started to outsource their server workloads to cloud service providers. At the same time, there has been a proliferation of devices at the “edge.” The most common edge device is your smartphone, but there are many other smart

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Linkedin Resilience with Bhaskaran Devaraj and Xiao Li

http://traffic.libsyn.com/sedaily/2018_02_05_LinkedinResilience.mp3Podcast: Play in new window | Download How do you build resilient, failure tested systems? Redundancy, backups, and testing are all important. But there is also an increasing trend towards chaos engineering–the technique of inducing controlled failures in order to prove that a system is fault tolerant in the way that you expect. In last week’s episode with Kolton Andrus, we discussed one way to build chaos engineering as a

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Chaos Engineering with Kolton Andrus

http://traffic.libsyn.com/sedaily/2018_02_02_Gremlin.mp3Podcast: Play in new window | Download The number of ways that applications can fail are numerous. Disks fail all the time. Servers overheat. Network connections get flaky. You assume that you are prepared for such a scenario, because you have replicated your servers. You have the database backed up. Your core application is spread across multiple availability zones. But are you really sure that your system is resilient? The

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How to Change an Enterprise’s Software and Culture with Zhamak Dehghani

http://traffic.libsyn.com/sedaily/2018_02_01_MicroservicesZhamakDehgani.mp3Podcast: Play in new window | Download On this show, we spend a lot of time talking about CI/CD, data engineering, and microservices. These technologies have only been widely talked about for the last 5-10 years. That means that they are easy to adopt for startups that get founded in the last 5-10 years, but not necessarily for older enterprises. Within a large enterprise, it can be challenging to make

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Developer Stereotypes with Sue Loh

http://traffic.libsyn.com/sedaily/2018_01_31_SueLoh.mp3Podcast: Play in new window | Download Sue Loh is a software engineer and author of a book with the goal of breaking developer stereotypes. Stereotyping among developers leads to bad outcomes. When incorrect assumptions are made about certain populations, those populations feel marginalized and engineering resources get misallocated. From the perspective of Sue, the main problem is about how children are socialized. Young girls in particular are discouraged from

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Design Principles From Functional Programming with Runar Bjarnason

http://traffic.libsyn.com/sedaily/2018_01_30_DesignPrinciplesforFunctional.mp3Podcast: Play in new window | Download Functional programming can improve the overall design of an application architecture. Runar Bjarnason has been exploring how writing in a functional style increases modularity and compositionality of software for many years. He is co-author of Functional Programming in Scala, a book that explores the relationship between functional programming and software design. In this interview with guest host Adam Bell, Runar explains how writing

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Deep Learning Hardware with Xin Wang

http://traffic.libsyn.com/sedaily/2018_01_29_DeepLearningHardware.mp3Podcast: Play in new window | Download Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but not identical. Deep learning workloads do not run

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