[Special courses] Building a Serverless Data Lake

Training

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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