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  • 2,221 episodes
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Episodes 2,221

Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • #1105
    Apr 5, 2019 · 54 min

    AWS Compute with Deepak Singh

    Upcoming event: FindCollabs Hackathon at App Academy on April 6, 2019 On Amazon Web Services, there are many ways to run an application on a single node. The first compute option on AWS was the EC2 virtual server instance. But EC2 is a large abstraction compared to what many people need for their nodes–which is a container with a smaller set of resources to work with. Containers can be run within a managed cluster like ECS or EKS, or run on their own as AWS Fargate instances, or simply as Docker containers running without a container orchestration tool. Beyond the option of explicit container instances, users can run their application as a “serverless” function-as-a-service such as AWS Lambda. Functions-as-a-service abstract away the container and let the developer operate at a higher level, while also providing some cost savings. Developers use these different compute options for different reasons. Deepak Singh is the director of compute services at Amazon Web Services, and he joins the show to discuss the use cases and tradeoffs of these options. Deepak also discusses how these tools are useful internally to AWS. ECS and Lambda are high-level APIs that are used to build even higher level services such as AWS Batch, which is a service for performing batch processing over large data sets.

  • #1103
    Apr 4, 2019 · 47 min

    Data with Ben Lorica

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Ben Lorica is the chief data scientist at O’Reilly Media and the program director of the Strata Data Conference. In his work, Ben spends time with people across the software industry, giving him broad perspective. In the early days of the data engineering ecosystem, the Hadoop vendor wars were starting between Cloudera and Hortonworks. Strata was a neutral ground for practitioners and open source contributors to meet and share ideas about the Hadoop ecosystem. Since then, the conference has grown to encompass topics such as data science, distributed databases, streaming frameworks, and machine learning. There are many open questions in the data world right now. What is the best path that an enterprise can take to build out a data platform? How should a software team be arranged to efficiently build machine learning models? Which distributed streaming frameworks should I use for what purpose? Ben joins the show to discuss modern data engineering, data science, and infrastructure.

  • #1102
    Apr 3, 2019 · 1 hr 11 min

    Stablecoins with Rune Christensen

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 A currency can fulfill numerous financial use cases. One use case is store of value: currency holders can reliably expect their currency to maintain some value, though that value may fluctuate over time. Another use case is speculation: currency holders are owning currency in the hope that the market price of the currency will increase over time. Bitcoin is a useful store of value and an instrument for speculation. However, Bitcoin still does not fulfill the financial use case that most people need from a currency: price stability. The price of Bitcoin fluctuates rapidly, making it difficult to use Bitcoin for small purchases such as coffee. Imagine you want to buy a cup of coffee with Bitcoin. The coffee shop owner needs to offer the option to sell you that cup of coffee using Bitcoin as the medium of exchange. This owner must denominate the price of that coffee as some number of Bitcoin. Since the price of Bitcoin fluctuates so rapidly, the coffee shop owner needs to adjust the price of that cup of coffee constantly in order to make sure that the coffee is cheap enough for the consumer to want to buy it, but expensive enough to make a profit. It is hard to assign prices to market goods in terms of Bitcoin because the currency is in constant flux. Even though many of us would like to use Bitcoin in our everyday lives, most marketplaces are denominated in US dollars or other currencies because a marketplace needs a stable currency in order to operate. Rune Christensen is the CEO of MakerDAO, a system that provides a price-stable cryptocurrency. MakerDAO is an elegant set of currencies, collateralized debt, smart contracts, and other incentive tools that result in the creation of several transparent, decentralized financial instruments. Rune joins the show to talk about the importance of stablecoins and how MakerDAO has engineered a decentralized currency that has maintained stability even through tumultuous market conditions.

