Learn how ByteDance optimizes SparkSQL to reduce compute costs in this 33-minute technical talk presented by Senior R&D Engineer Gan Hongnan. Explore practical strategies and techniques implemented at ByteDance for improving SparkSQL performance in data lake and data warehouse environments, with detailed insights into cost optimization approaches for large-scale data processing.
Optimization for Reducing Compute Cost of SparkSQL in ByteDance
Overview
Syllabus
Optimization for Reducing Compute Cost of SparkSQL in ByteDance
Taught by
The ASF
Related Courses
-
Exploration and Practice of Flink SQL Performance Optimization at ByteDance
-
Bytedance's Parquet Format Optimization for Cost Reduction and Efficiency
-
Smooth Migration Practice from MapReduce to Spark at ByteDance
-
Cost Optimization Deep Dive for Google Compute Engine
-
Cost Efficiency Strategies for Managed Apache Spark Service with Databricks
-
Apache Doris - Separation of Storage and Compute Architecture
