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#1060
Feb 12, 2019 · 55 min
Uber’s Monitoring Platform with Rob Skillington
Uber manages the car rides for millions of people. The Uber system must remain operational 24/7, and the app involves financial transactions and the safety of passengers. Uber infrastructure runs across thousands of server instances and produce terabytes of monitoring data. The monitoring data is used to understand the health of the software systems as well as relevant business metrics, such as driver efficiency, daily revenues, and user satisfaction. Uber adopted the Prometheus monitoring system to manage their monitoring data. Prometheus regularly scrapes metrics across infrastructure to gather time series data about the state of everything across Uber. As the usage of Prometheus has grown within the company, Uber has had to figure out how to scale their monitoring platform. M3 is a monitoring system built at Uber to scale Prometheus and provide a platform that can effectively scale the data storage as well as the query serving. Rob Skillington is a staff software engineer at Uber, and he joins the show to talk about monitoring at Uber–from the requirements of the system to the implementation of M3. At Uber, M3 powers dashboards, ad-hoc queries, and alerting. M3 was open sourced to give other users access to a scalable Prometheus solution. In a previous episode with Brian Boreham, we discussed one strategy for scaling Prometheus. Today’s episode covers another scalability solution, with M3.
#1059
Feb 11, 2019 · 1 hr 5 min
Pachyderm: Data Pipelines with Joe Doliner
Data infrastructure is advancing beyond the days of Hadoop MapReduce, single-node databases, and nightly reporting. Companies are adopting modern data warehouses, streaming data systems, and cloud-specific data tools like BigQuery. Every company with a large amount of data wants to aggregate that data into a data lake and make the data available to developers. All of this data can be used to power machine learning models which can potentially improve every area within a company where they have historical data. “Data pipeline” is a term used to describe the process of preparing data, building machine learning models, deploying those models, and tracking the results of those models. Pachyderm is a company and open source project that is focused on deployment, management, and scalability of data pipelines. Pachyderm allows developers to version data, track the state of data sets, backtest machine learning models, and collaborate on data. It also tackles the very hard problem of machine learning auditability. Joe Doliner is the CEO of Pachyderm and joins the show to discuss his experience building Pachyderm over the last five years. Data infrastructure has changed a lot in five years, and the world has moved in a direction that has benefitted Pachyderm, with more infrastructure moving to containers and more data teams advancing beyond a world of just Hadoop MapReduce. In today’s show, Joe talks about modern infrastructure, data provenance, and the long-term vision of Pachyderm.
#1058
Feb 8, 2019 · 55 min
Knative: Serverless Workloads with Ville Aikas
Infrastructure software is having a renaissance. Cloud providers offer a wide range of deployment tools, including virtual machines, managed Kubernetes clusters, standalone container instances, and serverless functions. Kubernetes has standardized the container orchestration layer and created a thriving community. The Kubernetes community gives the cloud providers a neutral ground to collaborate on projects that benefit everyone. The two forces of cloud providers and Kubernetes have led to massive improvements in software quality and development practices over the last few years. But one downside of the current ecosystem is that many more developers learn how to operate a Kubernetes cluster than perhaps is necessary. “Serverless” tools are at a higher level than Kubernetes, and can improve developer productivity–but a risk of using a serverless tool is the potential for lock-in, and a lack of portability. Knative is an open-source serverless platform from Google built on top of Kubernetes. Ville Aikas is a senior staff engineer at Google who has worked at the company for eleven years. With his experience, Ville brings a rare perspective to the subjects of Kubernetes, serverless, and the infrastructure lessons of Google. Ville joins the show to discuss Knative, the motivation for building it, and the future of “serverless” infrastructure.
#1057
Feb 7, 2019 · 46 min
VMware Kubernetes Strategy with Brad Meiseles
Virtualization software allows companies to get better utilization from their physical servers. A single physical host can manage multiple virtual machines using a hypervisor. VMware brought virtualization software to market, creating popular tools for allowing enterprises to deploy virtual machines throughout their organization. Containers provide another improvement to server utilization. A virtual machine can be broken up into containers, allowing multiple services to run within a single VM. Containers proliferated after the popularization of Docker, and the Kubernetes open source container orchestration system grew to be the most common way of managing the large numbers of containers that were running throughout an organization. As Kubernetes has risen to prominence, software infrastructure companies have developed Kubernetes services to allow enterprises to use Kubernetes more easily. VMware’s PKS is one example of a managed Kubernetes service. Brad Meiseles is a senior director of engineering at VMware with more than nine years of experience with the company. He joins the show to discuss virtualization, Kubernetes, containers, and the strategy of a large infrastructure provider like VMware.
