Do you have a spare Raspberry Pi lying around? And a Bluetooth games controller? Do you have access to boom lifts or other heavy machinery?
Well, then we most certainly (do not) have the project for you.
Allow us to introduce what is (possibly, probably, hopefully) the world’s first Raspberry Pi–controlled boom lift. Weighing in at 13,000lb, this is the epitome of DON’T try this at home.
Raspberry Pi-controlled boom lift
Shared on Reddit over the weekend, u/Ccundiff12’s project received many an upvote and concerned comment, but, as the poster explains, hacking the boom is a personal project for personal use to fix a specific problem — thankfully not something built for the sake of having some fun.
Meet STRETCH. Circa 1989 Genie Boom that I bought (cheap) from a neighbor. I use it to trim trees around my property. Its biggest problem was that it always got stuck. It’s not really an off-road vehicle. It used to take two people to move it around… one to drive the lift, and the other to push it with the tractor when it lost traction. The last time it got stuck, I asked my wife to assist by driving one of the two…….. the next day I started splicing into the control system. Now I can push with the tractor & run the boom via remote!
Backblaze is hiring a Director of Sales. This is a critical role for Backblaze as we continue to grow the team. We need a strong leader who has experience in scaling a sales team and who has an excellent track record for exceeding goals by selling Software as a Service (SaaS) solutions. In addition, this leader will need to be highly motivated, as well as able to create and develop a highly-motivated, success oriented sales team that has fun and enjoys what they do.
The History of Backblaze from our CEO In 2007, after a friend’s computer crash caused her some suffering, we realized that with every photo, video, song, and document going digital, everyone would eventually lose all of their information. Five of us quit our jobs to start a company with the goal of making it easy for people to back up their data.
Like many startups, for a while we worked out of a co-founder’s one-bedroom apartment. Unlike most startups, we made an explicit agreement not to raise funding during the first year. We would then touch base every six months and decide whether to raise or not. We wanted to focus on building the company and the product, not on pitching and slide decks. And critically, we wanted to build a culture that understood money comes from customers, not the magical VC giving tree. Over the course of 5 years we built a profitable, multi-million dollar revenue business — and only then did we raise a VC round.
Fast forward 10 years later and our world looks quite different. You’ll have some fantastic assets to work with:
A brand millions recognize for openness, ease-of-use, and affordability.
A computer backup service that stores over 500 petabytes of data, has recovered over 30 billion files for hundreds of thousands of paying customers — most of whom self-identify as being the people that find and recommend technology products to their friends.
Our B2 service that provides the lowest cost cloud storage on the planet at 1/4th the price Amazon, Google or Microsoft charges. While being a newer product on the market, it already has over 100,000 IT and developers signed up as well as an ecosystem building up around it.
A growing, profitable and cash-flow positive company.
And last, but most definitely not least: a great sales team.
You might be saying, “sounds like you’ve got this under control — why do you need me?” Don’t be misled. We need you. Here’s why:
We have a great team, but we are in the process of expanding and we need to develop a structure that will easily scale and provide the most success to drive revenue.
We just launched our outbound sales efforts and we need someone to help develop that into a fully successful program that’s building a strong pipeline and closing business.
We need someone to work with the marketing department and figure out how to generate more inbound opportunities that the sales team can follow up on and close.
We need someone who will work closely in developing the skills of our current sales team and build a path for career growth and advancement.
We want someone to manage our Customer Success program.
So that’s a bit about us. What are we looking for in you?
Experience: As a sales leader, you will strategically build and drive the territory’s sales pipeline by assembling and leading a skilled team of sales professionals. This leader should be familiar with generating, developing and closing software subscription (SaaS) opportunities. We are looking for a self-starter who can manage a team and make an immediate impact of selling our Backup and Cloud Storage solutions. In this role, the sales leader will work closely with the VP of Sales, marketing staff, and service staff to develop and implement specific strategic plans to achieve and exceed revenue targets, including new business acquisition as well as build out our customer success program.
Leadership: We have an experienced team who’s brought us to where we are today. You need to have the people and management skills to get them excited about working with you. You need to be a strong leader and compassionate about developing and supporting your team.
Data driven and creative: The data has to show something makes sense before we scale it up. However, without creativity, it’s easy to say “the data shows it’s impossible” or to find a local maximum. Whether it’s deciding how to scale the team, figuring out what our outbound sales efforts should look like or putting a plan in place to develop the team for career growth, we’ve seen a bit of creativity get us places a few extra dollars couldn’t.
Jive with our culture: Strong leaders affect culture and the person we hire for this role may well shape, not only fit into, ours. But to shape the culture you have to be accepted by the organism, which means a certain set of shared values. We default to openness with our team, our customers, and everyone if possible. We love initiative — without arrogance or dictatorship. We work to create a place people enjoy showing up to work. That doesn’t mean ping pong tables and foosball (though we do try to have perks & fun), but it means people are friendly, non-political, working to build a good service but also a good place to work.
Do the work: Ideas and strategy are critical, but good execution makes them happen. We’re looking for someone who can help the team execute both from the perspective of being capable of guiding and organizing, but also someone who is hands-on themselves.
Additional Responsibilities needed for this role:
Recruit, coach, mentor, manage and lead a team of sales professionals to achieve yearly sales targets. This includes closing new business and expanding upon existing clientele.
Expand the customer success program to provide the best customer experience possible resulting in upsell opportunities and a high retention rate.
Develop effective sales strategies and deliver compelling product demonstrations and sales pitches.
Acquire and develop the appropriate sales tools to make the team efficient in their daily work flow.
Apply a thorough understanding of the marketplace, industry trends, funding developments, and products to all management activities and strategic sales decisions.
Ensure that sales department operations function smoothly, with the goal of facilitating sales and/or closings; operational responsibilities include accurate pipeline reporting and sales forecasts.
This position will report directly to the VP of Sales and will be staffed in our headquarters in San Mateo, CA.
7 – 10+ years of successful sales leadership experience as measured by sales performance against goals. Experience in developing skill sets and providing career growth and opportunities through advancement of team members.
Background in selling SaaS technologies with a strong track record of success.
Strong presentation and communication skills.
Must be able to travel occasionally nationwide.
BA/BS degree required
Think you want to join us on this adventure? Send an email to email@example.com with the subject “Director of Sales.” (Recruiters and agencies, please don’t email us.) Include a resume and answer these two questions:
How would you approach evaluating the current sales team and what is your process for developing a growth strategy to scale the team?
What are the goals you would set for yourself in the 3 month and 1-year timeframes?
Thank you for taking the time to read this and I hope that this sounds like the opportunity for which you’ve been waiting.
Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.
Now that Amazon Neptune is generally available there are a few changes from the preview:
A large number of performance enhancements and updates
Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.
You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.
We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.
Amazon Neptune Tools Repo This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
Amazon Neptune Samples Repo This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.
Purpose Built Databases
There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.
I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.
As always, feel free to reach out in the comments or on twitter to provide any feedback!
Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to “core truth” of journalists. He is, without any sense of irony, going to call this “Pravda”. This is not simply wrong but evil.
Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.
But what he’s doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn’t measured by whether they follow established journalism ethics, but whether their “core truths” agree with Musk’s “core truths”.
An example of the problem is how the press fixates on Tesla car crashes due to its “autopilot” feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He’s got good reason to be concerned about this.
He argues that autopilot is safer than humans driving, and he’s got the statistics and government studies to back this up. Therefore, the press’s fixation on Tesla crashes is illegitimate “fake news”, titillating the audience with distorted truth.
But here’s the thing: that’s still only Musk’s version of the truth. Yes, on a mile-per-mile basis, autopilot is safer, but there’s nuance here. Autopilot is used primarily on freeways, which already have a low mile-per-mile accident rate. People choose autopilot only when conditions are incredibly safe and drivers are unlikely to have an accident anyway. Musk is therefore being intentionally deceptive comparing apples to oranges. Autopilot may still be safer, it’s just that the numbers Musk uses don’t demonstrate this.
And then there is the truth calling it “autopilot” to begin with, because it isn’t. The public is overrating the capabilities of the feature. It’s little different than “lane keeping” and “adaptive cruise control” you can now find in other cars. In many ways, the technology is behind — my Tesla doesn’t beep at me when a pedestrian walks behind my car while backing up, but virtually every new car on the market does.
Yes, the press unduly covers Tesla autopilot crashes, but Musk has only himself to blame by unduly exaggerating his car’s capabilities by calling it “autopilot”.
What’s “core truth” is thus rather difficult to obtain. What the press satisfies itself with instead is smaller truths, what they can document. The facts are in such cases that the accident happened, and they try to get Tesla or Musk to comment on it.
What you can criticize a journalist for is therefore not “core truth” but whether they did journalism correctly. When such stories criticize “autopilot”, but don’t do their diligence in getting Tesla’s side of the story, then that’s a violation of journalistic practice. When I criticize journalists for their poor handling of stories in my industry, I try to focus on which journalistic principles they get wrong. For example, the NYTimes reporters do a lot of stories quoting anonymous government sources in clear violation of journalistic principles.
If “credibility” is the concern, then it’s the classic Bond villain here that’s the problem: Musk himself. His track record on business statements is abysmal. For example, when he announced the Model 3 he claimed production targets that every Wall Street analyst claimed were absurd. He didn’t make those targets, he didn’t come close. Model 3 production is still lagging behind Musk’s twice adjusted targets.
So who has a credibility gap here, the press, or Musk himself?
