Core Messaging Flashcards
Why does AWS continue to be the leader in cloud? (Also: why do customers choose AWS over other providers?)
- General
- Services
- Innovation
- APN
- Experience
- Culture
Customers are choosing AWS over other providers because it has a lot more functionality, the largest and most vibrant community of customers and partners, the most proven operational and security expertise, and the business is innovating at a faster clip – especially in new areas such as Machine Learning and Artificial Intelligence, Internet of Things, and Serverless Computing.
1/AWS has more services, and more features within those services, than any other cloud provider–by a large amount. AWS has been continually expanding its services to support virtually any cloud workload, and it now has more than 175 fully featured services, including compute, storage, databases, networking, analytics, robotics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, application development, deployment, and management. In addition to having the greatest breadth of services, AWS also has the deepest functionality within those services. These differences matter, for example:
Look at compute. Many cloud providers offer compute services and can say “we have compute,” but it’s not just about checking that box. AWS has meaningfully more compute instances than anyone else. We have the most powerful GPU instances for machine learning (ML) training and graphics workloads with our EC2 P3dn instances. And for running machine learning at scale and in production, our new Inf1 instances for EC2, which are powered by AWS Inferentia chips, have the lowest cost-per-inference instances in the cloud. We also have the fastest processors in the cloud with the EC2 z1d instances (sustained turbo clock speed of 4.0 GHz), and the largest in-memory instances for SAP workloads with the EC2 High Memory instances (24 TB of memory). And only AWS has 100 Gbps network connectivity for standard instances, we’re the only one with Arm-based instances, and the only to offer a choice of Intel, AMD, and Arm processors.
You also see this depth difference with containers. AWS has a lot more capabilities for containers than any other cloud provider. And 81% of all containers in the cloud are running on AWS. We offer a managed Kubernetes service, Amazon EKS, as well as our own container service with Amazon ECS. This gives our customers a choice. If they want a container service that’s the most deeply integrated with the rest of the AWS platform capabilities, they can use Amazon ECS, which we fully control, so we can make sure every feature we launch works really closely with ECS—right from the get go. If customers prefer having a managed Kubernetes service, then they can use Amazon EKS–and 84% of Kubernetes workloads than run in the cloud today run on AWS. Customers that prefer not to have to worry about servers or clusters, and want to manage containers at the task level, can use our serverless-container offering, AWS Fargate. Fargate is the only way to run serverless containers securely and at scale. It’s much easier to run containers this way, which is why about 40% of customers adopting an AWS container service choose Fargate. And now, with AWS Fargate for Amazon EKS, which we launched at re:Invent 2019, we also offer customers the only way to run severless Kubernetes containers securely, reliably, and at scale.
With serverless, AWS Lambda integrates with 47 AWS services so you have a lot of flexibility in what you can build. Nobody else integrates with more than 20.
With AWS storage, customers have significantly more ways to get data into Amazon S3 than what you’ll find elsewhere.
You can also look at databases, where we have 15 database options. AWS has the most complete family of purpose-built databases to give customers the right tool for the right job so they can spend less money, be more productive, and change the customer experience.
2/AWS is also innovating faster than anyone else, and that gap in capability continues to expand. In 2011, we released over 80 significant services and features; in 2012, nearly 160; in 2013, 280; in 2014, 516; in 2015, 722; in 2016, 1,017; and 1,430 in 2017. In 2018 we launched 1,957 new features and services [as of March 1, 2019].
*NOTE: 2019 LAUNCH NUMBERS HAVE NOT YET BEEN APPROVED FOR USE.
3/With millions of active customers and tens of thousands of partners globally, AWS has the largest and most dynamic community. There is a real network effect when you use AWS. Customers across virtually every industry and of every size, including start-ups, enterprises, and public sector organizations, are running every imaginable use case on AWS. And, every AWS customer has the opportunity to benefit from all of the collaboration and feedback AWS gets from customers. And, when you look at AWS’s partner network, it is not just the thousands of systems integrators who built practices around AWS, but most ISVs and SaaS providers will adapt their technology to work on one technology infrastructure platform. Some will do two, very few will have the time to do three. And they all start with AWS because of our significant leadership position. It’s why you see a much more vibrant collection of ISVs and SaaS providers on AWS as you’re moving to the cloud and want those capabilities. You see it when Salesforce runs the vast majority of what they do on top of AWS, as does Workday and Splunk, Informatica and Infor and Acquia and Datadog and Databricks. Just a much broader collection of software that, when you’re moving to the cloud, you can use really easily on top of our platform.
