AWS-Online-Tech-Talks
In "Analytics in 15: Serverless Data Streaming Workloads with Amazon Kinesis", Nihar Sheth, a product manager at Amazon Kinesis, explains the importance of real-time analytics and the challenges of implementing open-source frameworks for streaming workloads. He breaks down the five key components of streaming architecture and highlights three Amazon Kinesis services that simplify the process of building serverless streaming workflows. The video also provides a use case on how Amazon Kinesis Data Streaming Services can be used by a medical device company with millions of IoT devices. The video concludes by encouraging viewers to check out Kinesis Data Streaming Services references and apply this knowledge to their own use cases.
In this section, Nihar Sheth, a product manager with Amazon Kinesis describes what is meant by real-time analytics and why organizations need this capability to process high volume and velocity data continually generated by various data sources. He explains that with advancements in technology, data is coming at companies at an exponential rate, and they need to make quick decisions to remain competitive. Real-time analytics helps companies detect security breaches, network outages, potential fraudulent credit card transactions and predicts failures in manufacturing plants. Nihar then goes on to break down five key components that streaming architecture has: data sources, streaming ingestion layer, streaming storage layer, stream processing layer, and data storage layer, each of which performs a specific function that ultimately contributes to generating real-time insights.
In this section, the speaker discusses the challenges that customers face when implementing open-source frameworks for streaming workloads and highlights three Amazon Kinesis services that make it easy to build serverless streaming workflows. The first service, Amazon Kinesis Data Streams, offers easy setup, serverless scaling, and consistent performance at scale. The second service, Kinesis Data Firehose, focuses on no-code data transformation and data delivery to various destinations without the customer having to write any new code. The third service discussed is Kinesis Data Analytics, which provides a fully managed runtime environment for customers to build and deploy Flink stream processing jobs.
In this section, the video discusses how Amazon Kinesis Data Streaming Services can be used to address the use cases of a medical device company that has millions of IoT devices generating billions of events and messages. The first use case is to create a real-time dashboard where the device company can view battery lives and patient health trends. The second use case is to react to real-time health events and send notifications to healthcare providers. Lastly, the third use case involves long-term storage for historical analysis that includes building machine learning models. The video suggests using Kinesis Data Analytics and Firehose to read data from Kinesis Data Streams and deliver it to services like Amazon OpenSearch and S3 for analysis and querying.
In this final excerpt of the video, the speaker thanks the audience for their time and encourages them to check out the Kinesis Data Streaming Services references provided to learn more about the topic. The speaker hopes that the audience has learned something about Amazon's Kinesis and can apply that knowledge to their own use cases. The video ends with background music playing.
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