Course Overview
The Starburst Essentials training program is a comprehensive 5-day course designed to equip data
professionals with the skills to query, manage, and optimize distributed data using Starburst, a
fast and scalable SQL query engine designed for running large-scale data queries across distributed
systems. This course is composed to help you master all the essential objectives to effectively use
Starburst in enterprise environments. You will learn foundational concepts, federated querying across
multiple data sources, advanced SQL techniques, data lake optimization, Apache Iceberg table formats,
streaming data integration with Kafka, security and governance, and data mesh architecture. Participants
gain hands-on experience in building scalable data pipelines, implementing role-based access control,
and integrating streaming data. By the end of the training, you will understand how to design and
operate high-performance, enterprise-grade data platforms using Starburst.
Course Objective
• Differentiate components in a Starburst cluster and understand their roles in distributed query execution
• Execute federated queries by joining multiple data sources without moving data from their source
• Leverage SQL functions including transforms, aggregates, and windowing functions for complex analytics
• Apply performance optimization techniques using SQL nuances, approximation strategies, and cost-based optimization
• Construct analytical queries using rollup, cube, and windowing functions for advanced business intelligence
• Use Hive and Iceberg table formats to construct, populate, query, and modify partitioned data lake tables
• Employ file size, format, and hierarchy strategies to improve query performance on large-scale data lakes
• Create and validate role-based access control (RBAC) and attribute-based access control (ABAC) policies
• Build data engineering pipelines with Starburst Galaxy for scalable data processing
• Integrate streaming data from Confluent Kafka into Starburst for real-time analytics
• Design and implement data mesh architecture using Starburst as the federated query engine
• Understand Starburst high availability, scalability, security, and authentication mechanisms
Pre-requisites
• Intermediate experience with SQL (SELECT, WHERE, JOIN, GROUP BY statements)
• Basic understanding of databases and data storage concepts
• Familiarity with data integration tools and techniques
• Basic understanding of distributed systems and big data concepts
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud Platform)
• Basic knowledge of Linux and command-line interface (CLI)
Course Curriculum
• Module 1A: Starburst Overview and Architecture
• Module 1B: Starburst Cluster Components
• Module 1C: Federated Querying Concepts
• Module 1D: SQL Functions and Query Execution
• Module 1E: Performance Optimization Basics
• Module 1F: Windowing Functions and Analytics
• Module 2A: Starburst Features and Architecture
• Module 2B: Data Lake Tables and Storage Separation
• Module 2C: Table Format Strategies (Hive vs Iceberg)
• Module 2D: File Formats and Optimization
• Module 2E: Query Performance Tuning
• Module 2F: Partitioning and Bucketing Strategies
• Module 3A: Apache Iceberg Fundamentals
• Module 3B: Iceberg Table Creation and Management
• Module 3C: Schema Evolution and CDC
• Module 3D: Snapshots and Compaction
• Module 3E: Advanced Iceberg Features
• Module 3F: Comparing Hive, Iceberg, and Delta Lake
• Module 4A: Cost-Based Optimizer (CBO)
• Module 4B: Query Plan Analysis and EXPLAIN
• Module 4C: Performance Tuning Techniques
• Module 4D: Parallel Processing Strategies
• Module 4E: Statistics and Query Optimization
• Module 4F: Identifying and Resolving Bottlenecks
• Module 5A: Starburst Security Architecture
• Module 5B: Authentication Mechanisms
• Module 5C: Role-Based Access Control (RBAC)
• Module 5D: Attribute-Based Access Control (ABAC)
• Module 5E: Data Governance and Compliance
• Module 5F: Audit and Monitoring
• Module 6A: Starburst Galaxy Overview
• Module 6B: High Availability and Scalability
• Module 6C: Cluster Configuration and Management
• Module 6D: Worker Node Scaling and Failover
• Module 6E: Query Execution and Load Balancing
• Module 6F: Best Practices for Enterprise Deployments
• Module 7A: Confluent Kafka Fundamentals
• Module 7B: Kafka Topic and Connector Setup
• Module 7C: Starburst Streaming Ingest Configuration
• Module 7D: Real-time Data Processing
• Module 7E: Schematizing Streaming Data
• Module 7F: Querying Live Data Streams
• Module 8A: Analyzing Complex Bank Payment Data
• Module 8B: ISO 20022 Payment Standards
• Module 8C: XML Data Processing
• Module 8D: Creating Data Products for Analysis
• Module 8E: Tableau Integration
• Module 8F: Building Analytical Dashboards
• Module 9A: Data Mesh Concepts and Principles
• Module 9B: Domain-Oriented Data Ownership
• Module 9C: Data as a Product
• Module 9D: Self-Serve Data Platform Design
• Module 9E: Federated Computational Governance
• Module 9F: Implementing Data Mesh with Starburst
• Lab 1: Starburst Cluster Navigation and Setup
• Lab 2: Federated Querying Across Multiple Sources
• Lab 3: SQL Functions and Query Optimization
• Lab 4: Window Functions for Advanced Analytics
• Lab 5: Iceberg Table Creation and Partitioning
• Lab 6: Performance Tuning and Query Plans
• Lab 7: Implementing Access Control Policies
• Lab 8: Streaming Data Ingest with Kafka
• Lab 9: Building Data Products
• Lab 10: Designing Data Mesh Architecture
