Data Warehouse Opplæringskurs

The course is part of the Data Scientist skill set (Domain: Data and Technology).

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.

Format of the Course

- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

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

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

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

Audience

- Developers
- Administrators
- Operation Engineers

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

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

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.

This instructor-led, live training in Norge (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.

This instructor-led, live training in Norge (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.

In this instructor-led, live training in Norge, 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.