#1056
Feb 6, 2019 · 57 min
Cadre: Automated Investing with Leonid Movsesyan
Real estate is an asset that is not straightforward to invest in. Real estate can generate excellent returns for investors, but can require much more time and expertise than stocks. Cadre is a company that allows users to invest in real estate more easily and intelligently. Cadre provides users with lots of data about potential investments and enables investments in those opportunities within the platform. Leonid Movsesyan is the head of engineering at Cadre and joins the show to talk about the problems being solved by the company in areas of product development, infrastructure engineering, hiring, and data science. To build a platform for evaluating real estate investments, Cadre ingests and merges lots of data sets–some public and some private. This gives investors a detailed picture of the value of investments. Fintech Daily is a new podcast from Software Engineering Daily covering payments, cryptocurrencies, trading, and the intersection of finance and technology. We are looking for volunteer hosts for Fintech Daily, and if you are interested in working with us to conduct interviews, send an email to host@fintechdaily.co. You can find the podcast on iTunes, Google, and everywhere else, and if you are interested in hosting, don’t hesitate to reach out.
#1055
Feb 5, 2019 · 1 hr 1 min
RocksDB with Dhruba Borthakur and Igor Canadi
RocksDB is a storage engine based on the log structured merge tree data structure. RocksDB was developed at Facebook to provide a tool for embedded databases. The code for RocksDB is a fork of LevelDB, an embedded database built by Google for the Chrome browser. Every database has a storage engine. The storage engine is the low level data structure that manages the data in the database. RocksDB is widely used in database applications where a log structured merge tree is preferable to a b-tree. These tend to be write-heavy workloads. In past shows, we have explored applications of RocksDB in our coverage of databases like TiDB, data intensive applications like Smyte, and data platforms like Rockset. In today’s episode, Dhruba Borthakur and Igor Canadi join for a deep dive into how RocksDB works. Dhruba was the original creator of RocksDB, and Igor is a former Facebook engineer who worked on RocksDB in its early days. Both Dhruba and Igor work at Rockset. We talk about the log structured merge tree, discuss why an LSM has higher write throughput than storage engines based on a b-tree, and evaluate some of the use cases for RocksDB.
#1054
Feb 4, 2019 · 53 min
Scaling Hashicorp with Armon Dagdar and Mitchell Hashimoto
Hashicorp was founded seven years ago with the goal of building infrastructure tools for automating cloud workflows such as provisioning, secret management, and service discovery. Hashicorp’s thesis was that operating cloud infrastructure was too hard: there was a need for new tools to serve application developers. Hashicorp founders Mitchell Hashimoto and Armon Dadgar began releasing open source tools to fulfill their vision of better automation. Terraform, Vagrant, Consul, and other tools created by Hashicorp gained popularity, and Hashicorp began iterating on their business model. Today, Hashicorp makes money by offering enterprise features and support to enterprises such as Pinterest, Adobe, and Cruise Automation. Over the last seven years, enterprise software infrastructure has changed rapidly. First, enterprises moved from script-based infrastructure automation to container orchestration frameworks. Then, the container orchestration world consolidated around Kubernetes. Today, large enterprises are rapidly adopting Kubernetes with a mix of public cloud and on-prem vendors. At the same time, these enterprises are also becoming more willing to consume proprietary tools from the public cloud providers. Hashicorp has benefitted from all of this change. Their different tools fit into a variety of workflows, and are not closely coupled with any particular cloud provider or platform solution. Armon and Mitchell join today’s show to discuss the business model and the product philosophy of Hashicorp. We also touch on service mesh, zero trust networking, and their lessons from the container orchestration wars.
#1053
Feb 1, 2019 · 58 min
Engineering Philosophy with Tyler Cowen
Tyler Cowen’s book Stubborn Attachments outlines a framework that individuals can use to make decisions grounded in economic philosophy. In his previous books, Tyler examined recent economic history. Stubborn Attachments gives his perspective for navigating the future. Tyler is a professor of economics at George Mason University. He is also the host of Conversations with Tyler, a podcast that includes guests such as Ethereum creator Vitalik Buterin, Stripe co-founder Patrick Collison, and Coinbase CTO Balaji Srinivasan. Tyler blogs frequently at Marginal Revolution. Tyler’s previous appearance on Software Engineering Daily centered around his earlier books, including The Complacent Class. In this episode, Tyler describes the philosophy outlined in Stubborn Attachments, then we discuss how his philosophy relates to software engineering, podcasting, and economics. To find all 900 of our old episodes, including past episodes with writers, entrepreneurs, and venture capitalists, check out the Software Engineering Daily app in the iOS and Android app stores. Whether or not you are a software engineer, we have lots of content about technology, business, and culture. In our app, you can also become a paid subscriber and get ad-free episodes–and you can have conversations with other members of the Software Engineering Daily community.
