Amazon EC2 Reserved Instances and Other AWS Reservation Models AWS Whitepaper Amazon EC2 Reserved Instances and Other AWS Reservation Models AWS Whitepaper
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Amazon Dynamodb
Dynamo Amazon DynamoDB Nicolas Travers Inspiré de Advait Deo Vertigo N. Travers ESILV : Dynamo Amazon DynamoDB – What is it ? • Fully managed nosql database service on AWS • Data model in the form of tables • Data stored in the form of items (name – value attributes) • Automatic scaling ▫ Provisioned throughput ▫ Storage scaling ▫ Distributed architecture • Easy Administration • Monitoring of tables using CloudWatch • Integration with EMR (Elastic MapReduce) ▫ Analyze data and store in S3 Vertigo N. Travers ESILV : Dynamo Amazon DynamoDB – What is it ? key=value key=value key=value key=value Table Item (64KB max) Attributes • Primary key (mandatory for every table) ▫ Hash or Hash + Range • Data model in the form of tables • Data stored in the form of items (name – value attributes) • Secondary Indexes for improved performance ▫ Local secondary index ▫ Global secondary index • Scalar data type (number, string etc) or multi-valued data type (sets) Vertigo N. Travers ESILV : Dynamo DynamoDB Architecture • True distributed architecture • Data is spread across hundreds of servers called storage nodes • Hundreds of servers form a cluster in the form of a “ring” • Client application can connect using one of the two approaches ▫ Routing using a load balancer ▫ Client-library that reflects Dynamo’s partitioning scheme and can determine the storage host to connect • Advantage of load balancer – no need for dynamo specific code in client application • Advantage of client-library – saves 1 network hop to load balancer • Synchronous replication is not achievable for high availability and scalability requirement at amazon • DynamoDB is designed to be “always writable” storage solution • Allows multiple versions of data on multiple storage nodes • Conflict resolution happens while reads and NOT during writes ▫ Syntactic conflict resolution ▫ Symantec conflict resolution Vertigo N. -
Performance at Scale with Amazon Elasticache
Performance at Scale with Amazon ElastiCache July 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 ElastiCache Overview ......................................................................................................... 2 Alternatives to ElastiCache ................................................................................................. 2 Memcached vs. Redis ......................................................................................................... 3 ElastiCache for Memcached ............................................................................................... 5 Architecture with ElastiCache for Memcached ............................................................... -
Amazon Documentdb Deep Dive
DAT326 Amazon DocumentDB deep dive Joseph Idziorek Antra Grover Principal Product Manager Software Development Engineer Amazon Web Services Fulfillment By Amazon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda What is the purpose of a document database? What customer problems does Amazon DocumentDB (with MongoDB compatibility) solve and how? Customer use case and learnings: Fulfillment by Amazon What did we deliver for customers this year? What’s next? © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Purpose-built databases Relational Key value Document In-memory Graph Search Time series Ledger Why document databases? Denormalized data Normalized data model model { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? GET https://api.yelp.com/v3/businesses/{id} { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? response = yelp_api.search_query(term='ice cream', location='austin, tx', sort_by='rating', limit=5) Why document databases? for i in response['businesses']: col.insert_one(i) db.businesses.aggregate([ { $group: { _id: "$price", ratingAvg: { $avg: "$rating"}} } ]) db.businesses.find({ -
A Motion Is Requested to Authorize the Execution of a Contract for Amazon Business Procurement Services Through the U.S. Communities Government Purchasing Alliance
MOT 2019-8118 Page 1 of 98 VILLAGE OF DOWNERS GROVE Report for the Village Council Meeting 3/19/2019 SUBJECT: SUBMITTED BY: Authorization of a contract for Amazon Business procurement Judy Buttny services Finance Director SYNOPSIS A motion is requested to authorize the execution of a contract for Amazon Business procurement services through the U.S. Communities Government Purchasing Alliance. STRATEGIC PLAN ALIGNMENT The goals for 2017-2019 includes Steward of Financial Sustainability, and Exceptional, Continual Innovation. FISCAL IMPACT There is no cost to utilize Amazon Business procurement services through the U.S. Communities Government Purchasing Alliance. RECOMMENDATION Approval on the March 19, 2019 Consent Agenda. BACKGROUND U.S. Communities Government Purchasing Alliance is the largest public sector cooperative purchasing organization in the nation. All contracts are awarded by a governmental entity utilizing industry best practices, processes and procedures. The Village of Downers Grove has been a member of the U.S. Communities Government Purchasing Alliance since 2008. Through cooperative purchasing, the Village is able to take advantage of economy of scale and reduce the cost of goods and services. U.S. Communities has partnered with Amazon Services to offer local government agencies the ability to utilize Amazon Business for procurement services at no cost to U.S. Communities members. Amazon Business offers business-only prices on millions of products in a competitive digital market place and a multi-level approval workflow. Staff can efficiently find quotes and purchase products for the best possible price, and the multi-level approval workflow ensures this service is compliant with the Village’s competitive process for purchases under $7,000. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