Not only is Musk’s credibility problem ironic, so is the name he chose, “Pravada”, the Russian word for truth that was the name of the Soviet Union Communist Party’s official newspaper. This is so absurd this has to be a joke, yet Musk claims to be serious about all this.
Yes, the press has a lot of problems, and if Musk were some journalism professor concerned about journalists meeting the objective standards of their industry (e.g. abusing anonymous sources), then this would be a fine thing. But it’s not. It’s Musk who is upset the press’s version of “core truth” does not agree with his version — a version that he’s proven time and time again differs from “real truth”.
Just in case Musk is serious, I’ve already registered “www.antipravda.com” to start measuring the credibility of statements by billionaire playboy CEOs. Let’s see who blinks first.
I stole the title, with permission, from this tweet:
Regardless of your career path, there’s no denying that attending industry events can provide helpful career development opportunities — not only for improving and expanding your skill sets, but for networking as well. According to this article from PayScale.com, experts estimate that somewhere between 70-85% of new positions are landed through networking.
Narrowing our focus to networking opportunities with cloud computing professionals who’re working on tackling some of today’s most innovative and exciting big data solutions, attending big data-focused sessions at an AWS Global Summit is a great place to start.
AWS Global Summits are free events that bring the cloud computing community together to connect, collaborate, and learn about AWS. As the name suggests, these summits are held in major cities around the world, and attract technologists from all industries and skill levels who’re interested in hearing from AWS leaders, experts, partners, and customers.
In addition to networking opportunities with top cloud technology providers, consultants and your peers in our Partner and Solutions Expo, you’ll also hone your AWS skills by attending and participating in a multitude of education and training opportunities.
Here’s a brief sampling of some of the upcoming sessions relevant to big data professionals:
Be sure to check out the main page for AWS Global Summits, where you can see which cities have AWS Summits planned for 2018, register to attend an upcoming event, or provide your information to be notified when registration opens for a future event.
Abstract: Every day, hundreds of people fly on airline tickets that have been obtained fraudulently. This crime script analysis provides an overview of the trade in these tickets, drawing on interviews with industry and law enforcement, and an analysis of an online blackmarket. Tickets are purchased by complicit travellers or resellers from the online blackmarket. Victim travellers obtain tickets from fake travel agencies or malicious insiders. Compromised credit cards used to be the main method to purchase tickets illegitimately. However, as fraud detection systems improved, offenders displaced to other methods, including compromised loyalty point accounts, phishing, and compromised business accounts. In addition to complicit and victim travellers, fraudulently obtained tickets are used for transporting mules, and for trafficking and smuggling. This research details current prevention approaches, and identifies additional interventions, aimed at the act, the actor, and the marketplace.
Many of my colleagues are fortunate to be able to spend a good part of their day sitting down with and listening to our customers, doing their best to understand ways that we can better meet their business and technology needs. This information is treated with extreme care and is used to drive the roadmap for new services and new features.
AWS customers in the financial services industry (often abbreviated as FSI) are looking ahead to the Fundamental Review of Trading Book (FRTB) regulations that will come in to effect between 2019 and 2021. Among other things, these regulations mandate a new approach to the “value at risk” calculations that each financial institution must perform in the four hour time window after trading ends in New York and begins in Tokyo. Today, our customers report this mission-critical calculation consumes on the order of 200,000 vCPUs, growing to between 400K and 800K vCPUs in order to meet the FRTB regulations. While there’s still some debate about the magnitude and frequency with which they’ll need to run this expanded calculation, the overall direction is clear.
Building a Big Grid In order to make sure that we are ready to help our FSI customers meet these new regulations, we worked with TIBCO to set up and run a proof of concept grid in the AWS Cloud. The periodic nature of the calculation, along with the amount of processing power and storage needed to run it to completion within four hours, make it a great fit for an environment where a vast amount of cost-effective compute power is available on an on-demand basis.
Our customers are already using the TIBCO GridServer on-premises and want to use it in the cloud. This product is designed to run grids at enterprise scale. It runs apps in a virtualized fashion, and accepts requests for resources, dynamically provisioning them on an as-needed basis. The cloud version supports Amazon Linux as well as the PostgreSQL-compatible edition of Amazon Aurora.
Working together with TIBCO, we set out to create a grid that was substantially larger than the current high-end prediction of 800K vCPUs, adding a 50% safety factor and then rounding up to reach 1.3 million vCPUs (5x the size of the largest on-premises grid). With that target in mind, the account limits were raised as follows:
Spot Instance Limit – 120,000
EBS Volume Limit – 120,000
EBS Capacity Limit – 2 PB
If you plan to create a grid of this size, you should also bring your friendly local AWS Solutions Architect into the loop as early as possible. They will review your plans, provide you with architecture guidance, and help you to schedule your run.
Running the Grid We hit the Go button and launched the grid, watching as it bid for and obtained Spot Instances, each of which booted, initialized, and joined the grid within two minutes. The test workload used the Strata open source analytics & market risk library from OpenGamma and was set up with their assistance.
The grid grew to 61,299 Spot Instances (1.3 million vCPUs drawn from 34 instance types spanning 3 generations of EC2 hardware) as planned, with just 1,937 instances reclaimed and automatically replaced during the run, and cost $30,000 per hour to run, at an average hourly cost of $0.078 per vCPU. If the same instances had been used in On-Demand form, the hourly cost to run the grid would have been approximately $93,000.
Despite the scale of the grid, prices for the EC2 instances did not move during the bidding process. This is due to the overall size of the AWS Cloud and the smooth price change model that we launched late last year.
To give you a sense of the compute power, we computed that this grid would have taken the #1 position on the TOP 500 supercomputer list in November 2007 by a considerable margin, and the #2 position in June 2008. Today, it would occupy position #360 on the list.
I hope that you enjoyed this AWS success story, and that it gives you an idea of the scale that you can achieve in the cloud!
Bad software is everywhere. One can even claim that every software is bad. Cool companies, tech giants, established companies, all produce bad software. And no, yours is not an exception.
Who’s to blame for bad software? It’s all complicated and many factors are intertwined – there’s business requirements, there’s organizational context, there’s lack of sufficient skilled developers, there’s the inherent complexity of software development, there’s leaky abstractions, reliance on 3rd party software, consequences of wrong business and purchase decisions, time limitations, flawed business analysis, etc. So yes, despite the catchy title, I’m aware it’s actually complicated.
But in every “it’s complicated” scenario, there’s always one or two factors that are decisive. All of them contribute somehow, but the major drivers are usually a handful of things. And in the case of base software, I think it’s the fault of technical people. Developers, architects, ops.
We don’t seem to care about best practices. And I’ll do some nasty generalizations here, but bear with me. We can spend hours arguing about tabs vs spaces, curly bracket on new line, git merge vs rebase, which IDE is better, which framework is better and other largely irrelevant stuff. But we tend to ignore the important aspects that span beyond the code itself. The context in which the code lives, the non-functional requirements – robustness, security, resilience, etc.
We don’t seem to get security. Even trivial stuff such as user authentication is almost always implemented wrong. These days Twitter and GitHub realized they have been logging plain-text passwords, for example, but that’s just the tip of the iceberg. Too often we ignore the security implications.
“But the business didn’t request the security features”, one may say. The business never requested 2-factor authentication, encryption at rest, PKI, secure (or any) audit trail, log masking, crypto shredding, etc., etc. Because the business doesn’t know these things – we do and we have to put them on the backlog and fight for them to be implemented. Each organization has its specifics and tech people can influence the backlog in different ways, but almost everywhere we can put things there and prioritize them.
The other aspect is testing. We should all be well aware by now that automated testing is mandatory. We have all the tools in the world for unit, functional, integration, performance and whatnot testing, and yet many software projects lack the necessary test coverage to be able to change stuff without accidentally breaking things. “But testing takes time, we don’t have it”. We are perfectly aware that testing saves time, as we’ve all had those “not again!” recurring bugs. And yet we think of all sorts of excuses – “let the QAs test it”, we have to ship that now, we’ll test it later”, “this is too trivial to be tested”, etc.
And you may say it’s not our job. We don’t define what has do be done, we just do it. We don’t define the budget, the scope, the features. We just write whatever has been decided. And that’s plain wrong. It’s not our job to make money out of our code, and it’s not our job to define what customers need, but apart from that everything is our job. The way the software is structured, the security aspects and security features, the stability of the code base, the way the software behaves in different environments. The non-functional requirements are our job, and putting them on the backlog is our job.
You’ve probably heard that every software becomes “legacy” after 6 months. And that’s because of us, our sloppiness, our inability to mitigate external factors and constraints. Too often we create a mess through “just doing our job”.
And of course that’s a generalization. I happen to know a lot of great professionals who don’t make these mistakes, who strive for excellence and implement things the right way. But our industry as a whole doesn’t. Our industry as a whole produces bad software. And it’s our fault, as developers – as the only people who know why a certain piece of software is bad.
In a talk of his, Bob Martin warns us of the risks of our sloppiness. We have been building websites so far, but we are more and more building stuff that interacts with the real world, directly and indirectly. Ultimately, lives may depend on our software (like the recent unfortunate death caused by a self-driving car). And I’ll agree with Uncle Bob that it’s high time we self-regulate as an industry, before some technically incompetent politician decides to do that.
How, I don’t know. We’ll have to think more about it. But I’m pretty sure it’s our fault that software is bad, and no amount of blaming the management, the budget, the timing, the tools or the process can eliminate our responsibility.
