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试用DataCamp for Business课程描述
In this course, you’ll learn how to leverage these powerful technologies by helping a fictional data engineer named Cody. Your goal is to help her to collect real-time streaming data from city-owned vehicles, analyze the data, and send relevant alerts like speed warnings to drivers. Using Amazon Kinesis and Firehose, you’ll learn how to ingest data from millions of sources before using Kinesis Analytics to analyze data as it moves through the stream. You’ll also spin up serverless functions in AWS Lambda that will conditionally trigger actions based on the data received. By the end of this training you’ll know how to create live ElasticSearch dashboards with AWS QuickSight and CloudWatch—and hopefully helped Cody complete her ambitious project.
先决条件
Introduction to AWS Boto in PythonIntroduction to ShellStreaming Concepts1
Streaming in the cloud
In this first chapter, you will learn about the differences between batch and stream processing, create your first stream, manage its permissions, write to it and read from it.
2
Going serverless
The next step in your streaming data journey is learning how to use transformational lambda functions to go serverless. Through hands-on exercises, you will add lambda layers and trigger lambda functions on specific conditions.
3
Analyzing streaming data
You're now ready to encode and decode streaming data and analyze data directly in the stream. You will even use multiple streams to get daily vehicle top speeds.
4
Monitoring and visualizing streaming data
In this final chapter, you will discover how to monitor your stream's performance using logs, metrics, alarms and dashboards. You will use Elasticsearch and build your own Kibana dashboard.
Streaming Data with AWS Kinesis and Lambda
课程完成 通过 DataCamp for Mobile 提升您的数据技能
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。