4/AWS has unmatched experience, maturity, reliability, security, and performance. Internally, we say that there’s no compression algorithm for experience, and that’s because you can’t learn certain lessons until you get to different milestones in scale.
5/Finally, AWS customers have come to appreciate that our culture is really different. We are unusually customer focused (vs. competitor focused). 90% of what we build is driven by what customers tell us matters. We are also pioneers, and we hire builders who are always looking at how they can reinvent customer experiences that are flawed. Finally, we are unusually long-term oriented—we’re trying to build relationships and a business that outlasts all of us.
How is AWS different from other technology providers? (expands on point four in question
Customers have come to really appreciate that the AWS culture is really different. If a startup, enterprise, or government agency is going to partner with an infrastructure provider, it’s typically a long-term decision they’re making, and they really want to understand what’s unique about the culture, or the partner that they are choosing. There are three things that are different about AWS:
1: We’re unusually customer-focused. And a lot of companies say this. Very few walk that walk. Most of the big technology companies are competitor-focused. They look at what the competitors are doing, and they try to one-up the competitors. That can be a very successful strategy, it’s just not ours. 90% of what we build is driven by what customers tell us matters, and the other 10% are things we hear from customers where they may not articulate exactly what they want, but we try to read between the lines and invent on their behalf.
2: We’re pioneers. Most large technology companies have lost their will and DNA to invent. They acquire most of their innovation. And again, it’s a strategy that can work, it’s just not ours. We like to hire builders who look at customer experiences that are flawed, then figure out how to reinvent those. In a space that’s moving as fast as the cloud is, to be partnered with the company that has the most functionality, that’s iterating the quickest, has the largest community, had the vision for cloud from the start without having to patch together acquisitions, that’s very attractive.
3: We’re unusually long-term oriented. You won’t see our folks show up at customers’ doors a day before the end of the quarter or the day before the end of the year and try to harass them into a sale, not to be seen again for a year. We’re trying to build relationships and a business that lasts longer than all of us in this room. And you do that by doing right by customers over a long period of time.
This is why, in part, Splunk named us their alliance partner of the year in both 2016 and 2018. They’ll tell you we partner in a different way.
Why is AWS growing so fast?
We’re in the midst of a titanic shift to the cloud, and there are a couple reasons why people are moving so quickly.
The first is cost. With the cloud, you don’t have to lay out the capital up front for the servers and the data centers, and you instead get to pay for it as you consume it as a variable expense. And if you’ve ever had to provision infrastructure, you know you either have to decide to provision on the low side, and then if it turns out you don’t have enough, you create a customer outage, which most people don’t choose. So instead, you provision for the peak (and you rarely sit at the peak for long). In the cloud you just provision what you need–if it turns out you need less, you give it back to us and stop paying for it. That variable expense is lower than what virtually every company can do on its own because AWS has such large scale that we pass on to customers in the form of lower prices. In fact, we’ve lowered our prices 80 times since AWS launched in 2006 (as of Feb 6, 2020).
So cost is very compelling and almost always the conversation starter, but the number one reason that enterprises and governments are moving to the cloud is the agility and speed with which they can change their customer experience. If you look at most companies’ on-premises infrastructure, to get a server typically takes 10 to 12 weeks (sometimes longer) and then you have to build all this surrounding infrastructure software, like compute and storage and database and analytics and machine learning. In the cloud, you can provision thousands of servers in minutes and access over 175 services that you can put together and use however you want. So you get from an idea to implementation several orders of magnitude faster. That’s really attractive.
What are some of the emerging technologies AWS is focused on (or strategic technologies, or technologies that customers are adopting?