Why do I insist on bashing my fellow software engineers? Because if we start looking at software development with more responsibility; with the fact that if it fails, it’s our fault, then we’re more likely to get out of our current bug-ridden, security-flawed, fragile software hole and really become the experts of the future.
Some gaming consoles make it easy to stream to Twitch, some gaming consoles don’t (come on, Nintendo). So for those that don’t, I’ve made this beta version of the “Twitch-O-Matic”. No it doesn’t chop onions or fold your laundry, but what it DOES do is stream anything with HDMI output to your Twitch channel with the simple push of a button!
eSports and online game streaming
Interest in eSports has skyrocketed over the last few years, with viewership numbers in the hundreds of millions, sponsorship deals increasing in value and prestige, and tournament prize funds reaching millions of dollars. So it’s no wonder that more and more gamers are starting to stream live to online platforms in order to boost their fanbase and try to cash in on this growing industry.
Streaming to Twitch
Launched in 2011, Twitch.tv is an online live-streaming platform with a primary focus on video gaming. Users can create accounts to contribute their comments and content to the site, as well as watching live-streamed gaming competitions and broadcasts. With a staggering fifteen million daily users, Twitch is accessible via smartphone and gaming console apps, smart TVs, computers, and tablets. But if you want to stream to Twitch, you may find yourself using third-party software in order to do so. And with more buttons to click and more wires to plug in for older, app-less consoles, streaming can get confusing.
Side note: we Tinkernut
We’ve featured Tinkernut a few times on the Raspberry Pi blog – his tutorials are clear, his projects are interesting and useful, and his live-streamed comment videos for every build are a nice touch to sharing homebrew builds on the internet.
So, yes, we love him. [This is true. Alex never shuts up about him. – Ed.] And since he has over 500K subscribers on YouTube, we’re obviously not the only ones. We wave our Tinkernut flags with pride.
The Raspberry Pi Zero W is connected to the HDMI to CSI adapter via the camera connector, in the same way you’d attach the camera ribbon. Tinkernut uses a standard Raspbian image on an 8GB SD card, with SSH enabled for remote access from his laptop. He uses the simple command Raspivid to test the HDMI connection by recording ten seconds of video footage from his console.
One lead is all you need
Once you have the Pi receiving video from your console, you can connect to Twitch using your Twitch stream key, which you can find by logging in to your account at Twitch.tv. Tinkernut’s tutorial gives you all the commands you need to stream from your Pi.
To up the aesthetic impact of your project, adding buttons and backlights is fairly straightforward.
Pretty LED frills
To run the stream command, Tinketnut uses a button: press once to start the stream, press again to stop. Pressing the button also turns on the LED backlight, so it’s obvious when streaming is in progress.
For the full code and 3D-printable case STL file, head to Tinketnut’s hackster.io project page. And if you’re already using a Raspberry Pi for Twitch streaming, share your build setup with us. Cheers!
Enterprises adopt containers because they recognize the benefits: speed, agility, portability, and high compute density. They understand how accelerating application delivery and deployment pipelines makes it possible to rapidly slipstream new features to customers. Although the benefits are indisputable, this acceleration raises concerns about security and corporate compliance with software governance. In this blog post, I provide a solution that shows how Layered Insight, the pioneer and global leader in container-native application protection, can be used with seamless application build and delivery pipelines like those available in AWS CodeBuild to address these concerns.
Layered Insight solutions
Layered Insight enables organizations to unify DevOps and SecOps by providing complete visibility and control of containerized applications. Using the industry’s first embedded security approach, Layered Insight solves the challenges of container performance and protection by providing accurate insight into container images, adaptive analysis of running containers, and automated enforcement of container behavior.
AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can get started quickly by using prepackaged build environments, or you can create custom build environments that use your own build tools.
Security and compliance concerns span the lifecycle of application containers. Common concerns include:
Visibility into the container images. You need to verify the software composition information of the container image to determine whether known vulnerabilities associated with any of the software packages and libraries are included in the container image.
Governance of container images is critical because only certain open source packages/libraries, of specific versions, should be included in the container images. You need support for mechanisms for blacklisting all container images that include a certain version of a software package/library, or only allowing open source software that come with a specific type of license (such as Apache, MIT, GPL, and so on). You need to be able to address challenges such as:
· Defining the process for image compliance policies at the enterprise, department, and group levels.
· Preventing the images that fail the compliance checks from being deployed in critical environments, such as staging, pre-prod, and production.
Visibility into running container instances is critical, including:
· CPU and memory utilization.
· Security of the build environment.
· All activities (system, network, storage, and application layer) of the application code running in each container instance.
Protection of running container instances that is:
· Zero-touch to the developers (not an SDK-based approach).
· Zero touch to the DevOps team and doesn’t limit the portability of the containerized application.
· This protection must retain the option to switch to a different container stack or orchestration layer, or even to a different Container as a Service (CaaS ).
· And it must be a fully automated solution to SecOps, so that the SecOps team doesn’t have to manually analyze and define detailed blacklist and whitelist policies.
In AWS CodeCommit, we have three projects: ● “Democode” is a simple Java application, with one buildspec to build the app into a Docker container (run by build-demo-image CodeBuild project), and another to instrument said container (instrument-image CodeBuild project). The resulting container is stored in ECR repo javatestasjavatest:20180415-layered. This instrumented container is running in AWS Fargate cluster demo-java-appand can be seen in the Layered Insight runtime console as the javatestapplication in us-east-1. ● aws-codebuild-docker-imagesis a clone of the official aws-codebuild-docker-images repo on GitHub . This CodeCommit project is used by the build-python-builder CodeBuild project to build the python 3.3.6 codebuild image and is stored at the codebuild-python ECR repo. We then manually instructed the Layered Insight console to instrument the image. ● scan-java-imagecontains just a buildspec.yml file. This file is used by the scan-java-image CodeBuild project to instruct Layered Assessment to perform a vulnerability scan of the javatest container image built previously, and then run the scan results through a compliance policy that states there should be no medium vulnerabilities. This build fails — but in this case that is a success: the scan completes successfully, but compliance fails as there are medium-level issues found in the scan.
This build is performed using the instrumented version of the Python 3.3.6 CodeBuild image, so the activity of the processes running within the build are recorded each time within the LI console.
Build container image
Create or use a CodeCommit project with your application. To build this image and store it in Amazon Elastic Container Registry (Amazon ECR), add a buildspec file to the project and build a container image and create a CodeBuild project.
Scan container image
Once the image is built, create a new buildspec in the same project or a new one that looks similar to below (update ECR URL as necessary):
- echo Pulling down LI Scan API client scripts
- git clone https://github.com/LayeredInsight/scan-api-example-python.git
- echo Setting up LI Scan API client
- cd scan-api-example-python
- pip install layint_scan_api
- pip install -r requirements.txt
- echo Scanning container started on `date`
- IMAGEID=$(./li_add_image --name <aws-region>.amazonaws.com/javatest:20180415)
- ./li_wait_for_scan -v --imageid $IMAGEID
- ./li_run_image_compliance -v --imageid $IMAGEID --policyid PB15260f1acb6b2aa5b597e9d22feffb538256a01fbb4e5a95
Add the buildspec file to the git repo, push it, and then build a CodeBuild project using with the instrumented Python 3.3.6 CodeBuild image at <aws-region>.amazonaws.com/codebuild-python:3.3.6-layered. Set the following environment variables in the CodeBuild project: ● LI_APPLICATIONNAME – name of the build to display ● LI_LOCATION – location of the build project to display ● LI_API_KEY – ApiKey:<key-name>:<api-key> ● LI_API_HOST – location of the Layered Insight API service
Instrument container image
Next, to instrument the new container image:
In the Layered Insight runtime console, ensure that the ECR registry and credentials are defined (click the Setup icon and the ‘+’ sign on the top right of the screen to add a new container registry). Note the name given to the registry in the console, as this needs to be referenced in the li_add_imagecommand in the script, below.
Next, add a new buildspec (with a new name) to the CodeCommit project, such as the one shown below. This code will download the Layered Insight runtime client, and use it to instruct the Layered Insight service to instrument the image that was just built:
echo Pulling down LI API Runtime client scripts
git clone https://github.com/LayeredInsight/runtime-api-example-python
echo Setting up LI API client
pip install layint-runtime-api
pip install -r requirements.txt
echo Instrumentation started on `date`
./li_add_image --registry "Javatest ECR" --name IMAGE_NAME:TAG --description "IMAGE DESCRIPTION" --policy "Default Policy" --instrument --wait --verbose
Commit and push the new buildspec file.
Going back to CodeBuild, create a new project, with the same CodeCommit repo, but this time select the new buildspec file. Use a Python 3.3.6 builder – either the AWS or LI Instrumented version.
Run the build, again on the master branch.
If everything runs successfully, a new image should appear in the ECR registry with a -layered suffix. This is the instrumented image.
Run instrumented container image
When the instrumented container is now run — in ECS, Fargate, or elsewhere — it will log data back to the Layered Insight runtime console. It’s appearance in the console can be modified by setting the LI_APPLICATIONNAME and LI_LOCATION environment variables when running the container.
In the above blog we have provided you steps needed to embed governance and runtime security in your build pipelines running on AWS CodeBuild using Layered Insight.
This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team.
Healthcare data is generated at an ever-increasing rate and is predicted to reach 35 zettabytes by 2020. Being able to cost-effectively and securely manage this data whether for patient care, research or legal reasons is increasingly important for healthcare providers.