#1052
Jan 31, 2019 · 59 min
Architects of Intelligence with Martin Ford
Artificial intelligence is reshaping every aspect of our lives, from transportation to agriculture to dating. Someday, we may even create a superintelligence–a computer system that is demonstrably smarter than humans. But there is widespread disagreement on how soon we could build a superintelligence. There is not even a broad consensus on how we can define the term “intelligence”. Information technology is improving so rapidly we are losing the ability to forecast the near future. Even the most well-informed politicians and business people are constantly surprised by technological changes, and the downstream impact on society. Today, the most accurate guidance on the pace of technology comes from the scientists and the engineers who are building the tools of our future. Martin Ford is a computer engineer and the author of Architects of Intelligence, a new book of interviews with the top researchers in artificial intelligence. His interviewees include Jeff Dean, Andrew Ng, Demis Hassabis, Ian Goodfellow, and Ray Kurzweil. Architects of Intelligence is a privileged look at how AI is developing. Martin Ford surveys these different AI experts with similar questions. How will China’s adoption of AI differ from that of the US? What is the difference between the human brain and that of a computer? What are the low-hanging fruit applications of AI that we have yet to build? Martin joins the show to talk about his new book. In our conversation, Martin synthesizes ideas from these different researchers, and describes the key areas of disagreement from across the field. To find all 900 of our old episodes, including past episodes with authors and artificial intelligence researchers, check out the Software Engineering Daily app in the iOS and Android app stores. Whether or not you are a software engineer, we have lots of content about technology, business, and culture. In our app, you can also become a paid subscriber and get ad-free episodes–and you can have conversations with other members of the Software Engineering Daily community.
#1051
Jan 30, 2019 · 1 hr 5 min
Anatomy of Next: New World with Mike Solana
Mars is a cold, inhospitable planet far from earth. It presents one of the most complex challenges faced by engineers: how can we create a new world? To create a new world, first we have to get there. We can build new rockets with improved propulsion systems. We can build ships that allow us to survive the long, grueling trip from Earth to Mars. We can build robots that will help us construct our new home. And this is just the beginning. Mars could be warmed, and could develop a hydrologic cycle like the system of clouds and oceans on earth. Mars could be a place for new ideas and new cultures, unfettered by the conventions of Earth. Mike Solana is the host of Anatomy of Next, a podcast about technologies and philosophies of the future. He’s also a vice president at Founder’s Fund. In a previous episode, Mike joined the show to talk about artificial intelligence, genetics, and robotics. Today, we discuss Mars. The latest season of Anatomy of Next explores the science that is bringing us closer to exploring other planets. On his podcast, Mike speaks with engineers, researchers, and entrepreneurs about the state of the art of space technology–as well as the challenges that remain unsolved. Mike returns to the show to discuss this dream of a new world. Why should we go to Mars? And why should the software engineers listening to this podcast even care? To find all 900 of our old episodes, including past episodes with venture capitalists, futurists, and philosophers, check out the Software Engineering Daily app in the iOS and Android app stores. Whether or not you are a software engineer, we have lots of content about technology, business, and culture. In our app, you can also become a paid subscriber and get ad-free episodes–and you can have conversations with other members of the Software Engineering Daily community.
#1050
Jan 29, 2019 · 53 min
Likewar: The Weaponization of Social Media with P.W. Singer
Social media has transformed our lives. It has also transformed how wars are fought. P.W. Singer’s new book “Likewar: The Weaponization of Social Media” describes the far-reaching impact of social media on the tactics and strategies used by military, business, and everyday citizens. We have all read about stories such as Russian bots and Cambridge Analytica, but Likewar covers many more cases that are surprising and mildly frightening. From the Gaza Strip to the streets of Chicago to Taylor Swift’s Instagram feed, Likewar describes just how pervasive the effect of social media has been on warfare. Likewar also provides historical context. For software engineers, the repurposing of social media as a weapon is disconcerting. Many of us are working on products with a social networking component. Does this make us complicit in building weapons? We can find some reassurance in the fact that this has happened before: from the newspaper to the television, every new invention has been used repurposed for war. In a war, a new piece of technology always presents a new vector to gain an advantage in a conflict. Because the stakes are so high in a war, there is a large incentive to find creative ways to use technology to undermine your adversaries and to help your allies. P.W. Singer has written about robotics, cybersecurity, and modern warfare for a decade. In a previous episode, we discussed subjects like Stuxnet, drones, and social media manipulation. In today’s show, P.W. returns to talk about his book Likewar: The Weaponization of Social Media.
