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 training can be carried out locally on customer premises in Kazakhstan or in NobleProg corporate training centers in Kazakhstan.
Data Warehouse is also known as Enterprise Data Warehouse (EDW) or Data Warehousing.
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Testimonials
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
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
EDW Subcategories in Kazakhstan
DWH (Data Warehouse) Course Outlines in Kazakhstan
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
Apache Kylin is an extreme, distributed analytics engine for big data.
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
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
- Big data engineers
- Big Data analysts
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
The course is part of the Data Scientist skill set (Domain: Data and Technology).
21 hours
Apache Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo.
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.
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
28 hours
MemSQL is an in-memory, distributed, SQL database management system for cloud and on-premises. It's a real-time data warehouse that immediately delivers insights from live and historical data.
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
- Developers
- Administrators
- Operation Engineers
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Amazon Redshift is a petabyte-scale cloud-based data warehouse service in AWS.
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
- Developers
- IT Professionals
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
21 hours
This instructor-led, live training in Kazakhstan (online or onsite) is aimed at developers who wish to set up a multi-node Greenplum database.
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.
14 hours
This instructor-led, live training in Kazakhstan (online or onsite) is aimed at administrators who wish to set up Greenplum Database for business intelligence and data warehousing solutions.
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.
28 hours
In this instructor-led, live training in Kazakhstan, participants will learn how to build a Data Vault.
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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