Healthcare providers must have the ability to ingest, store and protect large volumes of data including clinical, genomic, device, financial, supply chain, and claims. AWS is well-suited to this data deluge with a wide variety of ingestion, storage and security services (e.g. AWS Direct Connect, Amazon Kinesis Streams, Amazon S3, Amazon Macie) for customers to handle their healthcare data. In a recent Healthcare IT News article, healthcare thought-leader, John Halamka, noted, “I predict that five years from now none of us will have datacenters. We’re going to go out to the cloud to find EHRs, clinical decision support, analytics.”
I realize simply storing this data is challenging enough. Magnifying the problem is the fact that healthcare data is increasingly attractive to cyber attackers, making security a top priority. According to Mariya Yao in her Forbes column, it is estimated that individual medical records can be worth hundreds or even thousands of dollars on the black market.
In this first of a 2-part post, I will address the value that AWS can bring to customers for ingesting, storing and protecting provider’s healthcare data. I will describe key components of any cloud-based healthcare workload and the services AWS provides to meet these requirements. In part 2 of this post we will dive deep into the AWS services used for advanced analytics, artificial intelligence and machine learning.
The data tsunami is upon us
So where is this data coming from? In addition to the ubiquitous electronic health record (EHR), the sources of this data include:
devices such as MRIs, x-rays and ultrasounds
sensors and wearables for patients
medical equipment telemetry
Additional sources of data come from non-clinical, operational systems such as:
claims and billing
Data from these sources can be structured (e.g., claims data) as well as unstructured (e.g., clinician notes). Some data comes across in streams such as that taken from patient monitors, while some comes in batch form. Still other data comes in near-real time such as HL7 messages. All of this data has retention policies dictating how long it must be stored. Much of this data is stored in perpetuity as many systems in use today have no purge mechanism. AWS has services to manage all these data types as well as their retention, security and access policies.
Imaging is a significant contributor to this data tsunami. Increasing demand for early-stage diagnoses along with aging populations drive increasing demand for images from CT, PET, MRI, ultrasound, digital pathology, X-ray and fluoroscopy. For example, a thin-slice CT image can be hundreds of megabytes. Increasing demand and strict retention policies make storage costly.
Due to the plummeting cost of gene sequencing, molecular diagnostics (including liquid biopsy) is a large contributor to this data deluge. Many predict that as the value of molecular testing becomes more identifiable, the reimbursement models will change and it will increasingly become the standard of care. According to the Washington Post article “Sequencing the Genome Creates so Much Data We Don’t Know What to do with It,”
“Some researchers predict that up to one billion people will have their genome sequenced by 2025 generating up to 40 exabytes of data per year.”
Although genomics is primarily used for oncology diagnostics today, it’s also used for other purposes, pharmacogenomics — used to understand how an individual will metabolize a medication.
It is increasingly challenging for the typical hospital, clinic or physician practice to securely store, process and manage this data without cloud adoption.
Amazon has a variety of ingestion techniques depending on the nature of the data including size, frequency and structure. AWS Snowball and AWS Snowmachine are appropriate for extremely-large, secure data transfers whether one time or episodic. AWS Glue is a fully-managed ETL service for securely moving data from on-premise to AWS and Amazon Kinesis can be used for ingesting streaming data.
Amazon S3, Amazon S3 IA, and Amazon Glacier are economical, data-storage services with a pay-as-you-go pricing model that expand (or shrink) with the customer’s requirements.
The above architecture has four distinct components – ingestion, storage, security, and analytics. In this post I will dive deeper into the first three components, namely ingestion, storage and security. In part 2, I will look at how to use AWS’ analytics services to draw value on, and optimize, your healthcare data.
A typical provider data center will consist of many systems with varied datasets. AWS provides multiple tools and services to effectively and securely connect to these data sources and ingest data in various formats. The customers can choose from a range of services and use them in accordance with the use case.
For use cases involving one-time (or periodic), very large data migrations into AWS, customers can take advantage of AWS Snowball devices. These devices come in two sizes, 50 TB and 80 TB and can be combined together to create a petabyte scale data transfer solution.
The devices are easy to connect and load and they are shipped to AWS avoiding the network bottlenecks associated with such large-scale data migrations. The devices are extremely secure supporting 256-bit encryption and come in a tamper-resistant enclosure. AWS Snowball imports data in Amazon S3 which can then interface with other AWS compute services to process that data in a scalable manner.
For use cases involving a need to store a portion of datasets on premises for active use and offload the rest on AWS, the Amazon storage gateway service can be used. The service allows you to seamlessly integrate on premises applications via standard storage protocols like iSCSI or NFS mounted on a gateway appliance. It supports a file interface, a volume interface and a tape interface which can be utilized for a range of use cases like disaster recovery, backup and archiving, cloud bursting, storage tiering and migration.
By using the AWS proposed reference architecture for disaster recovery, healthcare providers can ensure their data assets are securely stored on the cloud and are easily accessible in the event of a disaster. The “AWS Disaster Recovery” whitepaper includes details on options available to customers based on their desired recovery time objective (RTO) and recovery point objective (RPO).
AWS is an ideal destination for offloading large volumes of less-frequently-accessed data. These datasets are rarely used in active compute operations but are exceedingly important to retain for reasons like compliance. By storing these datasets on AWS, customers can take advantage of the highly-durable platform to securely store their data and also retrieve them easily when they need to. For more details on how AWS enables customers to run back and archival use cases on AWS, please refer to the following set of whitepapers.
A healthcare provider may have a variety of databases spread throughout the hospital system supporting critical applications such as EHR, PACS, finance and many more. These datasets often need to be aggregated to derive information and calculate metrics to optimize business processes. AWS Glue is a fully-managed Extract, Transform and Load (ETL) service that can read data from a JDBC-enabled, on-premise database and transfer the datasets into AWS services like Amazon S3, Amazon Redshift and Amazon RDS. This allows customers to create transformation workflows that integrate smaller datasets from multiple sources and aggregates them on AWS.
Healthcare providers deal with a variety of streaming datasets which often have to be analyzed in near real time. These datasets come from a variety of sources such as sensors, messaging buses and social media, and often do not adhere to an industry standard. The Amazon Kinesis suite of services, that includes Amazon Kinesis Streams, Amazon Kinesis Firehose, and Amazon Kinesis Analytics, are the ideal set of services to accomplish the task of deriving value from streaming data.
Example: Using AWS Glue to de-identify and ingest healthcare data into S3 Let’s consider a scenario in which a provider maintains patient records in a database they want to ingest into S3. The provider also wants to de-identify the data by stripping personally- identifiable attributes and store the non-identifiable information in an S3 bucket. This bucket is different from the one that contains identifiable information. Doing this allows the healthcare provider to separate sensitive information with more restrictions set up via S3 bucket policies.
To ingest records into S3, we create a Glue job that reads from the source database using a Glue connection. The connection is also used by a Glue crawler to populate the Glue data catalog with the schema of the source database. We will use the Glue development endpoint and a zeppelin notebook server on EC2 to develop and execute the job.
Step 1: Import the necessary libraries and also set a glue context which is a wrapper on the spark context:
Step 2: Create a dataframe from the source data. I call the dataframe “readmissionsdata”. Here is what the schema would look like:
Step 3: Now select the columns that contains indentifiable information and store it in a new dataframe. Call the new dataframe “phi”.
Step 4: Non-PHI columns are stored in a separate dataframe. Call this dataframe “nonphi”.
Step 5: Write the two dataframes into two separate S3 buckets
Once successfully executed, the PHI and non-PHI attributes are stored in two separate files in two separate buckets that can be individually maintained.
In 2016, 327 healthcare providers reported a protected health information (PHI) breach, affecting 16.4m patient records. There have been 342 data breaches reported in 2017 — involving 3.2 million patient records.
To date, AWS has released 51 HIPAA-eligible services to help customers address security challenges and is in the process of making many more services HIPAA-eligible. These HIPAA-eligible services (along with all other AWS services) help customers build solutions that comply with HIPAA security and auditing requirements. A catalogue of HIPAA-enabled services can be found at AWS HIPAA-eligible services. It is important to note that AWS manages physical and logical access controls for the AWS boundary. However, the overall security of your workloads is a shared responsibility, where you are responsible for controlling user access to content on your AWS accounts.
AWS storage services allow you to store data efficiently while maintaining high durability and scalability. By using Amazon S3 as the central storage layer, you can take advantage of the Amazon S3 storage management features to get operational metrics on your data sets and transition them between various storage classes to save costs. By tagging objects on Amazon S3, you can build a governance layer on Amazon S3 to grant role based access to objects using Amazon IAM and Amazon S3 bucket policies.
To learn more about the Amazon S3 storage management features, see the following link.
In the example above, we are storing the PHI information in a bucket named “phi.” Now, we want to protect this information to make sure its encrypted, does not have unauthorized access, and all access requests to the data are logged.
Encryption: S3 provides settings to enable default encryption on a bucket. This ensures any object in the bucket is encrypted by default.
Logging: S3 provides object level logging that can be used to capture all API calls to the object. The API calls are logged in cloudtrail for easy access and consolidation. Moreover, it also supports events to proactively alert customers of read and write operations.
Access control: Customers can use S3 bucket policies and IAM policies to restrict access to the phi bucket. It can also put a restriction to enforce multi-factor authentication on the bucket. For example, the following policy enforces multi-factor authentication on the phi bucket:
In Part 1 of this blog, we detailed the ingestion, storage, security and management of healthcare data on AWS. Stay tuned for part two where we are going to dive deep into optimizing the data for analytics and machine learning.
