Real-time data processing has become essential for monitoring, fraud detection, IoT telemetry, and personalized recommendations. Amazon Kinesis provides a scalable and fully managed solution for ingesting streaming data at scale, while Amazon S3 table buckets (Apache Iceberg) enable efficient querying and analytics on structured datasets.
In this Cloud Lab, you’ll set up a Kinesis Data Stream as the entry point for real-time events. You will then configure a Kinesis Data Firehose delivery stream to capture, process, and store this data into an S3 table bucket. You’ll simulate real-time events being sent into the pipeline using a Python-based data generator script. Finally, you’ll use Amazon Athena to query and analyze the data stored in Iceberg tables.
After completing this Cloud Lab, you’ll have the skills to design and implement a serverless streaming data pipeline on AWS, build real-time ingestion workflows, store data in queryable formats, and use Athena for analytics. These are valuable skills for data engineering, analytics, and cloud-based big data systems careers.
The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
