Amazon dynamodb at aws re_invent 2016—wrap-up _ aws database blog

We wrapped up an exciting AWS re:Invent. Data recovery joondalup It was great to interact with current and future Amazon DynamoDB customers and hear their feedback and suggestions.

Multiple re:Invent breakout sessions highlighted DynamoDB.

Database of genomic variants These sessions consisted of deep dives, best practices, and customer talks with real-life examples from industries like gaming, adtech, IoT, and others.

In case you missed attending a session, following are links to the session recordings, along with session abstracts to give you an idea of what each session is about. Database viewer We hope you find these videos useful as you leverage the performance and flexibility of DynamoDB for your applications.

In this session, Raju Gulabani, vice president of AWS Database Services (AWS), discusses the evolution of database services on AWS and the new database services and features we launched this year, and shares our vision for continued innovation in this space. H data recovery registration code free download We are witnessing an unprecedented growth in the amount of data collected, in many different shapes and forms. Database hardware Storage, management, and analysis of this data requires database services that scale and perform in ways not possible before. Database roles AWS offers a collection of such database and other data services like Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR to process, store, manage, and analyze data. B tree database management system In this session, we provide an overview of AWS database services and discuss how our customers are using these services today.

In this session, we look at questions such as: Which database is best suited for your use case? Should you choose a relational database or NoSQL or a data warehouse for your workload? Would a managed service like Amazon RDS, Amazon DynamoDB, or Amazon Redshift work better for you, or would it be better to run your own database on Amazon EC2? FanDuel has been running its fantasy sports service on Amazon Web Services (AWS) since 2012. Database file We learn best practices and insights from FanDuel’s successful migrations from self-managed databases on EC2 to fully managed database services.

In this session, we explore Amazon DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. Data recovery near me We go over best practices for schema design with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. Database job description We explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, DynamoDB Streams, and more. Data recovery 94fbr We also provide lessons learned from operating DynamoDB at scale, including provisioning DynamoDB for IoT.

In this session, we talk about the key differences between a relational database management service (RDBMS) and nonrelational (NoSQL) databases like Amazon DynamoDB. Database foreign key You will learn about suitable and unsuitable use cases for NoSQL databases. Database as a service You’ll learn strategies for migrating from an RDBMS to DynamoDB through a five-phase, iterative approach. Iphone 6 data recovery See how Sony migrated an on-premises MySQL database to the cloud with Amazon DynamoDB, and see the results of this migration.

In this session, we share how an team that owns a document management platform that manages billions of critical customer documents for migrated from a relational to a nonrelational database. Database google drive Initially, the service was built as an Oracle database. Data recovery geek squad As it grew, the team discovered the limits of the relational model and decided to migrate to a nonrelational database. Database recovery pending They chose Amazon DynamoDB for its built-in resilience, scalability, and predictability. Data recovery prices We provide a template that customers can use to migrate from a relational data store to DynamoDB. Database sharding We also provided details about the entire process: design patterns for moving from a SQL schema to a NoSQL schema; mechanisms used to transition from an ACID (Atomicity, Consistency, Isolation, Durability) model to an eventually consistent model; migration alternatives considered; pitfalls in common migration strategies; and how to ensure service availability and consistency during migration.

In this session, we learn how Toyota Racing Development (TRD) developed a robust and highly performant real-time data analysis tool for professional racing. Database keys with example Learn how TRD structured a reliable, maintainable, decoupled architecture built around Amazon DynamoDB as both a streaming mechanism and a long-term persistent data store. Data recovery xfs In racing, milliseconds matter and even moments of downtime can cost a race. Database management systems 3rd edition We see how TRD used DynamoDB together with Amazon Kinesis and Amazon Kinesis Firehose to build a real-time streaming data analysis tool for competitive racing.

In this session Greg Brandt and Liyin Tang, Data Infrastructure engineers from Airbnb, discuss the design and architecture of Airbnb’s streaming ETL infrastructure, which exports data from RDS for MySQL and DynamoDB into Airbnb’s data warehouse, using a system called SpinalTap. Database engineer salary We also discuss how we leverage Apache Spark Streaming to compute derived data from tracking topics and/or database tables, and HBase to provide immediate data access and generate cleanly time-partitioned Hive tables.