Last week, we shared the first half of our Q&A with Raspberry Pi Trading CEO and Raspberry Pi creator Eben Upton. Today we follow up with all your other questions, including your expectations for a Raspberry Pi 4, Eben’s dream add-ons, and whether we really could go smaller than the Zero.
Get your questions to us now using #AskRaspberryPi on Twitter
With internet security becoming more necessary, will there be automated versions of VPN on an SD card?
There are already third-party tools which turn your Raspberry Pi into a VPN endpoint. Would we do it ourselves? Like the power button, it’s one of those cases where there are a million things we could do and so it’s more efficient to let the community get on with it.
Just to give a counterexample, while we don’t generally invest in optimising for particular use cases, we did invest a bunch of money into optimising Kodi to run well on Raspberry Pi, because we found that very large numbers of people were using it. So, if we find that we get half a million people a year using a Raspberry Pi as a VPN endpoint, then we’ll probably invest money into optimising it and feature it on the website as we’ve done with Kodi. But I don’t think we’re there today.
Have you ever seen any Pis running and doing important jobs in the wild, and if so, how does it feel?
It’s amazing how often you see them driving displays, for example in radio and TV studios. Of course, it feels great. There’s something wonderful about the geographic spread as well. The Raspberry Pi desktop is quite distinctive, both in its previous incarnation with the grey background and logo, and the current one where we have Greg Annandale’s road picture.
And so it’s funny when you see it in places. Somebody sent me a video of them teaching in a classroom in rural Pakistan and in the background was Greg’s picture.
Raspberry Pi 4!?!
There will be a Raspberry Pi 4, obviously. We get asked about it a lot. I’m sticking to the guidance that I gave people that they shouldn’t expect to see a Raspberry Pi 4 this year. To some extent, the opportunity to do the 3B+ was a surprise: we were surprised that we’ve been able to get 200MHz more clock speed, triple the wireless and wired throughput, and better thermals, and still stick to the $35 price point.
We’re up against the wall from a silicon perspective; we’re at the end of what you can do with the 40nm process. It’s not that you couldn’t clock the processor faster, or put a larger processor which can execute more instructions per clock in there, it’s simply about the energy consumption and the fact that you can’t dissipate the heat. So we’ve got to go to a smaller process node and that’s an order of magnitude more challenging from an engineering perspective. There’s more effort, more risk, more cost, and all of those things are challenging.
With 3B+ out of the way, we’re going to start looking at this now. For the first six months or so we’re going to be figuring out exactly what people want from a Raspberry Pi 4. We’re listening to people’s comments about what they’d like to see in a new Raspberry Pi, and I’m hoping by early autumn we should have an idea of what we want to put in it and a strategy for how we might achieve that.
Could you go smaller than the Zero?
The challenge with Zero as that we’re periphery-limited. If you run your hand around the unit, there is no edge of that board that doesn’t have something there. So the question is: “If you want to go smaller than Zero, what feature are you willing to throw out?”
It’s a single-sided board, so you could certainly halve the PCB area if you fold the circuitry and use both sides, though you’d have to lose something. You could give up some GPIO and go back to 26 pins like the first Raspberry Pi. You could give up the camera connector, you could go to micro HDMI from mini HDMI. You could remove the SD card and just do USB boot. I’m inventing a product live on air! But really, you could get down to two thirds and lose a bunch of GPIO – it’s hard to imagine you could get to half the size.
What’s the one feature that you wish you could outfit on the Raspberry Pi that isn’t cost effective at this time? Your dream feature.
Well, more memory. There are obviously technical reasons why we don’t have more memory on there, but there are also market reasons. People ask “why doesn’t the Raspberry Pi have more memory?”, and my response is typically “go and Google ‘DRAM price’”. We’re used to the price of memory going down. And currently, we’re going through a phase where this has turned around and memory is getting more expensive again.
Machine learning would be interesting. There are machine learning accelerators which would be interesting to put on a piece of hardware. But again, they are not going to be used by everyone, so according to our method of pricing what we might add to a board, machine learning gets treated like a $50 chip. But that would be lovely to do.
Which citizen science projects using the Pi have most caught your attention?
I like the wildlife camera projects. We live out in the countryside in a little village, and we’re conscious of being surrounded by nature but we don’t see a lot of it on a day-to-day basis. So I like the nature cam projects, though, to my everlasting shame, I haven’t set one up yet. There’s a range of them, from very professional products to people taking a Raspberry Pi and a camera and putting them in a plastic box. So those are good fun.
How does it feel to go to bed every day knowing you’ve changed the world for the better in such a massive way?
What feels really good is that when we started this in 2006 nobody else was talking about it, but now we’re part of a very broad movement.
We were in a really bad way: we’d seen a collapse in the number of applicants applying to study Computer Science at Cambridge and elsewhere. In our view, this reflected a move away from seeing technology as ‘a thing you do’ to seeing it as a ‘thing that you have done to you’. It is problematic from the point of view of the economy, industry, and academia, but most importantly it damages the life prospects of individual children, particularly those from disadvantaged backgrounds. The great thing about STEM subjects is that you can’t fake being good at them. There are a lot of industries where your Dad can get you a job based on who he knows and then you can kind of muddle along. But if your dad gets you a job building bridges and you suck at it, after the first or second bridge falls down, then you probably aren’t going to be building bridges anymore. So access to STEM education can be a great driver of social mobility.
By the time we were launching the Raspberry Pi in 2012, there was this wonderful movement going on. Code Club, for example, and CoderDojo came along. Lots of different ways of trying to solve the same problem. What feels really, really good is that we’ve been able to do this as part of an enormous community. And some parts of that community became part of the Raspberry Pi Foundation – we merged with Code Club, we merged with CoderDojo, and we continue to work alongside a lot of these other organisations. So in the two seconds it takes me to fall asleep after my face hits the pillow, that’s what I think about.
We’re currently advertising a Programme Manager role in New Delhi, India. Did you ever think that Raspberry Pi would be advertising a role like this when you were bringing together the Foundation?
No, I didn’t.
But if you told me we were going to be hiring somewhere, India probably would have been top of my list because there’s a massive IT industry in India. When we think about our interaction with emerging markets, India, in a lot of ways, is the poster child for how we would like it to work. There have already been some wonderful deployments of Raspberry Pi, for example in Kerala, without our direct involvement. And we think we’ve got something that’s useful for the Indian market. We have a product, we have clubs, we have teacher training. And we have a body of experience in how to teach people, so we have a physical commercial product as well as a charitable offering that we think are a good fit.
It’s going to be massive.
What is your favourite BBC type-in listing?
There was a game called Codename: Druid. There is a famous game called Codename: Droid which was the sequel to Stryker’s Run, which was an awesome, awesome game. And there was a type-in game called Codename: Druid, which was at the bottom end of what you would consider a commercial game.
And I remember typing that in. And what was really cool about it was that the next month, the guy who wrote it did another article that talks about the memory map and which operating system functions used which bits of memory. So if you weren’t going to do disc access, which bits of memory could you trample on and know the operating system would survive.
I still like type-in listings. The Raspberry Pi 2018 Annual has a type-in listing that I wrote for a Babbage versus Bugs game. I will say that’s not the last type-in listing you will see from me in the next twelve months. And if you download the PDF, you could probably copy and paste it into your favourite text editor to save yourself some time.
This article, pointed out by @TheGrugq, is stupid enough that it’s worth rebutting.
“The views and opinions expressed are those of the author and not necessarily the positions of the U.S. Army, Department of Defense, or the U.S. Government.” <- I sincerely hope so… “the cyber guns of August” https://t.co/xdybbr5B0E
The article starts with the question “Why did the lessons of Stuxnet, Wannacry, Heartbleed and Shamoon go unheeded?“. It then proceeds to ignore the lessons of those things.
Some of the actual lessons should be things like how Stuxnet crossed air gaps, how Wannacry spread through flat Windows networking, how Heartbleed comes from technical debt, and how Shamoon furthers state aims by causing damage.
But this article doesn’t cover the technical lessons. Instead, it thinks the lesson should be the moral lesson, that we should take these things more seriously. But that’s stupid. It’s the sort of lesson people teach you that know nothing about the topic. When you have nothing of value to contribute to a topic you can always take the moral high road and criticize everyone for being morally weak for not taking it more seriously. Obviously, since doctors haven’t cured cancer yet, it’s because they don’t take the problem seriously.
The article continues to ignore the lesson of these cyber attacks and instead regales us with a list of military lessons from WW I and WW II. This makes the same flaw that many in the military make, trying to understand cyber through analogies with the real world. It’s not that such lessons could have no value, it’s that this article contains a poor list of them. It seems to consist of a random list of events that appeal to the author rather than events that have bearing on cybersecurity.
Then, in case we don’t get the point, the article bullies us with hyperbole, cliches, buzzwords, bombastic language, famous quotes, and citations. It’s hard to see how most of them actually apply to the text. Rather, it seems like they are included simply because he really really likes them.
The article invests much effort in discussing the buzzword “OODA loop”. Most attacks in cyberspace don’t have one. Instead, attackers flail around, trying lots of random things, overcoming defense with brute-force rather than an understanding of what’s going on. That’s obviously the case with Wannacry: it was an accident, with the perpetrator experimenting with what would happen if they added the ETERNALBLUE exploit to their existing ransomware code. The consequence was beyond anybody’s ability to predict.