  • #1101
    Apr 2, 2019 · 1 hr 8 min

    Blitzscaling with Chris Yeh

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Chris Yeh is an entrepreneur, investor, and author. He co-wrote Blitzscaling with LinkedIn founder Reid Hoffman. Blitzscaling is a strategy for growing a company that has found product market fit. Blitzscaling prioritizes speed over efficiency, arguing that fast growth is necessary to achieve “first scaler advantage.” When a company is the first to scale successfully within a large market, that company gains access to a wealth of market opportunities that are not available to companies which are not at scale. Examples of successful Blitzscalers include Airbnb, LinkedIn, Amazon, and Facebook. In the hypergrowth phases of these companies, there were deliberate strategic tradeoffs that caused the company to suffer in the short term in exchange for the chance at market dominance in the long term. Blitzscaling is a broad strategic concept which manifests differently in different companies. When Airbnb was in its early stages of growth in 2011, the company was faced with the existential threat of a European competitor called Wimdu. Wimdu offered to sell to Airbnb, but this would have required the merger of two companies with distinctly different cultures. Instead, Airbnb chose to raise more money and rapidly expand into Europe. In contrast, Google’s rapid path to becoming a dominant information service involved acquisitions that we now see as key Google products, including Android, Google Maps, and Google Earth. Through numerous examples in recent business history, Blitzscaling explores the fundamental tradeoff between speed and efficiency, usually biasing speed as the preferable element. But Blitzscaling does not work for every company. In the food delivery sector, many companies who tried to blitzscale ended up going out of business because they had lowered their prices too much in order to try to earn customer loyalty. By lowering their prices too much, food delivery startups built businesses with fundamentally bad unit economics and a fickle customer base. In other cases, aggressive blitzscaling can work for a short period of time, but can cause a company’s culture to suffer in ways that are very hard to repair. Blitzscaling can also cause problems in a core software product. Growing too quickly can cause a product to have a bloated user interface. If the backend infrastructure layer expands too quickly, sensitive data could be left exposed due to a lack of proper software security policies. Chris Yeh joins the show to talk about the strategy of Blitzscaling and his wide-ranging career. Chris studied creative writing and product design at Stanford before joining DE Shaw, the famous quantitative hedge fund. Later, he became an investor and worked in several leadership roles in software companies. His wide range of experiences make Chris an excellent author and conversationalist. We explored the ideas of both Blitzscaling and his previous book The Alliance, which lays out a modern vision for the dynamic between employers and employees. We also talked about investing, Dungeons and Dragons, and podcasting.

  • #1100
    Apr 1, 2019 · 1 hr 5 min

    Uber Infrastructure with Prashant Varanasi and Akshay Shah

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Uber’s infrastructure supports millions of riders and billions of dollars in transactions. Uber has high throughput and high availability requirements, because users depend on the service for their day-to-day transportation. When Uber was going through hypergrowth in 2015, the number of services was growing rapidly, as was the load across those services. Using a cloud provider was a risky option, because the costs could potentially grow out of control. Uber made a decision early on to invest in physical hardware in order to keep costs at a reasonable level. In the last 3 years, Uber’s infrastructure has stabilized. The platform engineering team has built systems for monitoring, deployment, and service proxying. Developing and maintaining microservices within Uber has become easier. Prashant Varanasi and Akshay Shah are engineers who have been with Uber for more than three years. They work on Uber’s platform engineering team, and their current focus is on the service proxy layer, a sidecar that runs alongside Uber services providing features such as load balancing, service discovery, and rate limiting. Prashant and Akshay join the show to talk about Uber infrastructure, microservices, and the architecture of a service proxy. We also talk in detail about the benefits of using Go for critical systems infrastructure, and some techniques for profiling and debugging in Go.

  • #1099
    Mar 29, 2019 · 46 min

    Workload Scheduling with Brian Grant

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Google has been building large-scale scheduling systems for more than fifteen years. Google Borg was started around 2003, giving engineers at Google a unified platform to issue long-lived service workloads as well as short-lived batch workloads onto a pool of servers. Since the early days of Borg, the scheduler systems built by Google have matured through several iterations. Omega was an effort to improve the internal Borg system, and Kubernetes is an open source container orchestrator built with the learnings of Borg and Omega. A scheduling system needs to be able to accept a wide variety of workload types and find compute resources within a cluster to schedule those workloads onto. There is a wide variety of potential workloads that could be scheduled–batch jobs, stateful services, stateless services, and daemon services. Different workloads can have different priority levels. A high priority workload should be able to find compute resources quickly, and a low priority workload can wait longer to find resources. Brian Grant is a principal engineer at Google. He joins the show to talk about his experience building workload schedulers and designing APIs for engineers to interface with those schedulers.