“Attribution” is the marketing term of art for allocating full or partial credit to individual advertisements that eventually lead to a purchase, sign up, download, or other desired consumer interaction. Jstor database In this session, DataXu shares how they used DynamoDB at the core of their attribution system to store terabytes of advertising history data. E m database The system is cost-effective and dynamically scales from 0 to 300K requests per second on demand with predictable performance and low operational overhead.

In this session, Carl Youngblood, Lead Engineer of Under Armour, shares the keys to success as Under Armour implemented cross-region replication with Amazon DynamoDB Streams. Data recovery richmond va The session also includes a quick recap of DynamoDB and its features.

In this session, we talk about how Quantcast used AWS services including DynamoDB to implement real-time campaign analytics. Data recovery software Quantcast provides its advertising clients the ability to run targeted ad campaigns reaching millions of online users. Data recovery advisor The real-time bidding for campaigns runs on thousands of machines across the world. Database host name When Quantcast wanted to collect and analyze campaign metrics in real-time, they turned to AWS to rapidly build a scalable, resilient, and extensible framework. Database performance Quantcast used Amazon Kinesis streams to stage data, Amazon EC2 instances to shuffle and aggregate the data, and Amazon DynamoDB and Amazon ElastiCache for building scalable time-series databases. Data recovery broken hard drive With Elastic Load Balancing and Auto Scaling groups, they are able to set up distributed microservices with minimal operation overhead. Database xe This session discusses their use case, how they architected the application with AWS technologies integrated with their existing home-grown stack, and the lessons they learned.

In this session, we share an overview of leveraging serverless architectures to support high performance data intensive applications. Database yml mysql Fulfillment by Amazon (FBA) built the Seller Inventory Authority Platform (IAP) using Amazon DynamoDB Streams, AWS Lambda functions, Amazon Elasticsearch Service, and Amazon Redshift to improve results and reduce costs. 5 database is locked Scopely shares how they used a flexible logging system built on Amazon Kinesis, Lambda, and Amazon Elasticsearch Service to provide high-fidelity reporting on hotkeys in Memcached and DynamoDB, and drastically reduce the incidence of hotkeys. Database fundamentals Both of these customers are using managed services and serverless architecture to build scalable systems that can meet the projected business growth without a corresponding increase in operational costs.

In this session, Chris Taylor from Chick-fil-A shares how they managed to scale using AWS services. Database concepts Chris leads the team providing back-end services for the massively popular Chick-fil-A One mobile app that launched in June 2016. Database icon Chick-fil-A follows AWS best practices for web services and leverages numerous AWS services, including AWS Elastic Beanstalk, Amazon DynamoDB, AWS Lambda, and Amazon S3. Database versioning This was the largest technology-dependent promotion in Chick-fil-A history. Database 2013 To ensure their architecture would perform at unknown and massive scale, Chris worked with AWS Support through an AWS Infrastructure Event Management (IEM) engagement and leaned on automated operations to enable load testing before launch.

In this session, you’ll learn about Telltale Games’ migration from Apache CouchDB to Amazon DynamoDB, the challenges of adjusting capacity to handling spikes in database activity, and how Telltale Games streamlined its analytics storage to provide new perspectives of player interaction to improve its games. Database cursor Every choice made in Telltale Games titles influences how your character develops and how the world responds to you. Database list With millions of users making thousands of choices in a single episode, Telltale Games tracks this data and leverages it to build more relevant stories in real time as the season is developed.

In this session, we look at how some AWS customers are using real-time analytics to capture windows of opportunity: a telco with a major promotion, an advertising retargeter with global demands, and a personal IoT provider with a lifestyle solution. Database queries must be We dig deeper into their architecture and look for common patterns that can be used to build a real-time analytics platform in a cost-optimized way. Database journal We even see how a light-load, real-time analytics system can be built for less than $1000.