A. Machine Learning (ML)/Artificial Intelligence (AI):
In the fullness of time, virtually every application will be infused with ML and AI. Most customers we work with are very interested in ML. Tens of thousands of customers are running machine learning on AWS, spurred by the broad adoption of Amazon SageMaker. It’s a pretty broad set of customers, including companies like Liberty Mutual Insurance, Slack, C-Span, Intuit, Pinterest, Capital One, the American Heart Association, Yelp, FINRA, and NBC. While an incredible amount of progress being made in organizations using ML and AI, we’re still at the relative beginning. We have about twice as much ML being run on AWS than you’ll find anywhere else, and yet it’s really early for most organizations. We think about machine learning in three layers of the stack, and we believe in the fullness of time, most organizations that have significant technology capability will use all three of these.
i. The bottom layer is for expert machine learning practitioners–including advanced developers and data scientists–who are comfortable building, tuning, training, deploying, and managing models themselves, and working at the framework level. For fast, cost-effective model training customers have P2 and P3 instances, and our P3dn instances are the most powerful GPU instances available in the cloud today. And, unlike other providers who try to funnel everybody into using only one framework, AWS supports all the major frameworks (TensorFlow, MXNet, PyTorch 2, Caffe 2, etc.) And 90 percent of data scientists use multiple frameworks because different frameworks are better for different types of workloads. Today, TensorFlow has the most resonance. About 85% of the TensorFlow workloads running in the cloud today run on AWS.
And, for inference, which actually makes up about 90% of ML costs, customers can use Elastic Inference to save up to 75% on inference costs by adding a little GPU acceleration to any Amazon EC2 instance so they don’t have to pay for an entire GPU instance if they don’t need a full instance for inference. To further reduce the costs of inference, we recently announced our Inf1 instances for EC2 (at re:Invent 2019), which are powered by AWS Inferentia chips, and have the lowest cost-per-inference instances in the cloud.
ii. If we want machine learning to be as expansive as we really want it to be, we need to make it much more accessible to people who aren’t machine learning practitioners. Today, there are very few of these experts out there. So, at the middle layer of the stack, we built Amazon SageMaker, a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the machine learning process, empowering everyday developers and scientists to successfully use machine learning. SageMaker is a sea-level change for everyday developers and data scientists being able to access and build machine learning models. Tens of thousands of customers are now standardizing on top of SageMaker. We continue to add important capabilities to Amazon SageMaker, and announced over 50 capabilities in the last year, including Amazon SageMaker Ground Truth for building training data sets, which reduces labeling costs by 70%; the AWS Marketplace for ML, which gives you hundreds of algorithms from others that you can use in your machine learning; and a deep learning model compiler, Neo, that lets you train models once and run them anywhere with up to 2X improvement in performance. We were the first to build reinforcement learning into a service like SageMaker, which people have been using in conjunction with DeepRacer for a year. And at re:Invent 2019 in December, we introduced SageMakerStudio, which is the first fully integrated development environment (IDE) for machine learning. SageMaker Studio is a web-based IDE that allows you to store and collect all of the things that you need–whether it’s code, or notebooks, or data sets, or settings, or project folders–all in one place, one pane of glass. It makes it much easier to manage all those pieces in building a machine learning model. We also announced several significant capabilities that are both stand-alone APIs and integrated components of SageMaker Studio including:
• SageMaker Notebooks, which are one-click notebooks with elastic compute so you can spin up a notebook in seconds–without provisioning instances.
• SageMaker Experiments, which is a way to capture, organize, and search every step of building, training, and tuning your models automatically.
• SageMaker Debugger, which allows developers to debug and profile their model training to improve the accuracy of their machine learning models.
• SageMaker Model Monitor, which is a way to detect concept drift by monitoring models deployed to production automatically.
• SageMaker Autopilot, which is auto ML with full control and visibility.
iii. At the top layer of the stack, we have services that people call AI because they mimic human cognition. And, the services make it really easy to incorporate AI (e.g. turning text to speech, transcribing audio into text, or translate text into multiple languages) into applications without having to build and train algorithms. We have Amazon Rekognition for deep-learning based video and image analysis, Amazon Polly for translating text to speech, Amazon Lex for building conversations, Amazon Transcribe for converting speech to text, Amazon Translate for translating text between languages, Amazon Comprehend and Amazon Comprehend Medical natural language processing for understanding relationships and finding insights within text, Amazon Textract for extracting text and data from virtually any document, Amazon Personalize for real-time recommendations and personalizations, Amazon Forecast for accurate time series forecasting. And we announced four new AI services at re:Invent 2019 in December, including Amazon Fraud Detector, which is a machine learning service that does fraud management for you; Amazon CodeGuru, which is a new machine learning service to automate code reviews and to also identify your most expensive lines of code; Contact Lens for Amazon Connect, which is machine learning-powered contact center analytics for Amazon Connect; and Amazon Kendra, a new service that reinvents enterprise search with machine learning and natural language processing.
