
Online or onsite, instructor-led live Data Warehouse training courses demonstrate through discussion and hands-on practice how to understand, plan and set up a Data Warehouse.
Data Warehouse training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Data Warehouse trainings in Hong Kong can be carried out locally on customer premises or in NobleProg corporate training centers.
Data Warehouse is also known as Enterprise Data Warehouse (EDW) or Data Warehousing.
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Testimonials
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
Example exercises; Practical work experience sharing
澳新银行
Course: Data Vault: Building a Scalable Data Warehouse
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Cube and DV
Alan Xie
Course: Data Vault: Building a Scalable Data Warehouse
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The teacher's knowledge of the data warehouse is comprehensive, and he praises it!
澳新银行
Course: Data Vault: Building a Scalable Data Warehouse
Machine Translated
The teacher explained in detail and discussed the atmosphere
澳新银行
Course: Data Vault: Building a Scalable Data Warehouse
Machine Translated
Practical application, help in explaining many different doubts
SGB-Bank S.A.
Course: Data Vault: Building a Scalable Data Warehouse
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Data Warehouse Subcategories in Hong Kong
Data Warehouse (DWH) Course Outlines in Hong Kong
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
In this instructor-led, live training, participants will learn the essentials of MemSQL for development and administration.
By the end of this training, participants will be able to:
- Understand the key concepts and characteristics of MemSQL
- Install, design, maintain, and operate MemSQL
- Optimize schemas in MemSQL
- Improve queries in MemSQL
- Benchmark performance in MemSQL
- Build real-time data applications using MemSQL
Audience
- Developers
- Administrators
- Operation Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of Amazon Redshift.
By the end of this training, participants will be able to:
- Install and configure Amazon Redshift
- Load, configure, deploy, query, and visualize data with Amazon Redshift
Audience
- Developers
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
By the end of this training, participants will be able to:
- Install and configure Pivotal Greenplum.
- Model data in accordance to current needs and future expansion plans.
- Carry out different techniques for distribute data across multiple nodes.
- Improve database performance through tuning.
- Monitor and troubleshoot a Greenplum database.
By the end of this training, participants will be able to:
- Address processing needs with Greenplum.
- Perform ETL operations for data processing.
- Leverage existing query processing infrastructures.
By the end of this training, participants will be able to:
- Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
- Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
- Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
- Build and deploy highly scalable and repeatable warehouses.
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