Amazon Kinesis Data Streams: celebrating a decade of real-time data innovation
Data is a key strategic asset for every organization, and every company is a data business at its core. However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses. Such data silos make it difficult to get unified views of the data in an organization and act in real time to derive the most value.
Ten years ago, we launched Amazon Kinesis Data Streams, the first cloud-native serverless streaming data service, to serve as the backbone for companies, to move data across system boundaries, breaking data silos. With data streaming, you can power data lakes running on Amazon Simple Storage Service (Amazon S3), enrich customer experiences via personalization, improve operational efficiency with predictive maintenance of machinery in your factories, and achieve better insights with more accurate machine learning (ML) models. Amazon Kinesis Data Streams is a foundational data strategy pillar for tens of thousands of customers. As streams of raw data come together, they unlock capabilities to continuously transform, enrich, and query data in real time via seamless integration with stream processing engines such as Amazon Managed Service for Apache Flink.
As an example, the National Hockey League (NHL) reimagined the fan experience by streaming live NHL EDGE game data and stats to offer hockey fans valuable insights to keep fans at the edge of their seats. NHL EDGE technology in the puck and players’ sweaters (jerseys) generate thousands of data points every second for the NHL, which can be analyzed by AWS to predict likely outcomes for key events like face-offs. To process and analyze thousands of signals, the NHL built a real-time streaming data foundation with Kinesis Data Streams and Amazon Managed Service for Apache Flink to stream, prepare, and feed data into ML models, helping inform face-off predictions in seconds and expanding new ways to engage viewers.
Building on such streaming data foundations, many customers are currently thinking about how to deliver transformative new products and services with generative AI. Streaming allows companies to connect the data available within data stores to large language models (LLMs) securely and in real time. Although LLMs are capable of working with billions of parameters, in order to deliver an engaging experience that is tailored to a company’s customers, LLMs require personalization data for the company’s users and proprietary knowledge stores within the company’s data stores. A data strategy that incorporates streaming is necessary to deliver personalization and proprietary data that is available for querying in real time.
Customers with real-time streaming data strategy are at the cutting edge of providing innovative products with generative AI. One customer adopted Kinesis Data Streams for their data strategy, and they stream billions of events from their digital products to derive real-time insights. With a combination of low-latency data streaming and analytics, they are able to understand and personalize the user experience via a seamlessly integrated, self-reliant system for experimentation and automated feedback. Earlier this year, building on their already strong data foundation, they launched an innovative digital media generative AI product. The same data foundation built on Kinesis Data Streams is used to continuously analyze how users interact with the generated content and helps the product team fine-tune the application.
“Real-time streaming data technologies are essential for digital transformation. These services help customers bring data to their applications and models, making them smarter. Real-time data gives companies an advantage in data-driven decisions, predictions, and insights by using the data at the very moment it is generated, providing an unparalleled edge in a world where timing is the key to success. Bring the data in once, use it across your organization, and act before the value of that data diminishes.”
– Mindy Ferguson, VP of AWS Streaming and Messaging.
As we celebrate the tenth anniversary of Kinesis Data Streams, customers have shared four key reasons they continue to value this revolutionary service. They love how they can easily stream data with no underlying servers to provision or manage, operate at a massive scale with consistent performance, achieve high resiliency and durability, and benefit from broad integration with myriad sources and sinks to ingest and process data respectively.
Ease of use
Getting started with Kinesis Data Streams is straightforward: developers can create a data stream with a few clicks on the Kinesis Data Streams console or with a single API call. Changing the size or configuration is also a single API call, and each data stream comes with a default 24-hour data retention period. Developers don’t have to worry about clusters, version upgrades, or storage capacity planning. They just turn on a data stream and start ingesting data.
The needs of our customers have evolved in the past 10 years. As more events get captured and streamed, customers want their data streams to scale elastically without any operational overhead. In response, we launched On-Demand streams in 2021 to provide a simple and automatic scaling experience. With On-Demand streams, you let the service handle scaling up a stream’s capacity proactively, and you’re only charged for the actual data ingested, retrieved, and stored. As our customers continued to ask for more capabilities, we increased the ingestion throughput limit of each On-Demand stream from 200MB/s to 1GB/s in March 2023, and then to 2GB/s in October 2023, to accommodate higher throughput workloads. To continue innovating to be the easiest streaming data service to use, we actively listen to our customer use cases.
Canva is an online design and visual communication platform. As it has rapidly grown from 30 million to 135 million monthly users, it has built a streaming data platform at scale that is effortless to operate for driving product innovations and personalizing the user experience.
“Amazon Kinesis Data Streams and AWS Lambda are used throughout Canva’s logging platform, ingesting and processing over 60 billion log events per day. The combination of Kinesis Data Streams and Lambda has abstracted plenty of work that’s often required in managing a massive data pipeline, such as deploying and managing a fleet of servers, whilst also providing a highly scalable and reliable service. It has allowed us to focus on delivering a world-class product by building highly requested features rather than spending time on operational work.”
– Phoebe Zhou, Software Engineer at Canva.
Operate at massive scale with consistent performance
A fundamental requirement of a streaming data strategy is ingesting and processing large volumes of data with low latency. Kinesis Data Streams processes trillions of records per day across tens of thousands of customers. Customers run more than 3.5 million unique streams and process over 45 PB of data per day. Our largest customers ingest more than 15 GB per second of real-time data with individual streams. That’s equivalent to streaming multiple data points for every person on earth, every second! Even at this scale, all our customers still retrieve data within milliseconds of availability.