  • #1098
    Mar 28, 2019 · 49 min

    Peloton: Uber’s Cluster Scheduler with Min Cai and Mayank Bansal

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Google’s Borg system is a cluster manager that powers the applications running across Google’s massive infrastructure. Borg provided inspiration for open source tools like Apache Mesos and Kubernetes. Over the last decade, some of the largest new technology companies have built their own systems that fulfill the roles of cluster management and resource scheduling. Netflix, Twitter, and Facebook have all spoken about their internal projects to make distributed systems resource allocation more economical. These companies find themselves continually reinventing scheduling and orchestration, with inspiration from Google Borg and their own internal experiences running large numbers of containers and virtual machines. Uber’s engineering team has built a cluster scheduler called Peloton. Peloton is based on Apache Mesos, and is architected to handle a wide range of workloads: data science jobs like Hadoop MapReduce; long running services such as a ridesharing marketplace service; monitoring daemons such as Uber’s M3 collector; and database services such as MySQL. Min Cai and Mayank Bansal are engineers at Uber who work on Peloton. When they set out to create Peloton, they looked at the existing schedulers in the ecosystem, including Kubernetes, Mesos, Hadoop’s YARN system, and Borg itself. Both Min and Mayank join the show today to give a brief history of distributed systems schedulers and discuss their work on Peloton. They have been working in the world of distributed systems schedulers for many years–including experiences building core Hadoop infrastructure and virtual machine schedulers at VMware.

  • #1097
    Mar 27, 2019 · 49 min

    Scaling Log Management with Renaud Boutet

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Log management requires the processing and indexing of high volumes of semi-structured data. A log management service takes log data and puts it in a cloud-hosted application so that application operators can access those logs to troubleshoot issues. A large tech company will produce terabytes of logs. Those logs are produced on the host where a service is running. A logging agent on that host will transfer the logs to the log management service in the cloud. Once the logs are in the cloud, they are parsed, indexed, and stored in a way that is easy to query. In 2014, Renaud Boutet co-founded Logmatic, a log management service that eventually became a leading provider. Logmatic was acquired by Datadog, and Renaud now works as a vice president at Datadog. In today’s episode, Renaud joins the show to talk about the architecture of a log management service. We talk about storage tiers, scalability requirements, failover strategies, and logging for serverless functions. Full disclosure: Datadog is a sponsor of Software Engineering Daily.

  • #1096
    Mar 26, 2019 · 1 hr 15 min

    Security Businesses with Steve Herrod

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Steve Herrod was the CTO at VMware and now works as a managing director at General Catalyst, where he focuses on investments relating to security. Large enterprises are difficult to secure. An enterprise has sprawling infrastructure, with both on-prem and cloud infrastructure. Identity management systems, vulnerability scanning, secure network infrastructure, and policy management tools are just a few example areas where enterprises spend billions of dollars on security software. Threats often make their way into an enterprise by way of social engineering. This can result in phishing attacks, corporate espionage, and ransomware. Protecting against social engineering is very difficult, as there are so many channels to communicate through–Facebook Messenger, Linkedin, email, and ad networks can all be used to perform social engineering attacks. Enterprise security software is a very different business from other types of software companies. Unlike developer tools or cloud infrastructure, security software is usually not self-serve. Security solutions usually require a longer sales and integration process with a customer. Steve Herrod joins the show to talk about the enterprise security world, the go-to-market strategy for successful security companies, and his perspective on what makes for a viable venture capital investment.

