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AWS Kinesis – A streaming platform of AWS Infrastructure

AWS Kinesis - A streaming platform of AWS Infrastructure
AWS Kinesis - A streaming platform of AWS Infrastructure

AWS Kinesis – A streaming platform of AWS Infrastructure

Learn AWS Kinesis – A data streaming application within AWS Infrastructure

What you’ll learn

AWS Kinesis – A streaming platform of AWS Infrastructure

  • AWS Kinesis
  • The streaming platform of AWS Infrastructure
  • Understand Datastream, Deliverystream, and Firehose
  • Stream data into S3 and Redshift
  • Monitoring the data pipelines
  • Understand the process of producers and consumers

Requirements

  • Basic knowledge of the streaming process is required, but not mandatory

Description

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and responds instantly instead of having to wait until all your data is collected before the processing can begin.

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Kinesis Data Streams is part of the Kinesis streaming data platform, along with Kinesis Data Firehose, Kinesis Video Streams, and Kinesis Data Analytics.

You can use Kinesis Data Streams for rapid and continuous data intake and aggregation. The type of data used can include IT infrastructure log data, application logs, social media, market data feeds, and web clickstream data. Because the response time for the data intake and processing is in real time, the processing is typically lightweight.

Multiple Kinesis Data Streams applications can consume data from a stream, so that multiple actions, like archiving and processing, can take place concurrently and independently. For example, two applications can read data from the same stream. The first application calculates running aggregates and updates an Amazon DynamoDB table, and the second application compresses and archives data to a data store like Amazon Simple Storage Service (Amazon S3). The DynamoDB table with running aggregates is then read by a dashboard for up-to-the-minute reports.

Who this course is for:

  • ETL Architects
  • ETL Developers
  • ETL Specialists
  • Data Integration Specialists
  • Data Engineers

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