You might claim that this is just the first stage, that they’ll loop around, observe Wannacry’s effects, orient themselves, decide, then act upon what they learned. Nope. Wannacry burned the exploit. It’s essentially removed any vulnerable systems from the public Internet, thereby making it impossible to use what they learned. It’s still active a year later, with infected systems behind firewalls busily scanning the Internet so that if you put a new system online that’s vulnerable, it’ll be taken offline within a few hours, before any other evildoer can take advantage of it.
See what I’m doing here? Learning the actual lessons of things like Wannacry? The thing the above article fails to do??
The article has a humorous paragraph on “defense in depth”, misunderstanding the term. To be fair, it’s the cybersecurity industry’s fault: they adopted then redefined the term. That’s why there’s two separate articles on Wikipedia: one for the old military term (as used in this article) and one for the new cybersecurity term.
As used in the cybersecurity industry, “defense in depth” means having multiple layers of security. Many organizations put all their defensive efforts on the perimeter, and none inside a network. The idea of “defense in depth” is to put more defenses inside the network. For example, instead of just one firewall at the edge of the network, put firewalls inside the network to segment different subnetworks from each other, so that a ransomware infection in the customer support computers doesn’t spread to sales and marketing computers.
The article talks about exploiting WiFi chips to bypass the defense in depth measures like browser sandboxes. This is conflating different types of attacks. A WiFi attack is usually considered a local attack, from somebody next to you in bar, rather than a remote attack from a server in Russia. Moreover, far from disproving “defense in depth” such WiFi attacks highlight the need for it. Namely, phones need to be designed so that successful exploitation of other microprocessors (namely, the WiFi, Bluetooth, and cellular baseband chips) can’t directly compromise the host system. In other words, once exploited with “Broadpwn”, a hacker would need to extend the exploit chain with another vulnerability in the hosts Broadcom WiFi driver rather than immediately exploiting a DMA attack across PCIe. This suggests that if PCIe is used to interface to peripherals in the phone that an IOMMU be used, for “defense in depth”.
Cybersecurity is a young field. There are lots of useful things that outsider non-techies can teach us. Lessons from military history would be well-received.
But that’s not this story. Instead, this story is by an outsider telling us we don’t know what we are doing, that they do, and then proceeds to prove they don’t know what they are doing. Their argument is based on a moral suasion and bullying us with what appears on the surface to be intellectual rigor, but which is in fact devoid of anything smart.
My fear, here, is that I’m going to be in a meeting where somebody has read this pretentious garbage, explaining to me why “defense in depth” is wrong and how we need to OODA faster. I’d rather nip this in the bud, pointing out if you found anything interesting from that article, you are wrong.
Elections serve two purposes. The first, and obvious, purpose is to accurately choose the winner. But the second is equally important: to convince the loser. To the extent that an election system is not transparently and auditably accurate, it fails in that second purpose. Our election systems are failing, and we need to fix them.
Today, we conduct our elections on computers. Our registration lists are in computer databases. We vote on computerized voting machines. And our tabulation and reporting is done on computers. We do this for a lot of good reasons, but a side effect is that elections now have all the insecurities inherent in computers. The only way to reliably protect elections from both malice and accident is to use something that is not hackable or unreliable at scale; the best way to do that is to back up as much of the system as possible with paper.
Recently, there have been two graphic demonstrations of how bad our computerized voting system is. In 2007, the states of California and Ohio conducted audits of their electronic voting machines. Expert review teams found exploitable vulnerabilities in almost every component they examined. The researchers were able to undetectably alter vote tallies, erase audit logs, and load malware on to the systems. Some of their attacks could be implemented by a single individual with no greater access than a normal poll worker; others could be done remotely.
Last year, the Defcon hackers’ conference sponsored a Voting Village. Organizers collected 25 pieces of voting equipment, including voting machines and electronic poll books. By the end of the weekend, conference attendees had found ways to compromise every piece of test equipment: to load malicious software, compromise vote tallies and audit logs, or cause equipment to fail.
It’s important to understand that these were not well-funded nation-state attackers. These were not even academics who had been studying the problem for weeks. These were bored hackers, with no experience with voting machines, playing around between parties one weekend.
It shouldn’t be any surprise that voting equipment, including voting machines, voter registration databases, and vote tabulation systems, are that hackable. They’re computers — often ancient computers running operating systems no longer supported by the manufacturers — and they don’t have any magical security technology that the rest of the industry isn’t privy to. If anything, they’re less secure than the computers we generally use, because their manufacturers hide any flaws behind the proprietary nature of their equipment.
We’re not just worried about altering the vote. Sometimes causing widespread failures, or even just sowing mistrust in the system, is enough. And an election whose results are not trusted or believed is a failed election.
Voting systems have another requirement that makes security even harder to achieve: the requirement for a secret ballot. Because we have to securely separate the election-roll system that determines who can vote from the system that collects and tabulates the votes, we can’t use the security systems available to banking and other high-value applications.
We can securely bank online, but can’t securely vote online. If we could do away with anonymity — if everyone could check that their vote was counted correctly — then it would be easy to secure the vote. But that would lead to other problems. Before the US had the secret ballot, voter coercion and vote-buying were widespread.
We can’t, so we need to accept that our voting systems are insecure. We need an election system that is resilient to the threats. And for many parts of the system, that means paper.
Let’s start with the voter rolls. We know they’ve already been targeted. In 2016, someone changed the party affiliation of hundreds of voters before the Republican primary. That’s just one possibility. A well-executed attack that deletes, for example, one in five voters at random — or changes their addresses — would cause chaos on election day.
Yes, we need to shore up the security of these systems. We need better computer, network, and database security for the various state voter organizations. We also need to better secure the voterregistration websites, with better design and better internet security. We need better security for the companies that build and sell all this equipment.
Multiple, unchangeable backups are essential. A record of every addition, deletion, and change needs to be stored on a separate system, on write-only media like a DVD. Copies of that DVD, or — even better — a paper printout of the voter rolls, should be available at every polling place on election day. We need to be ready for anything.
Next, the voting machines themselves. Security researchers agree that the gold standard is a voter-verified paper ballot. The easiest (and cheapest) way to achieve this is through optical-scan voting. Voters mark paper ballots by hand; they are fed into a machine and counted automatically. That paper ballot is saved, and serves as a final true record in a recount in case of problems. Touch-screen machines that print a paper ballot to drop in a ballot box can also work for voters with disabilities, as long as the ballot can be easily read and verified by the voter.
Finally, the tabulation and reporting systems. Here again we need more security in the process, but we must always use those paper ballots as checks on the computers. A manual, post-election, risk-limiting audit varies the number of ballots examined according to the margin of victory. Conducting this audit after every election, before the results are certified, gives us confidence that the election outcome is correct, even if the voting machines and tabulation computers have been tampered with. Additionally, we need better coordination and communications when incidents occur.
It’s vital to agree on these procedures and policies before an election. Before the fact, when anyone can win and no one knows whose votes might be changed, it’s easy to agree on strong security. But after the vote, someone is the presumptive winner — and then everything changes. Half of the country wants the result to stand, and half wants it reversed. At that point, it’s too late to agree on anything.
The politicians running in the election shouldn’t have to argue their challenges in court. Getting elections right is in the interest of all citizens. Many countries have independent election commissions that are charged with conducting elections and ensuring their security. We don’t do that in the US.
Instead, we have representatives from each of our two parties in the room, keeping an eye on each other. That provided acceptable security against 20th-century threats, but is totally inadequate to secure our elections in the 21st century. And the belief that the diversity of voting systems in the US provides a measure of security is a dangerous myth, because few districts can be decisive and there are so few voting-machine vendors.
We candobetter. In 2017, the Department of Homeland Security declared elections to be critical infrastructure, allowing the department to focus on securing them. On 23 March, Congress allocated $380m to states to upgrade election security.
These are good starts, but don’t go nearly far enough. The constitution delegates elections to the states but allows Congress to “make or alter such Regulations”. In 1845, Congress set a nationwide election day. Today, we need it to set uniform and strict election standards.
Do you have a clear understanding of the hybrid cloud? If you don’t, it’s not surprising.
Hybrid cloud has been applied to a greater and more varied number of IT solutions than almost any other recent data management term. About the only thing that’s clear about the hybrid cloud is that the term hybrid cloud wasn’t invented by customers, but by vendors who wanted to hawk whatever solution du jour they happened to be pushing.
Let’s be honest. We’re in an industry that loves hype. We can’t resist grafting hyper, multi, ultra, and super and other prefixes onto the beginnings of words to entice customers with something new and shiny. The alphabet soup of cloud-related terms can include various options for where the cloud is located (on-premises, off-premises), whether the resources are private or shared in some degree (private, community, public), what type of services are offered (storage, computing), and what type of orchestrating software is used to manage the workflow and the resources. With so many moving parts, it’s no wonder potential users are confused.
Let’s take a step back, try to clear up the misconceptions, and come up with a basic understanding of what the hybrid cloud is. To be clear, this is our viewpoint. Others are free to do what they like, so bear that in mind.
So, What is the Hybrid Cloud?
The hybrid cloud refers to a cloud environment made up of a mixture of on-premises private cloud resources combined with third-party public cloud resources that use some kind of orchestration between them.
To get beyond the hype, let’s start with Forrester Research‘s idea of the hybrid cloud: “One or more public clouds connected to something in my data center. That thing could be a private cloud; that thing could just be traditional data center infrastructure.”
To put it simply, a hybrid cloud is a mash-up of on-premises and off-premises IT resources.