  • #1095
    Mar 25, 2019 · 50 min

    CodeSandbox: Online Code Editor with Bas Buursma and Ives van Hoorne

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Coding in the browser has been attempted several times in the last decade. Building a development environment in the browser has numerous technical challenges. How does the code execute safely? How do you fit all of the requirements of a development environment into a browser window? How do you get users to switch from their normal IDE (interactive development environment)? CodeSandbox is an online code editor created by Ives van Hoorne and Bas Buursma. CodeSandbox allows users to program and run applications in the browser. It is a full developer platform that allows users the ability to install npm modules, run their code, and share their applications with other users. The engineering problems within CodeSandbox are not easy–building a web-based IDE is complicated. But CodeSandbox is also an exciting project because it lowers the barrier to entry for many newer programmers. The development experience for a new programmer is still a difficult onramp. If you are an experienced developer, you have a workflow that you are comfortable with. It might involve vim, or emacs, or JetBrains IDEs, or Eclipse. But newer developers can find these environments confusing and hard to get started with. The development environments of today are integrated with build tools, Github repositories, and deployment platforms. This can be overwhelming for a newer developer. CodeSandbox is a very visual tool, which makes it especially useful for new developers who learn through seeing examples running live in the browser. CodeSandbox is also used by web developers who want a modern, shareable form of developing software. Ives and Bas join the show to talk about the motivation for CodeSandbox and the engineering challenges they have solved.

  • #1094
    Mar 22, 2019 · 1 hr 2 min

    Apache Superset with Maxime Beauchemin

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Data engineering touches every area of an organization. Engineers need a data platform to build search indexes and microservices. Data scientists need data pipelines to build machine learning models. Business analysts need flexible dashboards to understand the trends and customer use for a product. Max Beauchemin is a data engineer who has worked at Airbnb, Lyft, and Facebook. He’s the creator of two successful open source projects: Apache Airflow and Apache Superset. In a previous show, Max discussed data engineering at Airbnb, and the usage of Airflow. In today’s show, Max discusses the engineering of Apache Superset. Superset is an open source business intelligence web application. Superset allows users to create visualizations, slice and dice their data, and query it. Superset integrates with Druid, a database that supports exploratory, OLAP-style workloads. One reason Superset is distinctive is that it is a full open source application. Many open source projects are tools like databases, command line tools, and web frameworks. Superset is an open source application that can be used by individuals who are not developers–so the audience is wider than the typical open source tool built for engineers. Max joins the show to talk about his experience as a data engineer at Airbnb and Lyft, and the open source projects he has started.

  • #1093
    Mar 21, 2019 · 52 min

    FaunaDB with Evan Weaver

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Twitter’s early engineers faced scalability problems that caused infrastructure failures on a regular basis. The infamous “fail whale” could happen as a result of problems in the application servers, the network, or the database layer. When Twitter was scaling in its early days, the cloud providers were still immature. Engineers did not have access to the autoscaling cloud infrastructure that is available today. The early Twitter architecture was a combination of open source tools and internally created infrastructure custom built for Twitter’s workloads. Evan Weaver was an early engineer at Twitter, and he saw the deficiencies of the data tools that the company had access to. Twitter engineers wanted access to a truly reusable data platform that would fit Twitter’s requirements: high availability, globally replicated, and transactionally consistent. By 2012, Evan had left Twitter and started consulting for other technology companies. He found that databases across the industry were lacking the same properties that Twitter wanted, and the ideas for FaunaDB began to percolate. Around this time, there were two relevant papers about distributed databases that had come out: the Spanner paper from Google and the Calvin paper, a distributed systems paper from Yale. With inspiration from the literature, his time at Twitter, and his knowledge from consulting, Evan started FaunaDB. Seven years later, FaunaDB is a fully fledged open source project as well as a database company with a cloud service offering. Fauna is an OLTP database used by companies like Nvidia, Nextdoor, and Capital One. Evan joins the show to talk about his time spent scaling Twitter and the architecture of FaunaDB.

  • #1092
    Mar 20, 2019 · 53 min

    ElasticSearch at Scale with Volkan Yazici

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Bol.com is the biggest e-commerce company in the Netherlands and Belgium. For 20 years, Bol has been developing its software architecture, which includes a variety of services and databases, and a mix of physical and cloud infrastructure. For an ecommerce company, the search engine is critical for allowing customers to find the products they are looking for. But search also has many applications for internal systems. A search engine is a database with a query engine, and internal application developers want to build on top of that database. Volkan Yazici is an engineer at Bol.com specializing in search and the author of the blog post Using ElasticSearch as the Primary Data Store. In his post, Volkan describes the process of scaling ElasticSearch to fit the use cases of both internal and external users at a large ecommerce company. Volkan joins the show to discuss how search infrastructure at scale can require a carefully architected data pipeline in order to propagate changes to a large data set to a search index.