Along with this broad range of services and devices, customers are working alongside Amazon’s expert data scientists in the AWS Machine Learning Lab to implement real-life use cases. We have a pretty giant investment in all layers of the machine learning stack and we believe that most companies, over time, will use multiple layers of that stack and have applications that are infused with machine learning.
B. IoT/Edge:
Over the next 10 to 20 years, it is likely most companies on-premises footprint will not be servers—those will virtually all be in the cloud—their on-premises footprint will be connected devices. Billions of these connected devices will be in the home, in the office, in factories, on ships and planes, in cars, in oil fields, and in agricultural fields. These sensors already are everywhere and they are typically small, with a small amount of CPU and a small amount of disk. This is why the cloud is disproportionately important to supplement those devices. Most of the big IoT applications that have been built over the last few years have been built using AWS to supplement them. Enel, Illumina, iRobot, LG Electronics, Comcast, Pentair, Deutsche Bahn, NASA Jet Propulsion Laboratory, Nokia, Panasonic Avionics, and Siemens among others. Today, with the combination of AWS, AWS IoT, and AWS Greengrass, developers have a single pane of glass that lets them decide what processing and analytics they want to do in the cloud, and what they want to do on the device itself, eliminating the latency of a round trip to the cloud. And, they have the same programming interface, which makes it much easier to collect data from these devices on the edge, perform large-scale analytics, pick predictive ML algorithms they can run on the devices to do predictions at the edge. This totally changes what is possible.
C. Compute:
The basic unit of compute is becoming smaller, and compute is an area where we continue to invest. We look at compute in three areas: instances, containers, and serverless.
i. Companies will continue to use instances for many, many years. We have more instances in every imaginable shape and size than you’ll find elsewhere and we continue to add more. We have the most powerful GPU instances for machine learning (ML) training and graphics workloads with our EC2 P3dn instances. And for running machine learning at scale and in production, our new Inf1 instances for EC2, which are powered by AWS Inferentia chips, have the lowest cost-per-inference instances in the cloud. We also have the fastest processors in the cloud with the EC2 z1d instances (sustained turbo clock speed of 4.0 GHz), and the largest in-memory instances for SAP workloads with the EC2 High Memory instances (24 TB of memory). And only AWS has 100 Gbps network connectivity for standard instances, we’re the only one with Arm-based instances, and the only to offer a choice of Intel, AMD, and Arm processors. Moreover, our new set of Arm-based instances powered by AWS Graviton2 processors, the M6g, R6g and C6g for EC2, offer 40% better price/performance than current x86-based instances.
ii. More and more customers are running containers, in part because they can deploy workloads in smaller chunks. And, as people are building in a micro services architecture, they can encapsulate those micro services in containers, and they’re easier to move around. We have four container services. Amazon ECS, which we launched in 2014, is the most capable and cloud integrated container service out there. For customers who want a managed Kubernetes service—we have Amazon EKS, which is growing unbelievably fast since we launched it in June 2018. In fact, 84% of the Kubernetes workloads running in the cloud today run on AWS. And, for customers who want to manage containers without having to worry about servers or clusters at all, we have AWS Fargate, which lets customers manage containers at the task level. Fargate is a serverless-container offering. It’s the only offering like it anywhere out there. It’s much easier to run containers this way, which is why about 40% of customers adopting an AWS container service choose Fargate. And with our newest container service, AWS Fargate for Amazon EKS (announced at re:Invent 2019 in December), we also offer customers the only way to run severless Kubernetes containers securely, reliably, and at scale. Customers using one of our four container services to run containers on AWS include Capital One, Coursera, Palringo, Fox Digital Consumer Group, Travelex, WeWork, Verizon, McDonald’s, GoPro, and Mapbox, Snap Inc., FICO, GoDaddy, Honeywell, Intuit, Pearson, Skyscanner, Verizon, Zendesk, Accenture, Ancestry.com, FireEye, General Electric, and KPMG, among others.
