Apache Spark vs Cloud-Native SQL Engines

Overview

Opinionated comparison of Apache Spark and cloud-native SQL engines, exploring their strengths, use cases, and complementary nature for data processing and business intelligence tasks.

Syllabus

• Introduction to Data Processing and BI Tools
Overview of Data Processing Architectures
Business Intelligence and Analytical Workflows
• Introduction to Apache Spark
Architecture and Ecosystem
Core Components and Functionalities
Strengths in Data Processing
• Overview of Cloud-Native SQL Engines
Key Players (e.g., BigQuery, Snowflake, Redshift)
Architecture and Query Execution
Strengths in Data Processing
• Detailed Comparison: Apache Spark vs Cloud-Native SQL Engines
Performance and Scalability
Cost Efficiency
Flexibility and Integration
Ease of Use and Accessibility
• Use Cases and Best Practices
Ideal Scenarios for Using Apache Spark
Optimal Conditions for Cloud-Native SQL Engines
How to Choose the Right Tool
• Complementary Use Cases
Hybrid Workflows: Leveraging Both Systems
Data Lake and Data Warehouse Synergy
Real-world Examples and Case Studies
• Future Trends and Emerging Technologies
Evolution of Data Processing Tools
Impact of AI and Machine Learning Trends
Innovations in Cloud-Native Solutions
• Course Summary and Conclusions
Key Takeaways
Final Thoughts on Tool Selection and Adoption
• Practical Exercises and Hands-On Labs
Setting Up and Running a Spark Application
Executing Queries in Cloud-Native SQL Engines
Comparing Performance and Results

Subjects

Conference Talks