  • #1091
    Mar 19, 2019 · 55 min

    Serverless GraphQL with Tanmai Gopal

    Upcoming events: A Conversation with Haseeb Qureshi at Cloudflare on April 3, 2019 FindCollabs Hackathon at App Academy on April 6, 2019 Modern web development tools have given frontend developers more power. On the frontend, JavaScript frameworks like React and Vue have become easier to work with. For deployment, tools like Netlify and Zeit give developers a workflow that is tightly integrated with GitHub. At the database layer, autoscaling document storage systems like Firebase and hosted Mongo solutions make it easier to work with objects. There are also a multitude of APIs that give developers rich business functionality out of the box, making it easy to build applications around SMS, payments, and computer vision. If you are building a new application today, you have the option to build it around a completely “serverless” architecture. As the backend and frontend have changed, the middleware to communicate between those layers has also evolved. GraphQL is a modern way of fetching data from disparate data sources. In previous episodes, we have talked about how GraphQL works, and some common patterns for using GraphQL in mature applications. In today’s episode, Tanmai Gopal joins the show to describe how to use GraphQL in newer applications. Tanmai is the CEO of Hasura, a company building tools around GraphQL. He discusses the advantages of using serverless functions together with GraphQL, and how to architect an event-based serverless application.

  • #1090
    Mar 18, 2019 · 1 hr 4 min

    OSS Businesses with Mike Volpi

    In the world of commercial open source, there is plenty of room for both point solution providers and cloud providers. But they are competing for the same customers, and the competitive battlefield is expanding to the nuanced world of software licensing. By changing their licenses, open source projects like Kafka, MongoDB, and Redis can prohibit AWS from certain usage patterns. This might offer some protection for companies based around the point solutions–companies like Confluent and RedisLabs. Beyond the fracas of the battle between cloud providers and point solutions, there are newer open source companies with models that do not fit tightly into any historical business models. HashiCorp makes a suite of differentiated open source tools that have not been seriously contested or offered as a service by cloud providers. GitLab makes an open source platform that is built with monitoring, logging, CI, and code hosting out of the box. As the world of open source business models expands, more companies will find opportunity in open sourcing the code that runs their products. In many cases, they will find that it strengthens their advantage rather than weakens it. The defensibility of many businesses relies more on data and network effects than the contents of the codebase. We may see the default question gradually shift from “why should I open source my codebase?” to “why shouldn’t I open source my codebase?” Mike Volpi is a partner at Index Ventures and has invested in many open source businesses over the last decade. He is on the board of Confluent, Cockroach Labs, Kong, and Elastic. Mike joins the show to share his perspective on open source business models of the past, present, and future.

  • #1089
    Mar 17, 2019 · 47 min

    Crypto Bubble with Haseeb Qureshi

    This is a post written and narrated by Haseeb Qureshi, a cryptocurrency investor and entrepreneur. Haseeb is speaking at an upcoming Software Engineering Daily Meetup. The ICO bubble had no single cause. Mono-causal explanations always fall short in explaining complex phenomena. But its effects are easier to pinpoint. There are now many world class teams well-capitalized to build, scale, and evolve blockchain technology, and tens of millions of people in the world who now understand decentralization, proof of work, and private keys. Looking back, it’s really quite amazing! It comes at a high cost, but Perez hints: it’s likely that bubbles like these are the only way to overcome technological inertia. At the same time, most people had their first interaction with crypto during its orgiastic adolescence. It’s not a great look. But this has been true for every technological revolution of the last 250 years. In that regard, crypto is in good company. I was too young to appreciate the dot com bubble when it happened. It’s strange to say, but I’m glad to have witnessed a speculative bubble from up close. I’ve now got war stories to share with future generations. It was a wild time, when anyone in the world could launch a coin and raise tens of millions of dollars to build a network that no one could control. I don’t think we’ll see anything like that again for a long time. So what happens now? If you believe that crypto has the stuff of a technological revolution, then as Perez puts it, the collapse will pave the way for a more fruitful deployment phase. At the end of the day, I’m an optimist about technology. So it won’t surprise you that I think this deployment phase is coming. But it will be slow, unglamorous, and probably won’t make for nearly as entertaining of headlines. Oh well.