Amazon Elasticache Deep Dive Powering Modern Applications with Low Latency and High Throughput
Amazon ElastiCache Deep Dive Powering modern applications with low latency and high throughput Michael Labib Sr. Manager, Non-Relational Databases © 2020, Amazon Web Services, Inc. or its Affiliates. Agenda • Introduction to Amazon ElastiCache • Redis Topologies & Features • ElastiCache Use Cases • Monitoring, Sizing & Best Practices © 2020, Amazon Web Services, Inc. or its Affiliates. Introduction to Amazon ElastiCache © 2020, Amazon Web Services, Inc. or its Affiliates. Purpose-built databases © 2020, Amazon Web Services, Inc. or its Affiliates. Purpose-built databases © 2020, Amazon Web Services, Inc. or its Affiliates. Modern real-time applications require Performance, Scale & Availability Users 1M+ Data volume Terabytes—petabytes Locality Global Performance Microsecond latency Request rate Millions per second Access Mobile, IoT, devices Scale Up-out-in E-Commerce Media Social Online Shared economy Economics Pay-as-you-go streaming media gaming Developer access Open API © 2020, Amazon Web Services, Inc. or its Affiliates. Amazon ElastiCache – Fully Managed Service Redis & Extreme Secure Easily scales to Memcached compatible performance and reliable massive workloads Fully compatible with In-memory data store Network isolation, encryption Scale writes and open source Redis and cache for microsecond at rest/transit, HIPAA, PCI, reads with sharding and Memcached response times FedRAMP, multi AZ, and and replicas automatic failover © 2020, Amazon Web Services, Inc. or its Affiliates. What is Redis? Initially released in 2009, Redis provides: • Complex data structures: Strings, Lists, Sets, Sorted Sets, Hash Maps, HyperLogLog, Geospatial, and Streams • High-availability through replication • Scalability through online sharding • Persistence via snapshot / restore • Multi-key atomic operations A high-speed, in-memory, non-Relational data store. • LUA scripting Customers love that Redis is easy to use. -
Amazon Mechanical Turk Developer Guide API Version 2017-01-17 Amazon Mechanical Turk Developer Guide
Amazon Mechanical Turk Developer Guide API Version 2017-01-17 Amazon Mechanical Turk Developer Guide Amazon Mechanical Turk: Developer Guide Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Mechanical Turk Developer Guide Table of Contents What is Amazon Mechanical Turk? ........................................................................................................ 1 Mechanical Turk marketplace ....................................................................................................... 1 Marketplace rules ............................................................................................................... 2 The sandbox marketplace .................................................................................................... 2 Tasks that work well on Mechanical Turk ...................................................................................... 3 Tasks can be completed within a web browser ....................................................................... 3 Work can be broken into distinct, bite-sized tasks ................................................................. -
Web Application Hosting in the AWS Cloud AWS Whitepaper Web Application Hosting in the AWS Cloud AWS Whitepaper
Web Application Hosting in the AWS Cloud AWS Whitepaper Web Application Hosting in the AWS Cloud AWS Whitepaper Web Application Hosting in the AWS Cloud: AWS Whitepaper Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Web Application Hosting in the AWS Cloud AWS Whitepaper Table of Contents Abstract ............................................................................................................................................ 1 Abstract .................................................................................................................................... 1 An overview of traditional web hosting ................................................................................................ 2 Web application hosting in the cloud using AWS .................................................................................... 3 How AWS can solve common web application hosting issues ........................................................... 3 A cost-effective alternative to oversized fleets needed to handle peaks ..................................... 3 A scalable solution to handling unexpected traffic -
Analytics Lens AWS Well-Architected Framework Analytics Lens AWS Well-Architected Framework
Analytics Lens AWS Well-Architected Framework Analytics Lens AWS Well-Architected Framework Analytics Lens: AWS Well-Architected Framework Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Analytics Lens AWS Well-Architected Framework Table of Contents Abstract ............................................................................................................................................ 1 Abstract .................................................................................................................................... 1 Introduction ...................................................................................................................................... 2 Definitions ................................................................................................................................. 2 Data Ingestion Layer ........................................................................................................... 2 Data Access and Security Layer ............................................................................................ 3 Catalog and Search Layer ................................................................................................... -
