AWS Glossary

Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. With a few clicks in the AWS Management Console, you can create an API that acts as a “front door” for applications to access data, business logic, or functionality from your back-end services, such as workloads running on Amazon Elastic Compute Cloud (Amazon EC2), code running on AWS Lambda, or any web application.

Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. Amazon API Gateway has no minimum fees or startup costs. You pay only for the API calls you receive and the amount of data transferred out.

an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to setup or manage, and you pay only for the queries you run. To get started, simply point to your data in S3, define the schema, and start querying using standard SQL.

Amazon Aurora is a MySQL-compatible relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL with the security, availability, and reliability of a commercial database at one tenth the cost.

Amazon CloudFront is a global content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to your viewers with low latency and high transfer speeds. CloudFront is integrated with AWS – including physical locations that are directly connected to the AWS global infrastructure, as well as software that works seamlessly with services including AWS Shield for DDoS mitigation, Amazon S3, Elastic Load Balancing or Amazon EC2 as origins for your applications, and Lambda@Edge to run custom code close to your viewers.

You can get started with CloudFront in minutes, using the same AWS tools that you’re already familiar with: APIs, AWS Management Console, AWS CloudFormation, CLIs, and SDKs. CloudFront offers a simple, pay-as-you-go pricing model with no upfront fees or required long-term contracts, and support for CloudFront is included in your existing AWS Support subscription.

provides a reliable, scalable, and flexible monitoring solution that you can start using within minutes. You no longer need to set up, manage, and scale your own monitoring systems and infrastructure.

• Use CloudWatch to monitor your AWS resources and the applications you run on AWS in real time.
• Use CloudWatch Events to send system events from AWS resources to AWS Lambda functions, Amazon SNS topics, streams in Amazon Kinesis, and other target types.
• Use CloudWatch Logs to monitor, store, and access your log files from Amazon EC2 instances, AWS CloudTrail, or other sources.

uses natural language processing (NLP) to extract insights about the content of documents without the need of any special preprocessing. Amazon Comprehend processes any text files in UTF-8 format. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. Use Amazon Comprehend to create new products based on understanding the structure of documents. With Amazon Comprehend you can search social networking feeds for mentions of products, scan an entire document repository for key phrases, or determine the topics contained in a set of documents.

equip machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud at any scale. You can quickly launch Amazon EC2 instances on Amazon Linux or Ubuntu, preinstalled with popular deep learning frameworks. Examples include Apache MXNet and Gluon, TensorFlow, the Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Theano, Torch and Keras. You can use these frameworks to train sophisticated, custom AI models; experiment with new algorithms; or to learn new skills and techniques.

Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs. You only pay for the AWS resources needed to store and run your applications.

pairs a connected HD camera developer kit with a set of sample projects to help developers learn machine learning concepts using hands-on computer vision use cases. AWS DeepLens is preconfigured to work with multiple AWS services to provide a deep learning framework that is optimized for AWS DeepLens, so it is easy to build computer vision applications. AWS DeepLens is powerful enough for experts, but designed to help all developers get started quickly with little to no experience in deep learning.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use Amazon DynamoDB to create a database table that can store and retrieve any amount of data, and serve any level of request traffic. Amazon DynamoDB automatically spreads the data and traffic for the table over a sufficient number of servers to handle the request capacity specified by the customer and the amount of data stored, while maintaining consistent and fast performance.

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides resizeable computing capacity—literally, servers in Amazon’s data centers—that you use to build and host your software systems.

Amazon Elastic Container Registry (Amazon ECR) is a fully managed Docker container registry that makes it easy for developers to store, manage, and deploy Docker container images.

Amazon Elastic Container Service (Amazon ECS) is a highly scalable, fast, container management service that makes it easy to run, stop, and manage Docker containers on a cluster of Amazon EC2 instances.

Amazon Elastic Container Service runs the Kubernetes management infrastructure for you across multiple AWS availability zones to eliminate a single point of failure. Amazon EKS is certified Kubernetes conformant so you can use existing tooling and plugins from partners and the Kubernetes community. Applications running on any standard Kubernetes environment are fully compatible and can be easily migrated to Amazon EKS.

a web service that makes it easy to process large amounts of data efficiently. Amazon EMR uses Hadoop processing combined with several AWS products to do such tasks as web indexing, data mining, log file analysis, machine learning, scientific simulation, and data warehousing.