  • #1087
    Mar 15, 2019 · 1 hr 2 min

    GitLab with Sid Sijbrandij

    GitLab is an open source platform for software development. GitLab started with the ability to manage git repositories and now has functionality for collaboration, issue tracking, continuous integration, logging, and tracing. GitLab’s core business is selling to enterprises who want a self-hosted git installation, such as banks or other companies who prefer not to use a git service in the cloud. The vision for GitLab is to provide a platform for managing the full software development lifecycle, from code hosting to deployment–as well as tools for observability and project management. Sid Sijbrandij is the CEO of GitLab and he joins the show to talk about the product, the business, and the company’s vision for the future. GitLab’s strategy is to offer a set of tools that work for developers out of the box, cutting down on time spent integrating each individual vendor.

  • #1086
    Mar 14, 2019 · 1 hr 3 min

    Linux Kernel Development with Shuah Khan

    An operating system kernel manages the system resources that are needed to run applications. The Linux kernel runs most of the smart devices that we interact with, and is the largest open source project in history. Shuah Khan has worked on operating systems for two decades, including 13 years at HP and 5 years at Samsung. She has worked on proprietary operating systems and a variety of Linux operating system environments, including mobile devices. Shuah joins the show to discuss her work within Linux and her experience contributing to open source. Shuah has made significant contributions to kselftest, a set of tests for Linux. Testing the Linux kernel is complicated. Because there is so much depth to the codebase, and such a variety of ways that Linux can be used, there is also a variety of ways that the operating system gets tested. There is smoke testing, performance testing, and regression testing. There are trees of tests, and as a developer you may only want to run a subset of the tests in that tree. The conversation with Shuah ranged from the low level practices of testing the kernel to a high level discussion of how the Linux kernel can reveal dynamics of human nature.

  • #1085
    Mar 13, 2019 · 53 min

    Cryptojacking: Bitcoin Malware with Estaban Vargas

    Malware is malicious software that makes money for the creator of that software. Malware can appear onto a user’s computer if that user visits a malicious website or installs malicious software by accident. There are many types of malware. Spyware sits on your machine and logs your data in order to sell it. Ransomware can lock your computer and demand that you pay money to unlock it. Adware serves you popup ads that you don’t want to see. Cryptojacking is a newer form of malware. Cryptojacking software uses your computer to mine Bitcoin and other cryptocurrencies. Cryptojacking can occur when you visit a website that is running JavaScript that is executing along with the rest of the webpage. When you visit a website with a cryptojacker, your computer will become slower, because your CPU is being taken over to mine cryptocurrency. Cryptojacking can occur anywhere that code runs–and there is a lot of code running on cloud providers. Cloud providers themselves are very secure. But a cloud provider cannot force its customers to be secure. Users who host an insecure application on a cloud provider may get infected with a cryptojacker. If I host a large, complex website on a cloud provider, and I’m serving millions of users, I’m already paying a lot in cloud costs. But when my application gets infected with a cryptojacker, my costs could shoot up. And if I don’t know why my costs are increasing, I might leave the cloud provider. Estaban Vargas is the co-founder of SafeTalpa, a company that provides defense against cryptojackers. Estaban joins the show to explain how cryptojackers work and why cloud providers have trouble defending against them.

  • #1084
    Mar 12, 2019 · 49 min

    Ad Fraud Engineering with Praneet Sharma and Shailin Dhar

    Advertising fraud occurs when a brand pays for an advertisement online and that advertisement is shown to an automated bot account that has been created to view ads. Advertising fraud is rampant on the Internet. It’s not possible to know how much money is lost to ad fraud, but the costs are in the billions of dollars. Praneet Sharma and Shailin Dhar are the founders of Method Media Intelligence, a company that builds solutions around improving advertising quality. In previous shows, Praneet and Shailin have described the online advertising ecosystem in detail. They have told stories of bot farms, replay attacks, and adtech companies. In today’s episode, Praneet and Shailin return to the show to discuss how advertising fraud is getting worse–not better. Praneet and Shailin worked with BuzzFeed reporter Craig Silverman, who was a previous guest on the show to talk about his remarkable findings about mobile advertising fraud, which accounts for hundreds of millions of dollars in theft every year.

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