To expand on that a bit, we can say that the hybrid cloud refers to a cloud environment made up of a mixture of on-premises private cloud resources combined with third-party public cloud resources that use some kind of orchestration between them. The advantage of the hybrid cloud model is that it allows workloads and data to move between private and public clouds in a flexible way as demands, needs, and costs change, giving businesses greater flexibility and more options for data deployment and use.
In other words, if you have some IT resources in-house that you are replicating or augmenting with an external vendor, congrats, you have a hybrid cloud!
Private Cloud vs. Public Cloud
The cloud is really just a collection of purpose built servers. In a private cloud, the servers are dedicated to a single tenant or a group of related tenants. In a public cloud, the servers are shared between multiple unrelated tenants (customers). A public cloud is off-site, while a private cloud can be on-site or off-site — or on-prem or off-prem.
As an example, let’s look at a hybrid cloud meant for data storage, a hybrid data cloud. A company might set up a rule that says all accounting files that have not been touched in the last year are automatically moved off-prem to cloud storage to save cost and reduce the amount of storage needed on-site. The files are still available; they are just no longer stored on your local systems. The rules can be defined to fit an organization’s workflow and data retention policies.
The hybrid cloud concept also contains cloud computing. For example, at the end of the quarter, order processing application instances can be spun up off-premises in a hybrid computing cloud as needed to add to on-premises capacity.
Hybrid Cloud Benefits
If we accept that the hybrid cloud combines the best elements of private and public clouds, then the benefits of hybrid cloud solutions are clear, and we can identify the primary two benefits that result from the blending of private and public clouds.
Benefit 1: Flexibility and Scalability
Undoubtedly, the primary advantage of the hybrid cloud is its flexibility. It takes time and money to manage in-house IT infrastructure and adding capacity requires advance planning.
The cloud is ready and able to provide IT resources whenever needed on short notice. The term cloud bursting refers to the on-demand and temporary use of the public cloud when demand exceeds resources available in the private cloud. For example, some businesses experience seasonal spikes that can put an extra burden on private clouds. These spikes can be taken up by a public cloud. Demand also can vary with geographic location, events, or other variables. The public cloud provides the elasticity to deal with these and other anticipated and unanticipated IT loads. The alternative would be fixed cost investments in on-premises IT resources that might not be efficiently utilized.
For a data storage user, the on-premises private cloud storage provides, among other benefits, the highest speed access. For data that is not frequently accessed, or needed with the absolute lowest levels of latency, it makes sense for the organization to move it to a location that is secure, but less expensive. The data is still readily available, and the public cloud provides a better platform for sharing the data with specific clients, users, or with the general public.
Benefit 2: Cost Savings
The public cloud component of the hybrid cloud provides cost-effective IT resources without incurring capital expenses and labor costs. IT professionals can determine the best configuration, service provider, and location for each service, thereby cutting costs by matching the resource with the task best suited to it. Services can be easily scaled, redeployed, or reduced when necessary, saving costs through increased efficiency and avoiding unnecessary expenses.
Comparing Private vs Hybrid Cloud Storage Costs
To get an idea of the difference in storage costs between a purely on-premises solutions and one that uses a hybrid of private and public storage, we’ll present two scenarios. For each scenario we’ll use data storage amounts of 100 terabytes, 1 petabyte, and 2 petabytes. Each table is the same format, all we’ve done is change how the data is distributed: private (on-premises) cloud or public (off-premises) cloud. We are using the costs for our own B2 Cloud Storage in this example. The math can be adapted for any set of numbers you wish to use.
Scenario 1100% of data on-premises storage
Data stored On-Premises: 100%
On-premises cost range
Low — $12/TB/Month
High — $20/TB/Month
Scenario 220% of data on-premises with 80% public cloud storage (B2)
Data stored On-Premises: 20%
Data stored in Cloud: 80%
On-premises cost range
Low — $12/TB/Month
High — $20/TB/Month
Public cloud cost range
Low — $5/TB/Month (B2)
High — $20/TB/Month
On-premises + public cloud cost range
As can be seen in the numbers above, using a hybrid cloud solution and storing 80% of the data in the cloud with a provider such as Backblaze B2 can result in significant savings over storing only on-premises. For other cost scenarios, see the B2 Cost Calculator.
When Hybrid Might Not Always Be the Right Fit
There are circumstances where the hybrid cloud might not be the best solution. Smaller organizations operating on a tight IT budget might best be served by a purely public cloud solution. The cost of setting up and running private servers is substantial.
An application that requires the highest possible speed might not be suitable for hybrid, depending on the specific cloud implementation. While latency does play a factor in data storage for some users, it is less of a factor for uploading and downloading data than it is for organizations using the hybrid cloud for computing. Because Backblaze recognized the importance of speed and low-latency for customers wishing to use computing on data stored in B2, we directly connected our data centers with those of our computing partners, ensuring that latency would not be an issue even for a hybrid cloud computing solution.
It is essential to have a good understanding of workloads and their essential characteristics in order to make the hybrid cloud work well for you. Each application needs to be examined for the right mix of private cloud, public cloud, and traditional IT resources that fit the particular workload in order to benefit most from a hybrid cloud architecture.
The Hybrid Cloud Can Be a Win-Win Solution
From the high altitude perspective, any solution that enables an organization to respond in a flexible manner to IT demands is a win. Avoiding big upfront capital expenses for in-house IT infrastructure will appeal to the CFO. Being able to quickly spin up IT resources as they’re needed will appeal to the CTO and VP of Operations.
Should You Go Hybrid?
We’ve arrived at the bottom line and the question is, should you or your organization embrace hybrid cloud infrastructures?
According to 451 Research, by 2019, 69% of companies will operate in hybrid cloud environments, and 60% of workloads will be running in some form of hosted cloud service (up from 45% in 2017). That indicates that the benefits of the hybrid cloud appeal to a broad range of companies.
Clearly, depending on an organization’s needs, there are advantages to a hybrid solution. While it might have been possible to dismiss the hybrid cloud in the early days of the cloud as nothing more than a buzzword, that’s no longer true. The hybrid cloud has evolved beyond the marketing hype to offer real solutions for an increasingly complex and challenging IT environment.
If an organization approaches the hybrid cloud with sufficient planning and a structured approach, a hybrid cloud can deliver on-demand flexibility, empower legacy systems and applications with new capabilities, and become a catalyst for digital transformation. The result can be an elastic and responsive infrastructure that has the ability to quickly respond to changing demands of the business.
As data management professionals increasingly recognize the advantages of the hybrid cloud, we can expect more and more of them to embrace it as an essential part of their IT strategy.
Tell Us What You’re Doing with the Hybrid Cloud
Are you currently embracing the hybrid cloud, or are you still uncertain or hanging back because you’re satisfied with how things are currently? Maybe you’ve gone totally hybrid. We’d love to hear your comments below on how you’re dealing with the hybrid cloud.
 Private cloud can be on-premises or a dedicated off-premises facility.
 Hybrid cloud orchestration solutions are often proprietary, vertical, and task dependent.
Good article about how difficult it is to insure an organization against Internet attacks, and how expensive the insurance is.
Companies like retailers, banks, and healthcare providers began seeking out cyberinsurance in the early 2000s, when states first passed data breach notification laws. But even with 20 years’ worth of experience and claims data in cyberinsurance, underwriters still struggle with how to model and quantify a unique type of risk.
“Typically in insurance we use the past as prediction for the future, and in cyber that’s very difficult to do because no two incidents are alike,” said Lori Bailey, global head of cyberrisk for the Zurich Insurance Group. Twenty years ago, policies dealt primarily with data breaches and third-party liability coverage, like the costs associated with breach class-action lawsuits or settlements. But more recent policies tend to accommodate first-party liability coverage, including costs like online extortion payments, renting temporary facilities during an attack, and lost business due to systems failures, cloud or web hosting provider outages, or even IT configuration errors.
There are challenges to creating these new insurance products. There are two basic models for insurance. There’s the fire model, where individual houses catch on fire at a fairly steady rate, and the insurance industry can calculate premiums based on that rate. And there’s the flood model, where an infrequent large-scale event affects large numbers of people — but again at a fairly steady rate. Internet+ insurance is complicated because it follows neither of those models but instead has aspects of both: individuals are hacked at a steady (albeit increasing) rate, while class breaks and massive data breaches affect lots of people at once. Also, the constantly changing technology landscape makes it difficult to gather and analyze the historical data necessary to calculate premiums.
Amazon Simple Notification Service (SNS) now supports VPC Endpoints (VPCE) via AWS PrivateLink. You can use VPC Endpoints to privately publish messages to SNS topics, from an Amazon Virtual Private Cloud (VPC), without traversing the public internet. When you use AWS PrivateLink, you don’t need to set up an Internet Gateway (IGW), Network Address Translation (NAT) device, or Virtual Private Network (VPN) connection. You don’t need to use public IP addresses, either.
Here’s how VPC Endpoints for SNS works. The following example is based on a banking system that processes mortgage applications. This banking system, which has been deployed to a VPC, publishes each mortgage application to an SNS topic. The SNS topic then fans out the mortgage application message to two subscribing AWS Lambda functions:
Save-Mortgage-Application stores the application in an Amazon DynamoDB table. As the mortgage application contains personally identifiable information (PII), the message must not traverse the public internet.
Save-Credit-Report checks the applicant’s credit history against an external Credit Reporting Agency (CRA), then stores the final credit report in an Amazon S3 bucket.
