Vanguard innovated and built a solution for replicating data across Regions in Amazon Kinesis Data Streams to make the data highly available. Also demonstrated is a robust checkpoint strategy to facilitate a Regional failover of the replication process when needed. The solution illustrates how to use DynamoDB global tables for tracking the replication checkpoints and configuration. With this architecture, Vanguard was able to deploy workloads depending on the CDC data to multiple Regions to meet business needs of high availability in the face of service impairments impacting CDC pipelines in the primary Region.
Disney+ is the streaming home of Disney, Pixar, Marvel, Star Wars, National Geographic, and more. From new releases to your favorite classics and exclusive Originals, there's something for everyone.
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. They built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Managed Service for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. These services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability.
Comcast Corporation creates incredible technology and entertainment that connects millions of people to the moments and experiences that matter most. At the core of this is Comcast’s high-speed data network, providing tens of millions of customers across the country with reliable internet connectivity. This mission has become more important now than ever.
Comcast uses Amazon Kinesis Data Streams to build a Streaming Data Platform that centralizes data exchanges. It is foundational to the way their data analysts and data scientists derive real-time insights from the data.
VMware Carbon Black analyzes over a trillion events every day as it strives to protect its customers from malicious online behavior. To ingest data from millions of distributed edge devices efficiently at scale, the team needed to transition from a solution that built workflows for each customer individually to a scalable, less costly alternative. Using Amazon Kinesis Data Streams, VMware adopted a serverless streaming-data strategy that provides real-time analytics in milliseconds. Now they process 1 PB streaming data per day with reduced operating and infrastructure costs.
When individual customers’ data increases or decreases, we can use the elasticity of Amazon Kinesis Data Streams to scale compute up or down to process data reliably while effectively managing our cost.
BT Group is the UK’s leading telecommunications and network provider and a leading provider of global communications services and solutions, serving customers in 180 countries. Its principal activities in the UK include the provision of fixed voice, mobile, broadband, and TV (including Sport), and a range of products and services over converged fixed and mobile networks to consumer, business, and public sector customers.
BT Group easily scaled sophisticated streaming applications for use cases like real-time alerting. BT Group’s technology platform developed a real-time monitoring solution using Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to support the rollout of Digital Voice, a new fixed VoIP service in the UK.
LaunchDarkly isn’t just a leader in feature management — it’s the first scalable feature management platform. Feature management allows development teams to innovate faster by fundamentally transforming how software is delivered to customers. With the ability to gradually release new software features to any segment of users on any platform, DevOps teams can standardize safe releases at scale, accelerate their journey to the cloud and collaborate more effectively with business teams.
LaunchDarkly’s feature management platform helps organizations to safely launch new features to production environments by separating software deployments from feature releases using feature flags. As part of our platform, which also provides monitoring of feature launches, we have an events API that ingests millions of events sent by our SDK and writes them into a database. In our previous architecture, we faced scaling challenges with this API as occasional traffic spikes would overwhelm the database with requests and at times even cause an outage or loss of event data. Therefore, we decided to re-architect our event-driven pipelines leveraging Amazon Kinesis Data Streams for its durability, scalability, and ease-of-use with features such as data replay. Using Kinesis Data Streams as our core data streaming platform, we have scaled up from ingesting approximately 1TB of data a day to more than 100 TBs of data. Now, and in the past year, our events API has responded successfully 99.99 percent of the time and persisted data successfully 99.999999 percent of the time.
At NerdWallet, we’re on a mission to provide clarity for all of life’s financial decisions. Every day, we help people find the right credit cards and mortgage rates, refinance their student loans, track their spending and so much more. We're proud to say that millions of people turn to Nerds for our objective advice, expert info, and helpful tools.
Nerdwallet was looking to build a cohesive and performant experience for their users. For that, they need to be able to use large volumes of varying user data sourced by multiple independent teams. This requires a strong data culture along with a set of data infrastructure and self-serve tooling that enables creativity and collaboration. To support real-time use cases, Amazon Kinesis Data Streams, AWS Lambda, Amazon Kinesis Data Firehose and Amazon Simple Notification Service (Amazon SNS) were added into the architecture. They landed on a serverless stream processing architecture that can scale to thousands of writes per second within minutes of freshness on our data lakes.
Face-off Probability is the National Hockey League’s (NHL) first advanced statistic using machine learning (ML) and artificial intelligence. It uses real-time Player and Puck Tracking (PPT) data to show viewers which player is likely to win a face-off before the puck is dropped, and provides broadcasters and viewers the opportunity to dive deeper into the importance of face-off matches and the differences in player abilities.
The Amazon Machine Learning Solutions Lab partnered with NHL hockey and data experts to work backward from their target goal of enhancing their fan experience. In addition, leveraging Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink, they were able not just to predict the winner of the face-off, but to build a foundation for solving a number of similar problems in a real-time and cost-efficient way.
TrueCar is a large online marketplace for automotive buying and selling in the United States. The team wanted to reduce data latency and drive analytics insights to improve performance and customer personalization. By adopting a streaming-data strategy with Amazon Kinesis Data Streams, TrueCar has improved the speed to insight of clickstream analytics by 48 times, automated its extract, transform, load (ETL) process, and improved observability into its metrics.
Slyp instantly delivers an interactive smart receipt inside a customer’s banking app, improving the post-purchase experience for customers, and unlocking new value for retailers and banks. Slyp’s mission is to turn the end of a transaction into the beginning of a customer relationship. By re-imagining receipts, we’ve unlocked their missed potential and made them fit them into today's smart, eco-conscious world.
We started using Amazon Kinesis Data Streams in December of 2019 for our high volume endpoints because it gives us a fast and highly reliable type of storage that protects our downstream systems, like databases, from extreme spikes in traffic. This also enables us to temporarily turn off the stream consumers and perform heavy maintenance tasks on downstream systems while maintaining 100 percent uptime.
Sean Murphy, Principal Software Engineer, Slyp
Thomson Reuters
Thomson Reuters is a leading source of information—including one of the world’s most trusted news organizations—for the world’s businesses and professionals. It provides companies with the intelligence, technology, and human expertise they need to find trusted answers, enabling them to make better decisions more quickly. Its customers include financial, risk, legal, tax, accounting, and media markets.
Because of the robust failover architecture and the technical capabilities of AWS, we have not lost a single event since we started collecting data.
Hearst Corporation, headquartered in New York City, is one of the largest media and information companies in the world. The company owns 15 daily and 36 weekly newspapers and more than 300 popular magazines worldwide, including Cosmopolitan, Esquire, and O, The Oprah Magazine. Hearst also has ownership interests in 31 television stations and leading cable television networks such as ESPN and A&E Networks. The organization’s diverse portfolio of interests also includes digital distribution and real estate ventures.
I don't know how we could have made our clickstream data pipeline work without Amazon Kinesis services. It would have involved many weeks of engineering. Kinesis Data Streams and Firehose make the entire process extremely simple and reliable.
Amazon Ads helps brands design ad experiences that delight customers and deliver meaningful results. Amazon Ads employed Amazon ElastiCache, Amazon Kinesis Data Streams, and Amazon Simple Queue Service to process billions of impressions every day at ultralow latency. Now, the company’s machine learning models recommend relevant products to customers in 20 markets.