iii. In 2014, AWS pioneered the event-driven serverless computing space with the launch of AWS Lambda. AWS Lambda lets developers run their code without provisioning or managing servers, and customers never have to worry about scaling, patching, or managing any servers. With serverless, a whole generation of developers is going to grow up not thinking about managing servers and clusters. When you think about how many customers are running AWS Lambda today, it’s kind of astonishing. It was just a few years ago that we launched the concept, and it was a brand new concept—today there are hundreds of thousands of customers running AWS Lambda, including companies like Autodesk, Coca-Cola, Expedia, Fannie Mae, FICO, FINRA, London Stock Exchange, T-Mobile, and Thomson Reuters, among others. And, AWS continues to invest in this area. It is one thing to have serverless compute capability, but developers would be pretty limited in what they can build with serverless compute if it doesn’t work with all of the other AWS services. Lambda integrates with 47 AWS services—nobody else integrates with more than 20.
D. Databases:
There are two trends we are seeing with databases. First, over the last few decades, companies have felt constrained by their commercial-grade database options and have been really unhappy with their old guard database providers—these offerings are expensive, proprietary, have high-lock-in, and punitive licensing terms. That’s why many customers have moved to the open database engines like Postgres, MySQL, and MariaDB. But, to get the same performance on these open engines that you get in commercial-grade old guard engines takes a lot of work. Customers want the performance of commercial-grade databases with the pricing and friendliness of open engines. That’s why AWS spent several years building our own database engine in Amazon Aurora, a fully managed MySQL and Postgres compatible service that has several-times-faster performance than the typical high-end implementations in those community editions. Moreover, it’s at least as durable, performant, and available as the commercial-grade databases, but at one tenth of the cost. Amazon Aurora continues to be the fastest growing service in the history of AWS, with tens of thousands of customers. Examples include Verizon, Expedia, CapitalOne, Astrazeneca, Dow Jones, Bristol-Myers Squibb, Samsung, and Ticketmaster. And, these are just some of the many customers that continue to migrate their databases to AWS. The number of databases migrated to AWS using the AWS Database Migration Service is more than 200,000 databases migrated since the introduction of the service.
The second interesting trend is that the days of using a relational database to solve all database requirements are over. Over the past 20-30 years, companies have run most of their workloads using relational databases. This made sense because those applications typically had gigabytes (or occasionally terabytes) of data and they needed complex joins and ad hoc queries. But a number of things have changed over the last few years. People have woken up to how useful data is at the same time the cost of compute and storage has come way down—in large part because of the cloud. This means that most applications today are storing terabytes and petabytes of data. And, the requirements for apps have changed. It’s not just about being able to handle more data, but there are new expectations around very low latency, the ability to process millions of requests per second, and handling millions of worldwide customers. Finally, more and more companies are beefing up their own tech capabilities to leverage the incredible pace of innovation. These really capable tech folks are moving towards microservices architectures with composable building blocks and purpose built tools. They don’t want to settle for a database that does a lot of things ok, and none really well. This all has led people away from using relational databases for every workload and instead using the database that is really great at what each app needs. AWS has 8 purpose built non-relational databases to give developers the right tool for the right app, including a reliable, scalable, and low-latency key value store with DynamoDB; an in-memory store (ElastiCache) that supports both Redis and Memcached; a fully managed graph database with Neptune; a fast, scalable fully managed time series database, TimeStream, for analyzing data that changes over time; a fully managed ledger database with a central trusted authority with QLDB; and DocumentDB, a fast, scalable document database that supports MongoDB. And if you want to manage large Cassandra clusters at scale, you can now use our Amazon Managed Apache Cassandra Service, which we announced at re:Invent 2019. Builders need the right tool for the right job when it comes to databases, and only AWS offers this.
E. Data Lakes and Analytics:
It’s amazing how much analytics are being done in the cloud today. It has never been easier to collect, store, analyze, and share data than it is today in the cloud. And that’s because it’s not only much more cost effective in the cloud, but also the analytics services available today change the possibilities.