The following diagram depicts the underlying architecture for this banking system:
To protect applicants’ data, the financial institution responsible for developing this banking system needed a mechanism to prevent PII data from traversing the internet when publishing mortgage applications from their VPC to the SNS topic. Therefore, they created a VPC endpoint to enable their publisher Amazon EC2 instance to privately connect to the SNS API. As shown in the diagram, when the VPC endpoint is created, an Elastic Network Interface (ENI) is automatically placed in the same VPC subnet as the publisher EC2 instance. This ENI exposes a private IP address that is used as the entry point for traffic destined to SNS. This ensures that traffic between the VPC and SNS doesn’t leave the Amazon network.
Set up VPC Endpoints for SNS
The process for creating a VPC endpoint to privately connect to SNS doesn’t require code changes: access the VPC Management Console, navigate to the Endpoints section, and create a new Endpoint. Three attributes are required:
The Security Group (SG) to be associated with the endpoint network interface. The Security Group controls the traffic to the endpoint network interface from resources in your VPC. If you don’t specify a Security Group, the default Security Group for your VPC will be associated.
The SNS API is served through HTTP Secure (HTTPS), and encrypts all messages in transit with Transport Layer Security (TLS) certificates issued by Amazon Trust Services (ATS). The certificates verify the identity of the SNS API server when encrypted connections are established. The certificates help establish proof that your SNS API client (SDK, CLI) is communicating securely with the SNS API server. A Certificate Authority (CA) issues the certificate to a specific domain. Hence, when a domain presents a certificate that’s issued by a trusted CA, the SNS API client knows it’s safe to make the connection.
VPC Endpoints can increase the security of your pub/sub messaging use cases by allowing you to publish messages to SNS topics, from instances in your VPC, without traversing the internet. Setting up VPC Endpoints for SNS doesn’t require any code changes because the SNS API address remains the same.
Regular visitors to this blog may have noticed that its name has changed from the Amazon SES Blog to the AWS Messaging and Targeting Blog. The Amazon SES team has been working closely with the Amazon Pinpoint team in recent months, so we decided to create a single source of information for both products.
If you’re a dedicated Amazon SES user, don’t worry—Amazon SES isn’t going anywhere. However, as the goals of our two teams started to overlap more and more, we realized that we had lots of ideas for blog posts that would be relevant to users of both products.
If you’re not familiar with Amazon Pinpoint yet, allow us to make a brief introduction. Amazon Pinpoint was originally created to help mobile app developers analyze the ways that their customers used their apps, and to send mobile push messages to those users. Over time, the capabilities of Amazon Pinpoint grew to include the ability to send transactional messages (such as order confirmations, welcome messages, and one-time passwords), segment your audience, schedule campaign execution, and send messages using other channels (including SMS and email). In short, Amazon Pinpoint helps you deliver the right message to the right customers at the right time using the right channel.
In the past, this blog focused mainly on providing information about new features and common issues. Our new blog will include that same information, as well as practical tips, industry best practices, and the exciting things our customers have done using Amazon SES and Amazon Pinpoint. We hope you enjoy it!
If you have any questions, or if there’s anything you’d like to see us cover in the blog, please let us know in the comments section.
American Public Television was like many organizations that have been around for a while. They were entrenched using an older technology — in their case, tape storage and distribution — that once met their needs but was limiting their productivity and preventing them from effectively collaborating with their many media partners. APT’s VP of Technology knew that he needed to move into the future and embrace cloud storage to keep APT ahead of the game.
Since 1961, American Public Television (APT) has been a leading distributor of groundbreaking, high-quality, top-rated programming to the nation’s public television stations. Gerry Field is the Vice President of Technology at APT and is responsible for delivering their extensive program catalog to 350+ public television stations nationwide.
In the time since Gerry joined APT in 2007, the industry has been in digital overdrive. During that time APT has continued to acquire and distribute the best in public television programming to their technically diverse subscribers.
This created two challenges for Gerry. First, new technology and format proliferation were driving dramatic increases in digital storage. Second, many of APT’s subscribers struggled to keep up with the rapidly changing industry. While some subscribers had state-of-the-art satellite systems to receive programming, others had to wait for the post office to drop off programs recorded on tape weeks earlier. With no slowdown on the horizon of innovation in the industry, Gerry knew that his storage and distribution systems would reach a crossroads in no time at all.
Living the tape paradigm
The digital media industry is only a few years removed from its film, and later videotape, roots. Tape was the input and the output of the industry for many years. As a consequence, the tools and workflows used by the industry were built and designed to work with tape. Over time, the “file” slowly replaced the tape as the object to be captured, edited, stored and distributed. Trouble was, many of the systems and more importantly workflows were based on processing tape, and these have proven to be hard to change.
At APT, Gerry realized the limits of the tape paradigm and began looking for technologies and solutions that enabled workflows based on file and object based storage and distribution.
Thinking file based storage and distribution
For data (digital media) storage, APT, like everyone else, started by installing onsite storage servers. As the amount of digital data grew, more storage was added. In addition, APT was expanding its distribution footprint by creating or partnering with distribution channels such as CreateTV and APT Worldwide. This dramatically increased the number of programming formats and the amount of data that had to be stored. As a consequence, updating, maintaining, and managing the APT storage systems was becoming a major challenge and a major resource hog.
Knowing that his in-house storage system was only going to cost more time and money, Gerry decided it was time to look at cloud storage. But that wasn’t the only reason he looked at the cloud. While most people consider cloud storage as just a place to back up and archive files, Gerry was envisioning how the ubiquity of the cloud could help solve his distribution challenges. The trouble was the price of cloud storage from vendors like Amazon S3 and Microsoft Azure was a non-starter, especially for a non-profit. Then Gerry came across Backblaze. B2 Cloud Storage service met all of his performance requirements, and at $0.005/GB/month for storage and $0.01/GB for downloads it was nearly 75% less than S3 or Azure.
Gerry did the math and found that he could economically incorporate B2 Cloud Storage into his IT portfolio, using it for both program submission and for active storage and archiving of the APT programs. In addition, B2 now gives him the foundation necessary to receive and distribute programming content over the Internet. This is especially useful for organizations that can’t conveniently access satellite distribution systems. Not to mention downloading from the cloud is much faster than sending a tape through the mail.
Adding B2 Cloud Storage to their infrastructure has helped American Public Television address two key challenges. First, they now have “unlimited” storage in the cloud without having to add any hardware. In addition, with B2, they only pay for the storage they use. That means they don’t have to buy storage upfront trying to match the maximum amount of storage they’ll ever need. Second, by using B2 as a distribution source for their programming APT subscribers, especially the smaller and remote ones, can get content faster and more reliably without having to perform costly upgrades to their infrastructure.
The road ahead
As APT gets used to their file based infrastructure and workflow, there are a number of cost saving and income generating ideas they are pondering which are now worth considering. Here are a few:
Program Submissions — New content can be uploaded from anywhere using a web browser, an Internet connection, and a login. For example, a producer in Cambodia can upload their film to B2. From there the film is downloaded to an in-house system where it is processed and transcoded using compute. The finished film is added to the APT catalog and added to B2. Once there, the program is instantly available for subscribers to order and download.
“The affordability and performance of Backblaze B2 is what allowed us to make the B2 cloud part of the APT data storage and distribution strategy into the future.” — Gerry Field
Easier Previews — At any time, work in process or finished programs can be made available for download from the B2 cloud. One place this could be useful is where a subscriber needs to review a program to comply with local policies and practices before airing. In the old system, each “one-off” was a time consuming manual process.
Instant Subscriptions — There are many organizations such as schools and businesses that want to use just one episode of a desired show. With an e-commerce based website, current or even archived programming kept in B2 could be available to download or stream for a minimal charge.
At APT there were multiple technologies needed to make their file-based infrastructure work, but as Gerry notes, having an affordable, trustworthy, cloud storage service like B2 is one of the critical building blocks needed to make everything work together.
At AWS re:Invent 2017 we launched Amazon Transcribe in private preview. Today we’re excited to make Amazon Transcribe generally available for all developers. Amazon Transcribe is an automatic speech recognition service (ASR) that makes it easy for developers to add speech to text capabilities to their applications. We’ve iterated on customer feedback in the preview to make a number of enhancements to Amazon Transcribe.
New Amazon Transcribe Features in GA
To start off we’ve made the SampleRate parameter optional which means you only need to know the file type of your media and the input language. We’ve added two new features – the ability to differentiate multiple speakers in the audio to provide more intelligible transcripts (“who spoke when”), and a custom vocabulary to improve the accuracy of speech recognition for product names, industry-specific terminology, or names of individuals. To refresh our memories on how Amazon Transcribe works lets look at a quick example. I’ll convert this audio in my S3 bucket.
Now if I needed to have a more complex technical discussion with a colleague I could create a custom vocabulary. A custom vocabulary is specified as an array of strings passed to the CreateVocabulary API and you can include your custom vocabulary in a transcription job by passing in the name as part of the Settings in a StartTranscriptionJob API call. An individual vocabulary can be as large as 50KB and each phrase must be less than 256 characters. If I wanted to transcribe the recordings of my highschool AP Biology class I could create a custom vocabulary in Python like this:
I can refer to this vocabulary later on by the name APBiology and update it programatically based on any errors I may find in the transcriptions.
Amazon Transcribe is available now in US East (N. Virginia), US West (Oregon), US East (Ohio) and EU (Ireland). Transcribe’s free tier gives you 60 minutes of transcription for free per month for the first 12 months with a pay-as-you-go model of $0.0004 per second of transcribed audio after that, with a minimum charge of 15 seconds.
When combined with other tools and services I think transcribe opens up a entirely new opportunities for application development. I’m excited to see what technologies developers build with this new service.
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