Amazon Glacier is a storage service optimized for infrequently used data, or “cold data.” The service provides durable and extremely low-cost storage with security features for data archiving and backup. With Amazon Glacier, you can store your data cost effectively for months, years, or even decades. Amazon Glacier enables you to offload the administrative burdens of operating and scaling storage to AWS, so you don’t have to worry about capacity planning, hardware provisioning, data replication, hardware failure detection and recovery, or time-consuming hardware migrations. For more service highlights and pricing information, go to the Amazon Glacier detail page.

a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. AWS Glue consists of a central data repository known as the AWS Glue Data Catalog, an ETL engine that automatically generates Python code, and a flexible scheduler that handles dependency resolution, job monitoring, and retries. AWS Glue is serverless, so there’s no infrastructure to set up or manage. Use the AWS Glue console to discover your data, transform it, and make it available for search and querying. You can also use the AWS Glue API operations to interface with AWS Glue.

makes it easy to collect, process, and analyze video and data streams in real time.

• Use Kinesis Video Streams to capture, process, and store video streams for analytics and machine learning.
• Use Kinesis Data Streams to build custom applications that analyze data streams using popular stream processing frameworks.
• Use Kinesis Data Firehose to load data streams into AWS data stores.
• Use Kinesis Data Analytics to analyze data streams with SQL.

an AWS service for building conversational interfaces into applications using voice and text. With Amazon Lex, the same deep learning engine that powers Amazon Alexa is now available to any developer, enabling you to build sophisticated, natural language chatbots into your new and existing applications. Amazon Lex provides the deep functionality and flexibility of natural language understanding (NLU) and automatic speech recognition (ASR) to enable you to build highly engaging user experiences with lifelike, conversational interactions and create new categories of products.

makes it easy for developers to build smart applications, including applications for fraud detection, demand forecasting, targeted marketing, and click prediction. The powerful algorithms of Amazon Machine Learning create machine learning (ML) models by finding patterns in your existing data. The service uses these models to process new data and generate predictions for your application.

a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud. Amazon MQ provides interoperability with your existing applications and services. Amazon MQ works with your existing applications and services without the need to manage, operate, or maintain your own messaging system.

a Text-to-Speech (TTS) cloud service that converts text into lifelike speech. You can use Amazon Polly to develop applications that increase engagement and accessibility. Amazon Polly supports multiple languages and includes a variety of lifelike voices, so you can build speech-enabled applications that work in multiple locations and use the ideal voice for your customers.

Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, resizable capacity for an industry-standard relational database and manages common database administration tasks.

Amazon RDS for PostgreSQL gives you access to the capabilities of a familiar PostgreSQL database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS.

With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale up to 16 TB of storage and 40,000 IOPS. Amazon RDS for PostgreSQL also enables you to scale out beyond the capacity of a single database deployment for read-heavy database workloads.

a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

makes it easy to add image and video analysis to your applications. You just provide an image or video to the Rekognition API, and the service can identify objects, people, text, scenes, and activities. It can detect any inappropriate content as well. Amazon Rekognition also provides highly accurate facial analysis and facial recognition. You can detect, analyze, and compare faces for a wide variety of use cases, including user verification, cataloging, people counting, and public safety.

Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images and videos daily—and requires no machine learning expertise to use. Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image or video file that’s stored in Amazon S3. Amazon Rekognition is always learning from new data, and we’re continually adding new labels and facial recognition features to the service.

Amazon Simple Storage Service (Amazon S3) is storage for the Internet. You can use Amazon S3 to store and retrieve any amount of data at any time, from anywhere on the web. You can accomplish these tasks using the simple and intuitive web interface of the AWS Management Console.

a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker provides flexible distributed training options that adjust to your specific workflows.

a service for customers who want to transport terabytes or petabytes of data to and from AWS, or who want to access the storage and compute power of the AWS Cloud locally and cost effectively in places where connecting to the internet may not be an option.

provides transcription services for your audio files. It uses advanced machine learning technologies to recognize spoken words and transcribe them into text.

a neural machine translation service for translating text to and from English across a breadth of supported languages. Powered by deep-learning technologies, Amazon Translate delivers fast, high-quality, and affordable language translation. It provides a managed, continually trained solution so you can easily translate company and user-authored content or build applications that require support across multiple languages. The machine translation engine has been trained on a wide variety of content across different domains to produce quality translations that serve any industry need.

AWS Fargate is a compute engine for Amazon ECS and EKS^* that allows you to run containers without having to manage servers or clusters. With AWS Fargate, you no longer have to provision, configure, and scale clusters of virtual machines to run containers. This removes the need to choose server types, decide when to scale your clusters, or optimize cluster packing. AWS Fargate removes the need for you to interact with or think about servers or clusters. Fargate lets you focus on designing and building your applications instead of managing the infrastructure that runs them.

With AWS Lambda, you can run code without provisioning or managing servers. You pay only for the compute time you consume—there’s no charge when your code isn’t running. You can run code for virtually any type of application or backend service—all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app.

AWS Server Migration Service (SMS) is an agentless service which makes it easier and faster for you to migrate thousands of on-premises workloads to AWS. AWS SMS allows you to automate, schedule, and track incremental replications of live server volumes, making it easier for you to coordinate large-scale server migrations.