Course Information
Schedules
- 10 Apr 2025 (Thu)
Registration period
10 Mar 2025 (Mon) - 9 Apr 2025 (Wed)
Price
HKD 3,000
(Early Bird HK3000)
(Early Bird HK3000)
Course Level
Study Mode
Duration
1 Day(s)
Language
Cantonese
Location
2/F, Centre Point, 181 Gloucester Road, Wanchai, Hong Kong
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Course Overview
Course description
In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade
Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with
open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and
AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing
components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both
analytics and machine learning workloads. You will also learn to apply security, performance, and cost
management best practices to the operation of Amazon EMR.
• Course level: Intermediate
• Duration: 1 day
Activities
This course includes presentations, interactive demos, practice labs, discussions, and class exercises
For further details of this course, please contact us at 2864 2490 or email to education@kenfil.com.
What You’ll Learn
Course objectives
In this course, you will learn to:
• Compare the features and benefits of data warehouses, data lakes, and modern data architectures
• Design and implement a batch data analytics solution
• Identify and apply appropriate techniques, including compression, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store data
• Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a
particular business use case
• Understand how data storage and processing affect the analysis and visualization mechanisms
needed to gain actionable business insights
• Secure data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices
Skills covered in this course
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