The foundation of all of this is storage. Most customers in the cloud use Amazon S3 as their storage and data lake solution because it has the most functionality, and unmatched availability, durability, and scalability. It’s also the most secure data store, and the only one that allows you to block public access at the bucket and account level and get inventory reports on all your objects. And we recently announced IAM Access Analyzer, which allows you to analyze all the access permissions on all your objects and buckets; and Amazon S3 Access Points, which radically simplifies managing access permissions at scale for applications using shared sets on S3. Over the years, customers have accumulated so much data, and a lot of that data lives in different silos, which makes it really hard to do anything with their data, including analytics. So customers pull that data together in a data lake. The most popular choice for cloud data lakes is S3. AWS hosts tens of thousands of data lakes running on S3 today. And it’s the object store and data lake that gives you the most ways to get your data into it, including through the wire, through our Direct Connect, through our backbone, through streaming with Kinesis Firehouse, through IoT and a storage gateway, through SFTP, through Snowball appliances, and even through a 45-foot container with Snowmobile. Almost every imaginable way you can get your data from wherever it is into your data lake you can do in S3–significantly more ways than you’ll find elsewhere. And With AWS Lake Formation, customers can build a secure data lake in days instead of weeks or months.
AWS also offers the most analytics capabilities of anyone. Customers can process vast amounts of data with Amazon EMR, which supports 21 different open source processing projects (Hadoope, Spark, HBase, Presto, and more), and run analytics on real-time streaming data with Amazon Kinesis. Amazon Kinesis Video Streams makes it easy to securely stream video from millions of connected devices to AWS for analytics, machine learning (ML), and other processing. Customers are also actively using Amazon Elasticsearch Service for real-time operational dashboards, Amazon QuickSight for business intelligence with great visualizations and embedded machine learning, and Amazon Athena to instantly analyze data stored in S3 using standard SQL. And, with Amazon Redshift, our fast, scalable data warehouse service, customers can perform complex queries on massive collections of structured data (petabytes in a Redshift datawarehouse or exabytes in a data lake built on S3) with super-fast performance. Redshift delivers 10x faster performance than other data warehouse by using machine learning, massively parallel query execution, and columnar storage on high-performance disk. Redshift also includes Redshift Spectrum to allow customers to run queries directly on exabytes of unstructured data in Amazon S3 without having to load or transform any data. And now, with new Redshift Federated Query, you can query across Redshift, S3, and our relational database services, including Aurora Postgres. We also recently introduced a new Advanced Query Accelerator (AQUA) for Redshift (coming in 2020), which is an innovative way to do hardware-accelerated cache that lets you have 10x better query performance than any other cloud data warehouse solution. And with new Amazon Redshift RA3 Instances with Managed Storage, you can scale storage and compute separately and cost-effectively for diverse data warehouse workloads.
F. New AWS Regions Globally:
We have 69 availability zones in 22 regions and have announced plans for 16 more Availability Zones and five more Regions. But we are at a fraction of the geographies we’re going to be in. And so I think over time, we will be in most of the large developed geographies with regions.
What are the major topics Andy covered in his 2019 re:Invent Keynote?
Andy’s re:Invent 2019 keynote covered the six most critical components of a successful transformation, including: Leadership Alignment, Breadth and Depth, Modernization, Data at Scale, Machine Learning, and Breaking Through Barriers. A summary of the re:Invent 2019 keynote themes is available here.
You’ve said that 90% of what AWS builds is what AWS customers ask for, while the remaining 10% is focused on strategic interpretations of customer needs. What’s a newer example of a something AWS built that customers asked for, and then what’s a newer example of a “strategic interpretation” of customer needs?
1: A good example of a service that we launched as a direct result of many customers asking for it is Amazon Redshift in February of 2013. Previously, we weren’t really thinking about a data warehouse, but customers were unhappy with the options that were available at the time—they were too hard to use and too costly. So they said us, “Why don’t you guys do this?” So we launched Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze data using existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and is less than one-tenth of the cost of most traditional data warehousing solutions. In its first year, Redshift quickly became the fastest-growing service in the history of AWS, holding that title until we announced Amazon Aurora a year later. Amazon Aurora is a fully MySQL-compatible database that provides the performance and availability of high-end commercial databases at one-tenth the cost. Here again, customers had been fed up with what’s been available for the last couple decades, especially the high cost, lock-in, punitive licensing terms, and complexity of old guard databases, and they asked us to come up with a better solution. Today, Amazon Aurora remains the fastest growing service in the history of AWS.
