
Online or onsite, instructor-led live Data Vault training courses demonstrate through interactive hands-on practice the fundamentals of Data Vault.
Data Vault 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 Vault training can be carried out locally on customer premises in Malaysia or in NobleProg corporate training centers in Malaysia.
NobleProg -- Your Local Training Provider
Testimonials
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course: Data Vault: Building a Scalable Data Warehouse
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
He knows the subject very well
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Ajay created a very helpful repository, filled with notes on processes and setups. He also goes through each of our virtual machines to make sure we are keeping up to speed, and he guides us when we're not. He is generous and helpful in training newbies like us.
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Instructor provided different ways to setup druid
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Detailed explanation and very approachable trainer
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Lo que más me gustó fue el dominio del tema por parte del trainer, su paciencia y claridad al explicar los conceptos, y especialmente su disposición constante para responder todas las dudas que surgieron. Fue una experiencia de aprendizaje realmente enriquecedora y muy agradable.
Patricio Condado - Oscar García López, SOKODB
Course: Greenplum Database
Data Vault Course Outlines in Malaysia
- 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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