Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. • Graphite — The AI developer productivity platform.  • Augment Code — AI coding assistant that pro engineering teams love. — Steve Huynh spent 17 years at Amazon, including four as a Principal Engineer. In this episode of The Pragmatic Engineer, I join Steve in his studio for a deep dive into what the Principal role actually involves, why the path from Senior to Principal is so tough, and how even strong engineers can get stuck. Not because they’re unqualified, but because the bar is exceptionally high. We discuss what’s expected at the Principal level, the kind of work that matters most, and the trade-offs that come with the title. Steve also shares how Amazon’s internal policies shaped his trajectory, and what made the Principal Engineer community one of the most rewarding parts of his time at the company. We also go into:  • Why being promoted from Senior to Principal is one of the hardest jumps in tech • How Amazon’s freedom of movement policy helped Steve work across multiple teams, from Kindle to Prime Video • The scale of Amazon: handling 10k–100k+ requests per second and what that means for engineering • Why latency became a company-wide obsession—and the research that tied it directly to revenue • Why companies should start with a monolith, and what led Amazon to adopt microservices • What makes the Principal Engineering community so special  • Amazon’s culture of learning from its mistakes, including COEs (correction of errors)  • The pros and cons of the Principal Engineer role • What Steve loves about the leadership principles at Amazon • Amazon’s intense writing culture and 6-pager format  • Why Amazon patents software and what that process looks like • And much more! — Timestamps (00:00) Intro (01:11) What Steve worked on at Amazon, including Kindle, Prime Video, and payments (04:38) How Steve was able to work on so many teams at Amazon  (09:12) An overview of the scale of Amazon and the dependency chain (16:40) Amazon’s focus on latency and the tradeoffs they make to keep latency low at scale (26:00) Why companies should start with a monolith  (26:44) The structure of engineering at Amazon and why Amazon’s Principal is so hard to reach (30:44) The Principal Engineering community at Amazon (36:06) The learning benefits of working for a tech giant  (38:44) Five challenges of being a Principal Engineer at Amazon (49:50) The types of managing work you have to do as a Principal Engineer  (51:47) The pros and cons of the Principal Engineer role  (54:59) What Steve loves about Amazon’s leadership principles (59:15) Amazon’s intense focus on writing  (1:01:11) Patents at Amazon  (1:07:58) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ Inside Amazon’s engineering culture — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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On this episode of Data Unchained, we bring you Sales Director at Titan Solutions Steve Low! Molly Presley, our host, and Steve discuss the ins and outs of Titan Solutions, how they help resellers asses new technologies that are coming out, how they shifted from a data storage company to a data management company, and how their new partnership with Hammerspace will better help their customers and company as a whole. Take a look into the age of data science on this latest podcast episode of Data Unchained!

Data #DataScience #DataManagement #DataStorage #Hammerspace

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Abstract Making Data Simple Podcast is hosted by Al Martin, WW VP Account Technical Leader IBM Technology Sales, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. This week on Making Data Simple, we have Rob Telson AI thought-leader and Vice President of Worldwide Sales at BrainChip, a global tech company that has developed artificial intelligence that learns like a brain, whilst prioritizing efficiency, ultra-low power consumption, and continuous learning. Rob has over 20 years of sales expertise in licensing intellectual property  and selling EDA technology and attended Harvard Business School. Show Notes 2:18 - Rob’s history 5:31 – Tell us about your business model 9:07 – Outline Nero networks 13:54 – Are you a processor, platform, or developer? 22:29 – Who are your customers? 27:34 – How long is the sale cycle? 28:26 – Who’s your biggest competitor?  32:18 – Has your career always been on the hardware side? Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  brainchip.com/developer  YouTube - brainchip Malcolm Gladwell – Outliers Clayton Chritensen – Disruptive Technology, Innovators Dilemma,  Dan Millman - Life you were meant to live That’s Outside of my Boat Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary Data governance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. The team at Immuta has built a platform that aims to tackle that problem in a flexible and maintainable fashion so that data teams can easily integrate authorization, data masking, and privacy enhancing technologies into their data infrastructure. In this episode Steve Touw and Stephen Bailey share what they have built at Immuta, how it is implemented, and how it streamlines the workflow for everyone involved in working with sensitive data. If you are starting down the path of implementing a data governance strategy then this episode will provide a great overview of what is involved.

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Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Feature flagging is a simple concept that enables you to ship faster, test in production, and do easy rollbacks without redeploying code. Teams using feature flags release new software with less risk, and release more often. ConfigCat is a feature flag service that lets you easily add flags to your Python code, and 9 other platforms. By adopting ConfigCat you and your manager can track and toggle your feature flags from their visual dashboard without redeploying any code or configuration, including granular targeting rules. You can roll out new features to a subset or your users for beta testing or canary deployments. With their simple API, clear documentation, and pricing that is independent of your team size you can get your first feature flags added in minutes without breaking the bank. Go to dataengineeringpodcast.com/configcat today to get 35% off any paid plan with code DATAENGINEERING or try out their free forever plan. You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data inf