This training focuses on Kylin architecture, cube design, data modeling, query optimization, and integration with Hadoop ecosystem components such as Hive, HDFS, and Spark.
Overview
Apache Kylin Training is a practical, hands-on program designed to equip learners with the skills required to build and manage OLAP (Online Analytical Processing) cubes for large-scale big data analytics. This training focuses on Kylin architecture, cube design, data modeling, query optimization, and integration with Hadoop ecosystem components such as Hive, HDFS, and Spark. Participants will gain real-world experience in enabling ultra-fast analytical queries on massive datasets using pre-computation techniques and distributed processing.
Learning Outcomes
Participants will gain strong practical skills in Apache Kylin, enabling them to build high-performance OLAP solutions for big data analytics and enterprise reporting systems.
Duration & Delivery Mode
16 hours
Target Audience
• Data Engineers and BI Developers
• Data Warehouse Professionals
• Big Data Analysts and Architects
• Hadoop Developers and Administrators
• IT Professionals working on analytics platforms
Pre-requisites
• Basic understanding of SQL and relational databases
• Familiarity with Hadoop ecosystem concepts (Hive, HDFS basics)
• Basic knowledge of data warehousing concepts
• Analytical thinking and data interpretation skills
Skillset Achieved
• Apache Kylin architecture and OLAP cube concepts
• Cube design and dimensional modeling
• Data ingestion from Hadoop and Hive sources
• Query optimization and performance tuning
• Pre-computation techniques for large-scale analytics
• Integration with BI tools and SQL interfaces
• Managing and monitoring Kylin environments
Course Outcome
Upon completion of this training, participants will be able to design and implement OLAP cubes using Apache Kylin for fast analytical querying. They will be capable of optimizing big data analytics workloads and enabling high-performance reporting on large-scale datasets.
Course Outline
Introduction to Apache Kylin and OLAP Concepts
• Overview of OLAP and big data analytics
• Apache Kylin architecture and components
• Cube fundamentals and star schema concepts
• Setting up Kylin environment
Data Modeling and Cube Design
• Dimensional modeling for OLAP cubes
• Fact and dimension table design
• Cube creation and configuration
• Data source integration (Hive/HDFS)
Query Processing and Optimization
• How Kylin accelerates SQL queries
• Indexing and pre-computation strategies
• Query optimization techniques
• Performance tuning for large datasets
Integration and Advanced Features
• Integration with BI tools and dashboards
• Cube maintenance and monitoring
• Handling large-scale distributed data
• Best practices for production environments
Assessment Topics
• Kylin architecture and OLAP fundamentals
• Cube design and dimensional modeling
• Data ingestion and integration
• Query optimization and performance tuning
• Pre-computation techniques
• BI integration and reporting
Evaluation
• Hands-on cube creation exercises
• SQL query optimization tasks
• Practical data modeling assignments
• Mini project on OLAP cube implementation
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants who successfully complete the training will receive an AcadNXT Certification in Apache Kylin Training, validating their expertise in OLAP cube design, big data analytics, query optimization, and enterprise BI acceleration using Apache Kylin.
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What Our Students Say
“The Kylin training was very practical and helped me understand OLAP cube concepts clearly.”
“Excellent hands-on sessions covering cube design and query optimization.”
“The course made large-scale analytics and pre-computation concepts easy to understand.”
“Very structured training with strong focus on real-world BI acceleration use cases.”
“This course gave me strong confidence in building OLAP-based analytics systems.”