The New Stack Podcast

When it comes to machine learning and GraphDB, “We’re in the first inning of a nine-inning game,” said Emil Eifrem, CEO and founder of Neo4j.  “If you look at machine learning algorithms, they are written in graph language, or can be expressed in graph language.”

Eifrem joins TC Currie in this episode of The New Stack Makers.  Speaking from Fort Mason in San Francisco at the GraphTour event earlier this year, Eifrem talks about graph databases, how they are different from relational databases, and how this decade-old technology is keeping up with the new kids on the block.

When it comes to machine learning and GraphDB, “We’re in the first inning of a nine-inning game,” said Emil Eifram, CEO and founder of Neo4j.  “If you look at machine learning algorithms, they are written in graph language, or can be expressed in graph language.”

Eifram joins TC Currie in this episode of The New Stack Makers.  Speaking from Fort Mason in San Francisco at the GraphTour event earlier this year, Eifram talks about graph databases, how they are different from relational databases, and how this decade-old technology is keeping up with the new kids on the block.

At the JFrog SwampUp conference in Napa earlier this year, Melody Meckfessel and former Google vice president Sam Ramji (now with Autodesk) were showing off the way Google makes its sausage. Unlike other less well curated development experiences, Google's process is worth examining and shouldn't leave anyone offended or covered in sausage leavings.

For starters, Google's internal development processes and practices are immense. The numbers revealed by Meckfessel at the conference showed that over 500 million tests are run per day inside Google's systems. That's to accommodate over 4 million builds, daily.

Why so many builds? Because Google's Bazel build system allows for near instant build processes, ensuring developers can quickly gain the feedback they need from their code.

Shannon Williams has played a very hands-on role in launching software for over 20 years. Recently, after observing the power and potential of containerization, Williams co-founded  Rancher Labs in 2014, after successfully creating Cloud.com (now owned by Citrix Systems).

Since 2014, things have changed. Kubernetes, of course, has emerged as the standard containerization platform, prompting Rancher to go “all Kubernetes” with its recent production release of Rancher’s namesake product Rancher 2.0.

In this episode of The New Stack Makers podcast, Williams takes a step back to offer his perspective on Kubernetes and why and how Rancher made the shift to the platform.

Just like Kung Fu in the 70's, serverless application development and deployment is hot. But just like Kung Fu, serverless is as much of a mindset as it is a platform. Amazon's Lambdas really kicked off the excitement, but going even further back, the origins of this style of programming can be found in functional principles: those found in Erlang, Haskell, and Scala.

Primarily, the idea of stateless computing and the goal of building discrete application functions drive this new paradigm of serverless. What's new about serverless is the fact that applications are offered up to the cloud to run in some unknown nebula managed by the cloud provider, with scaling needs completely abstracted away from the developer.

That's the promise, anyway. For the older readers out there, this probably sounds a bit like an elaborate new form of application server. And you'd be completely right. To this end, both IBM and Oracle's approach to the serverless revolution has been to offer open source runtimes for anyone to run in their own cloud.

It's a crowded market if you're a machine learning company. Every vendor under the sun has integrated some new-fangled AI-driven service, making the real ROI tough to spot in the jungle of buzzwords and feature creep. Paperspace is hoping to make that journey a little easier for businesses by offering Gradient, an easily manageable infrastructure platform for deep learning.

Under the hood, Paperspace is not just some AI startup: they're offering developers access to Google's Tensorflow Processing Units (TPUs), which are otherwise only available for research groups and other computer sciencey-types who've applied to Google to gain access. Paperspace is currently offering TPU access as well as GPU access in its deep learning platform.

That alone was worth sitting down with CEO Dillon Erb and CTO Tom Sanfilippo for a chat. These co-founders took time to discuss just what it's like to build deep learning applications with TPUs.

There are two sides to every software and IT story: the story of the people doing the work, and the stories the vendors will tell you about how that work can be done more efficiently. Sometimes, the hardest part of the job can be reconciling these two often disparate views of the world, as vision means pavement, and tools meet unique problems. That's why it's so important to discuss the usage of IT tooling with real IT practitioners. Today, we've got two very smart IT folks packed into this episode of the Maker's Podcast, both here to discuss the usage of Chef in enterprise environments.

First up is Stephen Figgins, Associate Director of Operations for Agile Technology Solutions at the University of Kansas. "We have been working with Chef for six years and have had a lot of success. Our Chef made it very easy for us to move to AWS. AWS had a lot of complexity to it, like learning how to setup security and networks and things like that. One thing we really didn't have to worry about in this was how do we configure the EC2 nodes we're going to create. Because we already knew how to take a blank EC2 node and make it run our application. We were able to not focus instead on lifting and shifting our applications from our non-cloud environment and instead focus on how we could best leverage the cloud," said Figgins.

Watch on YouTube: https://www.youtube.com/watch?v=nYpTuheMXE8

There is a big difference between agile development and continuous delivery and deployment, but you probably can't get to the latter without having implemented the former. At the end of the day, every company wants to ship better code more often, in order to innovate in its market, but actually turning your software development and IT teams into lean mean feature shipping machines isn't as easy as taking a straight road to a clear goal ahead.

Instead, the road to success is paved with DevOps tools, agile processes and best practices. One of those tools is Spinnaker, the open source, multi-cloud continuous delivery tool that originated at Google. Jon Stockdill, CTO and co-founder of Kenzan uses Spinnaker in his DevOps consulting engagements with clients. He said it's the best CI/CD solution out there, at the moment.

Chef isn't just for standing up machines anymore. With so much riding on enterprise infrastructure, it's tough to move things around with confidence, and to remain certain that everything you removed, moved or replaced is properly back online after such a shift. Chef has evolved to provide this sort of reassurance to IT administrators and developers, far beyond the original use case of provisioning and standing up single machines.

Brittany Woods, automation engineer at CARFAX, said that "I'm responsible for making sure Chef works for us, and making sure people's lives are easier because of it. We are a Linux shop. Primarily, we use Chef. Exclusively we use Chef for Linux. We build the systems to support the products, and we Chef to make that happen, and we use Chef to manage those systems throughout their entire lifecycle," said Woods.

"Chef is our way to fully manage that architecture from configuration--configuration specific to apps, configuration speicfic to tooling that we use--basically the entire build of the system outside of provisioning... Right now we are comprised of several different smaller teams that maintain a different focus. What products they support, they maintain cookbooks for those products. My role is to make them successful. To ensure they have the tools they need to be successful, and also to manage the Chef architecture," said Woods.

Watch on YouTube: https://www.youtube.com/watch?v=jEWRCSeYzQ0

Data in microservice-based environments can be difficult to manage at scale. When application servers scale to near infinity, the datastores can't necessarily expand to meet that demand; they can only be optimized to keep up, and perhaps sharded. Considering just how much enterprise information is stored in some of those large systems, it's a worrying proposition to be asked by management to increase application performance when much of it is tied to an Oracle or Microsoft database.

Surya Varanasi, CTO of Vexata, has been dealing with large amounts of data for over a decade, now. While he once worked around the hardware layer at Brocade, today he focuses very heavily on the enterprise databases that power businesses around the world. From Oracle, to SAP, to SAS, and Microsoft, Vexata swims in a decidedly enterprise pool of customers.