2: An example of something we launched that customers were not asking us for, but that we thought they would really like, is AWS Lambda. AWS Lambda lets customers upload some code through the AWS Management Console, set triggers, and when those triggers are met, it runs that code. Customers don’t have to think about servers. They don’t have to think about clusters. They don’t have to think about auto-scaling or administration. It’s all done for them. Lambda started with a seed of a customer idea. Many customers had situations where they were doing a just a few seconds of processing. For example, in some cases, the customer wanted to process objects as they were added to Amazon S3, such as resizing images. To do this they had to provision a compute instance and leave it up, even though each image took only a few seconds to process. And if they wanted to do this with high availability architecture, they would have to spin up multiple instances across multiple availability zones. Maintaining instances for these situations was time consuming and wasteful. These customers didn’t say “give us Lambda,” but we thought about what they were telling us and the reality was that they were only needing compute for a few seconds when some type of event occurred that triggered processing. And, there were many use cases where this was the case , such as extracting, transforming, and loading data for analytics and e-commerce, and automating scheduled tasks for IT processes. This is what put us on the path to Lambda and pioneering the event-driven, serverless computing space. This was a brand new concept. When you think about how many customers are running AWS Lambda today, it’s kind of astonishing. Today there are hundreds of thousands of customers running AWS Lambda, including companies like Autodesk, Coca-Cola, Expedia, Fannie Mae, FICO, FINRA, London Stock Exchange, T-Mobile, Thomson Reuters, Airbnb, Fox Corporation, General Electric, Philips, Netflix, Siemens, and Volkswagen, among others.
Is the AWS Cloud secure?
Yes, and security will always be our top priority. AWS has been architected to be the most flexible and secure cloud computing environment available today. Our core infrastructure is built to satisfy the security requirements for military, global banks, and other high-sensitivity organizations. AWS uses the same secure hardware and software to build and operate each of our regions, so all of our customers benefit from the only commercial cloud that has had its service offerings and associated supply chain vetted and accepted as secure enough for top-secret workloads. This is backed by a deep set of cloud security tools, with more than 200 security, compliance, and governance services and key features.
We have a shared responsibility model with the customer; AWS manages and controls the components from the host operating system and virtualization layer down to the physical security of the facilities in which the services operate, and AWS customers are responsible for building secure applications. We provide a wide variety of best practices documents, encryption tools, and other guidance our customers can leverage in delivering application-level security measures. In addition, AWS partners offer hundreds of tools and features to help customers meet their security objectives, ranging from network security, configuration management, access control, and data encryption.
AWS’s scale allows significantly more investment in security policing and countermeasures than almost any large company could afford themselves. For example, lots of CIOs worry about the rogue server under a developer’s desk running something destructive or that they don’t want running. Today, it’s really hard (if not impossible) for CIOs to know how many orphans there are and where they might be. With AWS, CIOs can use tools like AWS Config and resource tagging to see exactly what cloud assets their company is using at any moment. No more hidden servers under the desk or anonymously placed servers in a rack and plugged into the corporate network.
AWS has achieved a number of internationally recognized certifications and accreditations, demonstrating compliance with 3rd party assurance frameworks, such as ISO 27017 for cloud security, ISO 27018 for cloud privacy, and SOC 1, SOC 2 and SOC 3. Customers can be PCI and HIPAA compliant on AWS, and we have achieved important certifications like FedRAMP at the Moderate and High levels), as well as SRG Impact Levels 2, 4, 5 and 6 for DoD systems. These certifications help support customer compliance with requirements such as ITAR, FISMA, CJIS, and NIST 800-53 and 171. We also have ISO9001 which is primarily for healthcare, life sciences, medical devices, automotive and aerospace.
Are there any companies that run their entire infrastructure on AWS?
Yes, there are a number of companies that are running—or in the process of moving—the vast majority, or all of their infrastructure to AWS. Although most enterprises typically build a two-to-four-year migration plan, if you look at the amount of enterprise migration to AWS, it’s very substantial.
A complete list of AWS customers and partners who are “all in” on AWS (either born on AWS or have moved/are moving 90% or more of their IT infrastructure) and willing to say so in public is available here: http://bit.ly/2D0xIzh
What are the biggest challenges for enterprises and large customers moving to the cloud? (Can also be used to answer-what separates those customers that are aggressively/successfully migrating from those that aren’t?)
