# Databricks and Advanced Spark Day2 Lecture

> In this lesson, Zach focuses on the five key reasons why Spark jobs can be slow, including data model bottlenecks and job misconfigurations. He emphasizes the importance of processing only the necessary data, advocating for incremental refresh strategies over full dataset refreshes, which can lead…

- Web page: https://www.dataexpert.io/lesson/databricksandadvancedsparkday2lecture-feb26-p736-l2027
- Program: [DataExpert](https://www.dataexpert.io/program/data-expert)
- Module: Week 2: Databricks & Advanced Spark
- Access: Requires enrollment in DataExpert
- Length: 52 min video
- Skills: Data Modeling, ETL/ELT, Apache Spark, Databricks
- Academy: DataExpert.io Academy

## About this lesson

In this lesson, Zach focuses on the five key reasons why Spark jobs can be slow, including data model bottlenecks and job misconfigurations. He emphasizes the importance of processing only the necessary data, advocating for incremental refresh strategies over full dataset refreshes, which can lead to significant performance improvements. Additionally, Zach discusses the impact of source file formats and the need for proper configurations to avoid congestion and misconfigurations.

## Navigation

- Previous lesson: [Databricks and Advanced Spark Day1 Lab](https://www.dataexpert.io/lesson/databricksandadvancedsparkday1lab-feb26-p736-l2026.md)
- Next lesson: [Databricks and Advanced Spark Day2 Lab](https://www.dataexpert.io/lesson/databricksandadvancedsparkday2lab-feb26-p736-l2028.md)