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As customers modernize their data processing pipelines from batch workloads to real-time processing, they face unique challenges with scale, performance and operations of distributed real-time systems. In this session, learn how Amazon Kinesis services provide managed serverless offerings to handle terabytes of real-time data at low latency and with no operational overhead. See how customers use tens of existing AWS service integrations to source the data or build their custom data collectors to stream data, and how they utilize Amazon Kinesis Data Firehose's no-code ETL capabilities to transform and partition their data and send the data to one of AWS destinations like S3, Redshift, OpenSearch or third-party partners like Splunk and Datadog. Learning Objectives: Objective 1: Learn how to ingest terabytes of data for real-time processing with the serverless offering of Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose. Objective 2: See how to scale, optimize and monitor distributed real-time systems for custom processing at low latency and with no operational overhead. Objective 3: Understand how to load and analyze streaming data in a no-code ETL, serverless environment to transform and partition data for datalakes and 3rd party partners. ***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/kinesis/data-s... Subscribe to AWS Online Tech Talks On AWS: https://www.youtube.com/@AWSOnlineTec... Follow Amazon Web Services: Official Website: https://aws.amazon.com/what-is-aws Twitch: / aws Twitter: / awsdevelopers Facebook: / amazonwebservices Instagram: / amazonwebservices ☁️ AWS Online Tech Talks cover a wide range of topics and expertise levels through technical deep dives, demos, customer examples, and live Q&A with AWS experts. Builders can choose from bite-sized 15-minute sessions, insightful fireside chats, immersive virtual workshops, interactive office hours, or watch on-demand tech talks at your own pace. Join us to fuel your learning journey with AWS. #AWS