The InfoQ Podcast

In this week's podcast QCon chair Wesley Reisz talks to Matt Ranney who is the Chief Systems Architect at Uber, where he's helping build and scale everything he can. 

Why listen to this podcast:

 - Expanding a company and team at this rate is genuinely hard. Lots of mistakes have been made along the way.
 - Microservices allow companies to grow rapidly but have a cost in terms of aggregate velocity.
 - Uber is gradually moving its marketplace development from Node.js to Go and Java. Java is used for the map services.
 - Aggressive failure testing is used extensively in Uber.
 - Some early design choices - like using JSON over HTTP - make formal verification basically impossible.

Notes and links can be found on InfoQ: http://bit.ly/1TH8app

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Attend Matt Ranney's session at QCon New York 2016, Jun 13-17: http://bit.ly/1TH75ht

Show Notes

Uber's Chief Systems Architect on their Architecture and Rapid Growth

In this week's podcast QCon chair Wesley Reisz talks to Heather Fleming, who is the VP of product and program management at GILT, where she is responsible for not only the customer-facing website, but also back office things from distribution to order processing.

Why listen to this podcast:

- GILT treats every person as an individual, with a skillset that is outside their responsibilities.
- You should be able to be your authentic self wherever you are.
- Google found creating a psychologically safe work environment was key to high performing teams.
- You can kill a great team by taking away their autonomy and empowerment.
- Great engineers that want to be managers are fearful of losing their skill, and great engineers that don't want to be managers are put in those roles and are terrible managers.

Notes and links can be found on InfoQ: http://bit.ly/1U2Wgq9

1m 49s - One of the things that makes the culture at GILT so special is the "ingredients framework" that considers every person an individual, with a skillset that is outside what their title might say and what their role and responsibilities might be.
3m 29s - One of the team ingredients is called "motivator" - the ability to make sure that the team understands what they're working on, and more importantly the "why" and how it's driving business or customer value.
4m 35s - The relationship you have with your co-workers should be deeper than many of us are allowing them to be, because they are like your second family.

Quick scan our curated show notes on InfoQ. http://bit.ly/1U2Wgq9 

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. http://bit.ly/24x3IVq

Attend Heather Fleming's session at QCon New York 2016, Jun 13-17: http://bit.ly/1U2f3og

Show notes

GILT VP Heather Fleming on Unlocking the "Secret Sauce" of Great Teams

In this week's podcast Richard Seroter talks to James Shore, author of The Art of Agile Development and one of the original signatories of the Agile Manifesto. Also on the podcast are Llewellyn Falco, creator of the open source testing tool ApprovalTests and co-founder of Teaching Kids Programming, and Rebecca Wirfs-Brock, inventor of Responsibility-Driven Design, as well as the author of books including Designing Object: Oriented Software and Object Design: Roles, Responsibilities and Collaborations.

Why listen to this podcast:

 - A lot of people know how to do TDD and refactoring for the back end, but not for the font, but the basics are the same.
 - The basics of Test-Driven Development are the same for the front or back end.
 - If you don't know what's wrong, you don't know how to fix it.
 - The most common code smells, according to Llewellyn Falco: Clutter, long lines, long methods, duplication, and inconsistency.
 - How do we make, in an agile way, the architectural work visible, and not ignore it?
 - How do you have an incremental architecture and get measurements? If I say to you I'm going to go away for six months and figure it out, that's not very measurable.

Notes and links can be found on InfoQ: http://bit.ly/1Pse2r1

1m:10s - The talk 'Agile Engineering for the Web' was about how do you bring typical Agile engineering ideas like TDD and refactoring to the front-end languages.
1m:33s - A lot of people know how to do these things on the back-end, but when you get to the front end a lot of people just throw up their hands.
2m:24s - I see CSS bugs all the time, because it's very hard to refactor CSS without breaking something.

Quick scan our curated show notes on InfoQ. http://bit.ly/1Pse2r1

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. http://bit.ly/24x3IVq

James Shore, Llewellyn Falco, and Rebecca Wirfs-Brock on TDD and Architecture

In this week's podcast, professor Barry Burd talks to Shuman Ghosemajumder. Ghosemajumder is VP of product management at Shape Security and former click fraud czar for Google. Ghosemajumder is also the co-author of the book CGI Programming Unleashed, and was a keynote speaker at QCon New York 2016 presenting Security War Stories.

Why listen to this podcast:

With more of our lives conducted online through technology and information retrieval systems, the use of advanced technology gives criminals the opportunity to be able to do things that they weren't able to do.

 - Cyber-criminals come from all over the world and every socioeconomic background, so long as there's some level of access to computers and technology.
 - You see organised cyber-crime focusing on large companies because of the fact that they get a much greater sense of efficiency for their attacks.
 - Cyber-criminals are getting creative, and coming up with ways to interact with websites we haven't thought of before.
 - You can have very large scale attacks that are completely invisible from the point of view of the application that's being attacked.
 - The context of what are you are using software for is more important than just going through an understanding of the code level vulnerability.

Notes and links can be found on http://bit.ly/2atBFgk

The People Behind Cyber-Crime

5:28 - There are all kinds of different personalities and demographics involved. Cyber-criminals come from all over the world and every socioeconomic background, so long as there's some level of access to computers and technology. Even in cases where a cyber criminal doesn't know how to use technology directly, or how to create something like a piece of malware, they can still be involved in a cyber-criminal's scheme.
6:29 - A scheme which uses large groups of individuals and which doesn’t necessarily need to have skills itself, is stealing money from bank accounts. Being able to transfer money using malware on people’s machines from one account to another account that the cyber-criminal controls still involves getting that money out. That last step can involve a set of bank accounts that are assigned to real individuals.