Customers also want to process the same data with multiple applications, with each deriving a different value, without worrying about one application impacting the read throughput of another. Enhanced Fan-out offers dedicated read throughput and low latency for each data consumer. This has enabled enterprise platform teams to provide real-time data to more teams and applications.
VMware Carbon Black uses Kinesis Data Streams to ingest petabytes of data every day to secure millions of customer endpoints. The team focuses on its expertise while AWS manages data streaming to meet growing customer traffic and needs in real time.
“When an individual customer’s 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. This is why Kinesis Data Streams is a good fit. The biggest advantage is the managed nature of our solution on AWS. This has shaped our architecture and helped us shift complexity elsewhere.”
– Stoyan Dimkov, Staff Engineer and Software Architect at VMware Carbon Black.
Learn more about the case study.
Provide resiliency and durability for data streaming
With burgeoning data, customers want more flexibility in processing and reprocessing data. For example, if an application that is consuming data goes offline for a period, teams want to ensure that they resume processing at a later time without data loss. Kinesis Data Streams provides a default 24-hour retention period, enabling you to select a specific timestamp from which to start processing records. With the extended retention feature, you can configure the data retention period to be up to 7 days.
Some industries like financial services and healthcare have stricter compliance requirements, so customers asked for even longer data retention periods to support these requirements. Therefore, we followed up with long-term storage that supports data retention for up to 1 year. Now, thousands of Kinesis Data Streams customers use these features to make their streaming applications more resilient and durable.
Mercado Libre, a leading ecommerce and payments platform in Latin America, relies on Kinesis Data Streams to power its streaming data strategy around payment processing, customer experience, and operations.
“With Amazon Kinesis Data Streams at the core, we process approximately 70 billion daily messages distributed across thousands of data producers. By leveraging Kinesis Data Streams and Amazon DynamoDB Streams, we’ve embraced an event-driven architecture and are able to swiftly respond to data changes.”
– Joaquin Fernandez, Senior Software Expert at Mercado Libre.
Access your data no matter where it lives
Our customers use a wide variety of tools and applications, and an organization’s data often resides in many places. Therefore, the ability to easily integrate data across an organization is crucial to derive timely insights. Developers use the Kinesis Producer Library, Kinesis Client Library, and AWS SDK to quickly build custom data producer and data consumer applications. Customers have expanded their data producers ranging from microservices to smart TVs and even cars. We have over 40 integrations with AWS services and third-party applications like Adobe Experience Platform and Databricks. As detailed in our whitepaper on building a modern data streaming architecture on AWS, Kinesis Data Streams serves as the backbone to serverless and real-time use cases such as personalization, real-time insights, Internet of Things (IoT), and event-driven architecture. Our recent integration with Amazon Redshift enables you to ingest hundreds of megabytes of data from Kinesis Data Streams into data warehouses in seconds. To learn more about how to use this integration to detect fraud in near-real time, refer to Near-real-time fraud detection using Amazon Redshift Streaming Ingestion with Amazon Kinesis Data Streams and Amazon Redshift ML.
Another integration launched in 2023 is with Amazon Monitron to power predictive maintenance management. You can now stream measurement data and the corresponding inference results to Kinesis Data Streams, coordinate predictive maintenance, and build an IoT data lake. For more details, refer to Generate actionable insights for predictive maintenance management with Amazon Monitron and Amazon Kinesis.
Next, let’s go back to the NHL use case where they combine IoT, data streaming, and machine learning.
The NHL Edge IQ powered by AWS is helping bring fans closer to the action with advanced analytics and new ML stats such as Face-off Probability and Opportunity Analysis.
“We use Amazon Kinesis Data Streams to process NHL EDGE data on puck and Player positions, face-off location, and the current game situation to decouple data producers from consuming applications. Amazon Managed Service for Apache Flink is used to run Flink applications and consumes data from Kinesis Data Streams to call the prediction model in Amazon SageMaker to deliver the real-time Face-off Probability metric. The probability results are also stored in Amazon S3 to continuously retrain the model in SageMaker. The success of this project led us to build the next metric, Opportunity Analysis, which delivers over 25 insights into the quality of the scoring opportunity presented by each shot on goal. Kinesis Data Streams and Amazon Managed Service for Apache Flink applications were critical to making live, in-game predictions, enabling the system to perform opportunity analysis calculations for up to 16 live NHL games simultaneously.”
– Eric Schneider, SVP, Software Engineering at National Hockey League.
Learn more about the case study.
The future of data is real time
The fusion of real-time data streaming and generative AI promises to be the cornerstone of our digitally connected world. Generative AI, empowered by a constant influx of real-time information from IoT devices, sensors, social media, and beyond, is becoming ubiquitous. From autonomous vehicles navigating dynamically changing traffic conditions to smart cities optimizing energy consumption based on real-time demand, the combination of AI and real-time data will underpin efficiency and innovation across industries. Ubiquitous, adaptive, and deeply integrated into our lives, these AI-driven applications will enhance convenience and address critical challenges such as climate change, healthcare, and disaster response by using the wealth of real-time insights at their disposal. With Kinesis Data Streams, organizations can build a solid data foundation, positioning you to quickly adopt new technologies and unlock new opportunities sooner—which we anticipate will be enormous.
Learn more about what our customers are doing with data streaming. If you would like a quick exploration of Kinesis Data Streams concepts and use cases, check out our Amazon Kinesis Data Streams 101 playlist. To get started with building your data streams, visit the Amazon Kinesis Data Streams Developer Guide.
About the author
Roy (KDS) Wang is a Senior Product Manager with Amazon Kinesis Data Streams. He is passionate about learning from and collaborating with customers to help organizations run faster and smarter. Outside of work, Roy strives to be a good dad to his new son and builds plastic model kits.