#1049
Jan 28, 2019 · 58 min
Software Chasms with Martin Casado
Infrastructure software can be a great business. An infrastructure software company sells core technology to a large enterprise such as a bank or insurance company. This software has near zero marginal cost and generates a large annuity for the infrastructure software company. Once a bank has purchased your infrastructure software, the bank is likely to renew every year and never remove the software. Selling infrastructure software is like selling concrete or steel, except the software is cheaper to produce, easier to distribute, and generates an annuity rather than being a one-time sale. The fundamental economics of enterprise infrastructure software are extremely appealing, and every year more businesses enter the space–but few businesses ever leave. If you are starting an infrastructure software company, you can expect a complex battle for market share. There is no easy trick to get it into the hands of your target customer. Martin Casado studied computer science at Stanford before founding Nicira, a company that pioneered software-defined networking and virtualization technology. In 2012, Nicira sold to VMware for $1.26 billion. Martin now works as a general partner at Andreessen Horowitz. Martin writes about the modern strategies of building a successful infrastructure software company. He describes two methods of selling into an enterprise: bottoms-up and top-down. In a bottoms-up model, engineers within an enterprise start using your product to solve a well-defined problem, such as API management. As more and more employees within the organization start to use your product, you can begin to engage the enterprise about becoming a paying customer for your product. Since the enterprise is already using your product, the sales conversation is much easier. In the top-down model, you engage the CIO, CEO, or CTO directly and try to convince them that your product is worth paying for. When the senior leadership of a bank buys into your product idea, you can count on that senior leadership to convince their developers to use your product within the bank. It is a rare occurrence that your infrastructure software company will be able to fit cleanly into either of these models–bottoms-up or top-down. More often, there will be some bottoms-up usage, and some top-down buy-in for your product. But you will have to evangelize the product on all fronts. You will have to convince both the engineers and the senior leadership. Your product probably won’t speak for itself. You will have to develop expertise in sales, marketing, and consultancy. And in many cases, you might end up in an unending chasm. The unending chasm describes a mode in which an infrastructure company must function as both a product company and a consultancy. Your consultancy is necessary to integrate your product into the enterprise, and ensure that your software actually gets used. But it reduces the appealing economics of a pure software company. The unending chasm does not prevent you from being successful. Companies who have had very successful IPOs remain in the unending chasm. But it’s useful to know whether you are heading for an unending chasm–or if you are already in one. Martin Casado joins the show today for a discussion of product development, software engineering, and go-to-market strategy. To find all 900 of our episodes, including past episodes with a16z partners, check out the Software Engineering Daily app in the iOS and Android app stores. Whether or not you are a software engineer, we have lots of content about technology, business, and culture. In our app, you can also become a paid subscriber and get ad-free episodes–and you can have conversations with other members of the Software Engineering Daily community. Image Source
#1048
Jan 25, 2019 · 58 min
Kubeflow: TensorFlow on Kubernetes with David Aronchick
When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing and deployment process for continuous delivery of models. The continuous delivery process for machine learning models is like the continuous delivery process for microservices, but can be more complicated. A developer testing a model on their local machine is working with a smaller data set than what they will have access to when it is deployed. A machine learning engineer needs to be conscious of versioning and auditability. Kubeflow is a machine learning toolkit for Kubernetes based on Google’s internal machine learning pipelines. Google open sourced Kubernetes and TensorFlow, and the projects have users AWS and Microsoft. David Aronchick is the head of open source machine learning strategy at Microsoft, and he joins the show to talk about the problems that Kubeflow solves for developers, and the evolving strategies for cloud providers. David was previously on the show when he worked at Google, and in this episode he provides some useful discussion about how open source software presents a great opportunity for the cloud providers to collaborate with each other in a positive sum relationship.
