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[Special courses] Building a Serverless Data Lake

  • Lecturer

    Agilewing

  • Course Level

    Solution-based Training

  • ¥2,100.00

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  • Course description
  • Syllabus
  • Building a Serverless Data Lake is a one-day advanced training camp designed to teach you how to use AWS services to design, build, and operate a serverless data lake solution. This training camp covers the following topics: Large-scale data retrieval from any data source; secure and persistent data storage; the ability to use the right tools to process massive amounts of data; and understanding of the options available for analyzing data in near real time.
    • Course targetsBy taking this course, you will be able to:  Use services such as Kinesis Streams and Firehose to collect large amounts of data and store it securely and persistently in Simple Storage Service.  Create metadata indexes for data lakes.  Select the best tools to acquire, store, process, and analyze data in the data lake.  Apply what you have learned to hands-on experiments and gain practical experience by building complete solutions.
    • Target populationThis course applies to: Solution Architect  Big Data Developer  Data architects and analysts  Other practitioners of experimental data analysis
    • prerequisitesWe recommend that those attending this course meet the following prerequisites:  Excellent work experience with AWS core services, including Elastic Compute Cloud (EC2) and Simple Storage Service (S3)  Working experience in a programming or scripting language  Familiar with Linux operating system and command line interface  Requires laptop to complete lab exercises-Tablet is not recommended The way to teachThis course will be taught in combination with:  Instructor-led training (ILT)  Hands-on experiments
    • Course OutlineThis course covers the following concepts: Key services that help enable serverless data lake architecture Data analysis solutions that follow the acquisition, storage, processing and analysis workflow  Repeatable template deployment for implementing a data lake solution  Build metadata indexes and enable search  Establish a large-scale data extraction pipeline using multiple data sources  Transform data using simple functions triggered by events  Data processing using tools and services best suited for the case Options that can be used to better analyze processed data  Best practices for deployment and operations

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[Special courses] Building a Serverless Data Lake

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