More on this:

Quick scan our curated show notes on InfoQ. http://bit.ly/2atBFgk

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. http://bit.ly/24x3IVq

InfoQ Podcast

Shuman Ghosemajumder on Security and Cyber-Crime

In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil. O'Neil is the author of the blog mathbabe.org. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs. O'Neil earned a mathematics Ph.D. from Harvard University. Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed.

Why listen to this podcast:

 - There is a class of pernicious big data algorithms that are increasingly controlling society but are not open to scrutiny.
 - Flawed data can result in an algorithm that is, for instance, racist and sexist.  For example, the data used in predictive policing models is racist. But people tend to be overly trusting of algorithms because they are mathematical.
 - Data scientists have to make ethical decisions even if they don’t acknowledge it. Often problems stem from an abdication of responsibility.
 - Auditing for algorithms is still a very young field with ongoing academic research exploring approaches.
 - Government regulation of the industry may well be required.

Notes and links can be found on http://bit.ly/2eYVb9q

Weapons of math destruction

0m:43s - The central thesis of the book is that whilst not all algorithms are bad, there is a class of pernicious big data algorithms that are increasingly controlling society.
1m:32s - The classes of algorithm that O'Neil is concerned about - the weapons of math destruction - have three characteristics: they are widespread and impact on important decisions like whether someone can go to college or get a job, they are somehow secret so that the people who are being targeted don’t know they are being scored or don’t understand how their score is computed; and the third characteristic is they are destructive - they ruin lives.
2m:51s - These characteristics undermine the original intention of the algorithm, which is often trying to solve big society problems with the help of data.

More on this:

Quick scan our curated show notes on InfoQ. http://bit.ly/2eYVb9q

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

Cathy O'Neil on Pernicious Machine Learning Algorithms and How to Audit Them

Jonas Bonér, CTO of LightBend and creator Akka, discusses using Akka when developing distributed systems. He talks about the Actor Model, and how every Microservice needs to be viewed as a system to be successful. 

Why listen to this podcast:

 - Akka is JVM-based framework design for developing distributed systems leveraging the Actor Model - an approach for writing concurrent systems that treat actors as universal primitives and the most successful model with abstraction has been streaming
 - Circuit breakers in Akka are a backup and retry policy; they protect you by capturing failure data and allow you to roll back 
 - Every Microservice needs to be viewed as a system, it needs to have multiple parts that run on different machines in order to function and be fully resilient - thus is a Microsystem
 - Two different trends have emerged when it comes to hardware and environments: one is the trend toward Multi-core, the is a movement toward virtualized environments and the cloud
 - Saga pattern of managing long running transactions in a distributed system fits very well with messaging style architectures

Notes and links can be found on: http://bit.ly/2kwB2eB

Akka
The Actor Model
When Akka and the Actor Model is the perfect choice
Circuit breakers patterns in distributed systems
Two trends toward Multi-core
Reactive Manifesto
Event Driven vs. Message Driven
Reactive Programming and Streams
Microliths to Microsystems
What do you have to get right before you start trying to deploy a distributed systems?
Working with ML / AI at Lightbend to understand tracing through distributed system
Saga Pattern

More on this:
Quick scan our curated show notes on InfoQ http://bit.ly/2kwB2eB

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

Jonas Bonér on the Actor Model, Akka, Reactive Programming, Microservices and Distributed Systems

Shubha Nabar is a senior director of data science for Salesforce Einstein. Prior to working for Salesforce, she was a data scientist at LinkedIn and Microsoft.  In the podcast she discusses Salesforce Einstein and the problem space that they are trying to solve, explores the differences between enterprise and consumer for machine learning, and then talks about the Optimus Prime Scala library that they use in Salesforce.

Why listen to this podcast:
* The volume of data, and hardware advances have made it possible to do machine learning to do them a lot faster.
* AI is a science of building intelligent software, encompassing many aspects of intelligence that we tend to think of as human.
* If you can’t measure something, you can’t fix it.
* You have to think about what you can automate, rather than having a human to try and engineer out all those features.
* Get feedback on design.

Nora Jones, a senior software engineer on Netflix’ Chaos Team, talks with Wesley Reisz about what Chaos Engineering means today. She covers what it takes to build a practice, how to establish a strategy, defines cost of impact, and covers key technical considerations when leveraging chaos engineering.

Why listen to this podcast:

- Chaos engineering is a discipline where you formulate hypotheses, perform experiments, and evaluate the results afterwards.
- Injecting a bit of failure over time is going to make your system more resilient in the end.
- Start with Tier 2 or non-critical services first, and build up success stories to grow chaos further.
- As systems become more and more distributed, there becomes a higher need for chaos engineering.
- If you’re running your first experiment, get your service owners in a war room and get them to monitor the results of the test as it is running.

More on this: Quick scan our curated show notes on InfoQ http://bit.ly/2vJoimw

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Shubha Nabar Discusses Einstein, the Machine Learning System in Salesforce

Software engineers, architects and team leads have found inspiration to drive change and innovation in their team by listening to the weekly InfoQ Podcast. They have received essential information that helped them validate their software development map. We have achieved that by interviewing some of the top CTOs, engineers and technology directors from companies like Uber, Netflix and more. Over 1,200,000 downloads in the last 3 years.

Software engineers, architects and team leads have found inspiration to build better, high performing teams by listening to the weekly InfoQ Podcast. We have achieved that by interviewing some of the top CTOs, engineers and technology directors from companies like Uber, Netflix and more. Over 500,000 downloads in the last 3 years.

Shubha Nabar Discusses Einstein, the Machine Learning System in Salesforce

InfoQ

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