#1047
Jan 24, 2019 · 1 hr 7 min
TiDB: Distributed NewSQL with Kevin Xu
When a user interacts with an application to order a ride with a ridesharing app, the data for that user interaction is written to a “transactional” database. A transactional database is a database where specific rows need to be written to and read from quickly and consistently. Speed and consistency are important for applications like a user ordering a car, and riding around in that car, because the user’s client is frequently communicating with the database to update their session. Other applications of a transactional database would include a database that backs a messaging system, a banking application, or document editing software. The data from a transactional database is often reused in “analytic” databases. An analytic database can be used for performing large scale analysis, aggregations, averages, and other data science queries. The requirements for an analytic database are different from a transactional database because the data is not being used for an active user session. To fill the data in an analytic database, the transactional data gets copied from the transactional database in a process called ETL. The separation of the transaction data store from the analytic data store causes problems for data engineering. To address these problems, some newer databases combine transactional and analytic functionality in the same database. These databases are often called “NewSQL”. TiDB is an open source database built on RocksDB and Kubernetes. TiDB is widely used in China by high volume applications such as bike sharing and massively multiplayer online games. Kevin Xu works at PingCAP, a company built around TiDB. He joins the show to talk about modern databases, distributed systems, and the architecture for TiDB.
#1046
Jan 23, 2019 · 50 min
Storybook: UI Engineering with Zoltan Olah
React, Vue, and Angular are the most popular frontend JavaScript frameworks. Each of these frameworks lets frontend developers build components. A component is a high level visual abstraction that is used to compose a user interface. Frontend development has moved towards component-driven-development. At a typical technology company, a designer will put together a design file of different user interface elements, and the frontend engineer will take those UI elements and program code that can render those designs as components. As organizations have started to reuse their components and share them across the organization, the efficiency of design and frontend engineering is improving. User interface is gaining more of an emphasis with organizations and new tools are allowing frontend engineers and designers to work together more productively. One of these tools is Storybook, a system for sharing components and the code that renders those components. Zoltan Olah joins the show to talk about Storybook, and his company Chroma. Chroma is building tools to allow design-driven teams to work more effectively. We talked about how the relationship of designers and frontend engineers has some resemblance to the relationship between “dev” and “ops” before the DevOps movement. There are some frictions in the process of moving between design and engineering implementation, and in talking to Zoltan, I got an understanding for how much the UI layer could improve through better tooling.
#1045
Jan 22, 2019 · 1 hr
RSocket: Reactive Streaming Service Networking with Ryland Degnan
Netflix has thousands of service instances communicating with each other. When a Netflix client on a smartphone makes a request for a movie, that request hits Netflix’s backend, where the request is fulfilled by a chain of requests through different services. Services and clients communicate using several different interaction patterns. A service might send a single request and expect a single response. Or it might fire and forget, not expecting a response. A service also might send a single request and expect a stream of messages to be sent back over the network. In a highly interactive application like Netflix, there is a frequent use of “streams” of data. RSocket is a protocol that makes reactive streams easier to work with. Ryland Degnan is the CTO of Netifi, and he joins the show to discuss reactive streams and service-to-service networking. Ryland worked at Netflix on the Edge Platform team for four years, and he shares his experience working at Netflix, the challenges of networking at scale, and the company he is building around RSocket.
#1044
Jan 21, 2019 · 48 min
Prometheus Scalability with Bryan Boreham
Prometheus is an open source monitoring system and time series database. Prometheus includes a multi-dimensional data model, a query language called PromQL, and a pull model for gathering metrics from your different services. Prometheus is widely used by large distributed systems deployments such as Kubernetes and Cloud Foundry. Prometheus gathers metrics from your services by periodically scraping those services. Those metrics get gathered, compressed, and stored onto disk for querying. But Prometheus is designed to store all of its records on one host in one set of files–which limits the scalability and availability of those metrics. Cortex is an open source project built to scale Prometheus. Cortex effectively shards Prometheus by parallelizing the “ingestion” and storage of Prometheus metrics. Cortex can take metrics from multiple Prometheus instances and store them across a distributed NoSQL database like DynamoDB, BigTable, or Cassandra. Bryan Boreham is an engineer at Weaveworks, where he works on deployment, observability, and monitoring tools for containers and microservices. He wrote much of the code for Cortex, and we met up at KubeCon North America to talk about the motivation for creating Cortex, the broader landscape of Kubernetes monitoring, and other approaches to scaling Prometheus.