October 24, 2013—Amazon.Com, Inc
AMAZON.COM ANNOUNCES THIRD QUARTER SALES UP 24% TO $17.09 BILLION SEATTLE—(BUSINESS WIRE)—October 24, 2013—Amazon.com, Inc. (NASDAQ: AMZN) today announced financial results for its third quarter ended September 30, 2013. Operating cash flow increased 48% to $4.98 billion for the trailing twelve months, compared with $3.37 billion for the trailing twelve months ended September 30, 2012. Free cash flow decreased 63% to $388 million for the trailing twelve months, compared with $1.06 billion for the trailing twelve months ended September 30, 2012. Free cash flow for the trailing twelve months ended September 30, 2013 includes fourth quarter 2012 cash outflows for purchases of corporate office space and property in Seattle, Washington, of $1.4 billion. Common shares outstanding plus shares underlying stock-based awards totaled 475 million on September 30, 2013, compared with 469 million one year ago. Net sales increased 24% to $17.09 billion in the third quarter, compared with $13.81 billion in third quarter 2012. Excluding the $332 million unfavorable impact from year-over-year changes in foreign exchange rates throughout the quarter, net sales grew 26% compared with third quarter 2012. Operating loss was $25 million in the third quarter, compared with an operating loss of $28 million in third quarter 2012. The unfavorable impact from year-over-year changes in foreign exchange rates throughout the quarter on operating loss was $7 million. Net loss was $41 million in the third quarter, or $0.09 per diluted share, compared with a net loss of $274 million, or $0.60 per diluted share, in third quarter 2012. -
Leveraging Cloud-Based Predictive Analytics to Strengthen Audience Engagement
TECHNICAL PAPER Leveraging Cloud-Based Predictive Analytics to Strengthen Audience Engagement By U. Shakeel and M. Limcaco Abstract appointed time for the airing of our favorite TV show. To grow their business and increase their audience, content People now expect to watch the programming they distributors must understand the viewing habits and interests want on their own schedule, which is driving more and of content consumers. This typically requires solving tough more media companies to consider providing their own computational problems, such as rapidly processing vast over-the-top service. These services use the internet amounts of raw data from Web sites, social media, devices, for delivery, which introduces potential quality issues catalogs, and back-channel sources. For- that are out of the control of the media tunately, today’s content distributors can owner or distributer. To mitigate these take advantage of the scalability, cost risks, many media distributors invest effectiveness, and pay-as-you-go model This raises the heavily in solutions that detect play- of the cloud to address these challenges. question of how to back issues; these solutions require In this paper, we show content distribu- build the next- large amounts of computational capac- ity to process massive, raw datasets to tors how to use cloud technologies to build generation media predictive analytic solutions. We exam- provide real-time course correction. In ine architectural patterns for optimiz- deliv ery platform this way, distributors can provide more ing media delivery, and we discuss how that not only delivers reliable content that caters to the view- to assess the overall consumer experience reli able content ing habits of their audience. -
Enter the Purpose-Built Database Era: Finding the Right Database Type for the Right Job
Enter the Purpose-Built Database Era: Finding the right database type for the right job 1 INTRODUCTION Stepping into the purpose-built era Data is a strategic asset for every organization. As data continues to exponentially grow, databases are becoming increasingly crucial to understanding data and converting it to valuable insights. IT leaders need to look for ways to get more value from their data. If you’re running legacy databases on-premises, you’re likely finding that provisioning, operating, scaling, and managing databases is tedious, time-consuming, and expensive. You need modernized database solutions that allow you to spend time innovating and building new applications—not managing infrastructure. Moving on-premises data to managed databases built for the cloud can help you reduce time and costs. Once your databases are in the cloud, you can innovate and build new applications faster—all while getting deeper and more valuable insights. Migrating to the cloud is the first step toward entering the era of purpose-built databases. But once in the cloud, how do you know which types of databases to use for which functions? Read on to learn more about purpose-built database types—and how you can ensure a smooth transition into an era of innovation, performance, and business success. 2 WHY CHANGE? Going beyond relational only Before we begin discussing purpose-built databases, let’s examine the status quo—using relational databases for just about every use case. Relational databases were designed for tabular data with consistent structure and fixed schema. They work for problems that are well defined at the onset.