Qlik Sense & Big Data. Strategies for Enterprise Deployments

What you'll learn
ETL of Big Data using partition processing
The use of ODAG and Dynamic Views when working with Big Data
How partitioning works on Databases
ODAG Big Data exploration tool for Big Data tables
The use of control tables in Qlik ETL
Versioning of the data structures in offline data ETL
Parallel extract of the data
Using optimized load for different scenarios of loading the data in the dashboards
Realigning of the data structures to achieve optimized load
Requirements
Students need to have a basic knowledge of Qlik Sense, and should ideally already built a basic Qlik Sense project.
All examples presented in this course are downloadable.
Description
Most of the times when projects fail, they don't fail because a developer didn't know a chart function or a script command in Qlik Sense.They usually fail because the project's ETL strategy is not aligned to the environment in which the project is developed: database connections are too slow, datasets are too big, dashboard refresh time is too long.When datasets grow larger the whole ETL process might need to be redesigned to tackle this.In this training you will complement your Qlik knowledge with ETL strategies on Big Data and the use of ODAG and Dynamic Views.The course is split into three parts:The first part is an introduction on what Big Data is and how Qlik relates to Big Data.In the second part we will create projects from scratch for different scenarios of extraction, transformation and data load. You will learn some complex strategies on both offline data and database data sources, and the main focus would be on partition processing.The last part of the training is an...

Read more

Continue reading...