#1043
Jan 18, 2019 · 1 hr 1 min
Spot Instances with Amiram Shachar
When a developer provisions a cloud server, that server is called an “instance”. These instances can be used for running whatever workload a developer has, whether it is a web application, a database, or a set of containers. The cloud is cheap to get started on. New applications with few users can often be hosted on infrastructure that is less than $10 per month. But as an application grows in popularity, there is more demand for CPUs and storage. A company will start to buy more and more servers to scale up to the requirements of their growing user base. The costs of running infrastructure in the cloud will increase, and the company will start to look for ways to save money. One common method of saving money is to buy “spot instances”. A spot instance is an instance that is cheaper than “reserved instances” or “on-demand” instances. The reason that there are different instance types is because a giant cloud provider has a highly variable amount of work that is being demanded from that cloud provider. If you are in charge of AWS, you have to make sure that at any given time, you can give server resources to anyone that asks for it. Your data centers need to have physical machines that are ready to go at any time. This means that much of the time, you have server resources that are going unused. If you are a cloud provider, how can you get people to use your compute resources? You can make them cheaper. So a user can come along and buy your compute at the discounted “spot” price. But this presents a problem for the cloud provider. If you start to give away your compute at cheaper prices, and then the overall demand for your cloud resources go up once again, you are going to miss out on profits. As the cloud provider, you need to kick people off of your spot instances, so that you can take those same instances and sell them to people at the higher market prices. And this presents a problem for the user. If you buy a cheap spot instance, that instance is only available until the cloud provider decides to kick you off. You have a tradeoff between cost and availability of your instances. Because of this, spot instances are typically used only for workloads that are not mission critical–workloads that can afford to fail. Spotinst is a company that allows developers to deploy their workloads reliably onto spot instances. Spotinst works by detecting when a spot instance is going to be reclaimed by a cloud provider and re-scheduling the workload from that cloud provider onto a new spot instance. Amiram Shachar is the CEO of Spotinst. He joins the show to talk about the different types of instances across cloud providers, the engineering behind Spotinst, and how the usage of containers and the rise of Kubernetes is changing the business landscape of the cloud.
#1042
Jan 17, 2019 · 1 hr 3 min
Looker: Business Intelligence Platform with Daniel Mintz
If a business has been operating successfully for a few years, that business has accumulated a high volume of data. That data exists in spreadsheets, CSV files, log files, and balance sheets. Data might be spread across local files on a user’s laptop, databases in the cloud, or storage systems in an on-premise data center. Older businesses have more data, in more places, in more formats. Legacy systems and old batch processing jobs that have been running for years are taking data from one place and porting it to another. Every mature company needs to access and analyze the data in all of these different places–whether they are a publication with millions of readers like The Economist or a fast growing infrastructure provider like Twilio. “Business intelligence” is a term often used to describe tools for analyzing data in the form of charts, graphs, and reports. Business intelligence applications are crucial to the success of a business because they are used by everyone in an organization–whether you are a business analyst forecasting sales for the next quarter, an engineer who is determining how many servers to provision, or a CEO trying to decide what the best area of your business to focus on is. There have been several generations of business intelligence tools. Each generation of business intelligence is built for the trends and infrastructure of that generation. Looker is a more recent business intelligence tool that was built in light of several trends in software: the growth in volume of data; the growth in the number of systems that users need; the changing types of users that need to access data; and the need to share business intelligence across social workplace tools like Slack, Asana, and email. Daniel Mintz joins the show to describe his experience using business intelligence tooling and his work at Looker, as well as the landscape of business intelligence, ETL, and data engineering.
#1041
Jan 16, 2019 · 47 min
Human Sized Robots with Zach Allen
Robots are making their way into every area of our lives. Security robots roll around industrial parks at night, monitoring the area for intruders. Amazon robots tirelessly move packages around in warehouses, reducing the time and cost of logistics. Self-driving cars have become a ubiquitous presence in cities like San Francisco. For a hacker in a dorm room, or a researcher in a small lab, how do you get started with robotics? There are drones and other small options like AWS DeepRacer–but what is the equivalent of the Raspberry Pi for large, human-sized robots? Zach Allen is the founder of Slate Robotics, a company that makes large, human-sized robots that are at a low enough cost to be accessible to tinkerers, researchers, and prototype builders. Zach joins the show to talk about the state of robotics and why he started a robot company. What Zach is doing is quite hard–he is a solo founder who has bootstrapped a robotics company from scratch. He is set up in a strip mall in Missouri, where he has set up a row of 3-D printers to create the parts for his robots. He programs and assembles these robots himself. Whether you are interested in robots are thinking about starting a hardware company, this episode could be useful to you.

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