Apache Kylin: Real-Time OLAP on Big Data Training Course

Apache Kylin is a distributed analytics engine designed to provide OLAP (Online Analytical Processing) capabilities on big data. With the release of version 5.0, Kylin introduces real-time streaming support, enabling sub-second query responses on streaming data sources like Kafka. The platform offers a powerful SQL interface, seamless integration with BI tools, and a redesigned web UI for intuitive modeling and analysis.

This instructor-led, live training (online or onsite) is aimed at intermediate-level big data professionals who wish to utilize Apache Kylin for building real-time data warehouses and performing multidimensional analysis on large-scale datasets.

By the end of this training, participants will be able to:

Set up and configure Apache Kylin with real-time streaming data sources.
Design and build OLAP cubes for both batch and streaming data.
Perform complex queries with sub-second latency using Kylin's SQL interface.
Integrate Kylin with BI tools for interactive data visualization.
Optimize performance and manage resources effectively in Kylin.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Thank you for sending your enquiry! One of our team members will contact you shortly.

Thank you for sending your booking! One of our team members will contact you shortly.

Course Outline

Introduction to Apache Kylin

Overview of OLAP and its significance in big data analytics
Evolution of Apache Kylin and its architecture
Key features and capabilities of Kylin 50

Setting Up Apache Kylin

Installation prerequisites and environment setup
Configuring Kylin with Hadoop, Spark, and Kafka
Understanding Kylin's web UI and command-line tools

Data Modeling in Kylin

Designing star and snowflake schemas for OLAP cubes
Defining dimensions and measures
Creating and managing data models in Kylin's web UI

Building and Managing Cubes

Cube building process and job management
Incremental builds and auto-merge strategies
Monitoring cube health and performance

Real-Time Streaming with Kylin

Integrating Kafka as a streaming data source
Setting up real-time cubes and fusion models
Achieving low-latency analytics with streaming data

Querying and Analysis

Executing SQL queries using Kylin's query interface
Connecting BI tools (eg, Tableau, Power BI) to Kylin
Performing multidimensional analysis and drill-downs

Performance Optimization

Best practices for cube design and aggregation
Resource management and tuning for scalability
Troubleshooting common performance issues

Advanced Topics

Security and access control in Kylin
Extending Kylin with custom plugins and integrations
Exploring Kylin's REST APIs for automation

Summary and Next Steps

Requirements

An understanding of Hadoop and big data ecosystems
Familiarity with SQL and data warehousing concepts
Basic knowledge of streaming data platforms like Kafka

Audience

Big data engineers seeking to implement real-time analytics solutions
Data analysts aiming to leverage OLAP capabilities on large datasets
Data warehouse architects interested in modernizing their infrastructure

14 Hours

Need help picking the right course?

Testimonials (5)

Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.

Ian - Archeoworks Inc.

Course - ArcGIS Fundamentals

All the topics which he covered including examples. And also explained how they are helpful in our daily job.

madduri madduri - Boskalis Singapore Pte Ltd

Course - QGIS for Geographic Information System

I liked Pablo's style, the fact that he covered a lot of subjects from report design , customization with html to implementing simple ML algortithms. Good balance theoretical information / exercices. Pablo really covered all topics i was interested in and gave comprehensive answers to my questions.