A lot of the biggest challenges for large organizations to move to the cloud aren’t technical, they’re about people and culture. The biggest differences between organizations that talk about moving to the cloud, and those that actually do it and are having the most success often comes down to a few key things:
First, the senior leadership team needs to be aligned and truly committed that they want to move to the cloud. And they need to be setting clear direction and expectations with the rest of the organization to get everyone on the same page and working towards the same thing. It’s easy for others to do nothing or block things if the leadership team isn’t making the move a priority and building a culture for change.
Then, the most successful organizations moving to the cloud started with an aggressive top-down goal that forced the organization to move faster than it would have organically.
Third, it’s really important that organizations are trained on the cloud and comfortable with the concepts as part of the whole process. We train hundreds-of-thousands of people a year for that purpose.
And last, sometimes we find that organizations can get paralyzed if they can’t figure out how to move every last workload. There is no need to boil the ocean. So we often work with organizations to do a portfolio analysis to assess each application and build a plan for what to move short term, medium term, and last. This helps organizations get the benefits of the cloud for many of their applications much more quickly, and it really helps inform how they move the rest.
What is cloud computing
The term “cloud computing” refers to the on-demand delivery of IT resources via the Internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining your own data centers and servers, organizations can acquire technology such as compute power, storage, databases, and other services on an as-needed basis. It is similar to how consumers flip a switch to turn on the lights in their home, and the power company sends electricity. With cloud computing, AWS manages and maintains the technology infrastructure in a secure environment and businesses access these resources via the Internet to develop and run their applications. Capacity can grow or shrink instantly and businesses only pay for what they use.
How was AWS started?
After over a decade of building and running the highly scalable web application, Amazon.com, the company realized that it had developed a core competency in operating massive scale technology infrastructure and data centers, and embarked on a much broader mission of serving a new customer segment—developers and businesses—with web services they can use to build sophisticated, scalable applications.
What are the advantages of moving to the cloud? (or, why are enterprises moving so quickly to the cloud)
There are five reasons companies are moving so quickly to the AWS cloud.
The first is agility. AWS lets customers quickly spin up resources as they need them, deploying hundreds or even thousands of servers in minutes. This means customers can very quickly develop and roll out new applications, and it means teams can experiment and innovate more quickly and frequently. If an experiment fails, you can always de-provision those resources without risk.
The second reason is cost savings. If you look at how people end up moving to the cloud, almost always the conversation starter ends up being cost. AWS allows customers to trade capital expense for variable expense, and only pay for IT as they consume it. And, the variable expense is much lower than what customers can do for themselves because of AWS’s economies of scale. For example, Dow Jones has estimated that migrating its data centers to AWS will contribute to a global savings of $100 million in infrastructure costs.
The third reason is elasticity. Customers used to over provision to ensure they had enough capacity to handle their business operations at the peak level of activity. Now, they can provision the amount of resources that they actually need, knowing they can instantly scale up or down along with the needs of their business, which also reduces cost and improves the customer’s ability to meet their user’s demands.
The fourth reason is that the cloud allows customers to innovate faster because they can focus their highly valuable IT resources on developing applications that differentiate their business and transform customer experiences instead of the undifferentiated heavy lifting of managing infrastructure and data centers.
The fifth reason is that AWS enables customers to deploy globally in minutes. AWS has the concept of a Region, which is a physical location around the world where we cluster data centers. We call each group of logical data centers an Availability Zone. Using AWS, customers can leverage 69 Availability Zones across 22 geographic regions worldwide. And, we don’t plan to stop there.
What did you announce in the Q4 2019 quarterly earnings about AWS?
We saw continued strength in Amazon Web Services with revenue of nearly $10 billion (actual = $9.9 billion) in Q4, up 34% year-over-year. AWS is a large and rapidly growing business with a nearly $40 billion dollar run rate [actual rr is $39.83].
Are you are going to spin off the AWS business?
We have no plans to do so.
Have you pulled back on price cuts? How many price cuts have you had since inception?
Periodic price reductions ([80] since our inception) are a normal part of business for us. We expect to continue to lower prices this year and for many years to come. Our pricing philosophy is to work relentlessly to take cost out of our own cost structure and to pass those savings back to our AWS customers in the form of lower prices. It’s easy to lower prices, it’s much harder to be able to afford to lower prices – we work really hard at that in all our businesses, including AWS
