# Spark Batch Processing - Caching, UDFs, DataFrames, Datasets, SparkSQL, and Parquet (Day 2 Lecture) 

> In this lecture, we dive deeper into Spark, focusing on optimization with caching, temporary views, UDFs, DataFrame vs. Dataset vs. SparkSQL, Parquet, and tuning considerations.

- Web page: https://julie.techexpert.io/lesson/spark-batch-day-2-lecture-v2
- Program: [Data Engineering Boot Camp V2 Combined Track](https://julie.techexpert.io/program/data-engineering-boot-camp-v2-combined-track)
- Module: Week 4: Batch Pipelines with Apache Spark V2
- Access: Requires enrollment in Data Engineering Boot Camp V2 Combined Track
- Length: 54 min video
- Skills: SQL, Data Modeling, ETL/ELT, Apache Spark
- Academy: Julies academy to catch all other programs

## About this lesson

In this lecture, we dive deeper into Spark, focusing on optimization with caching, temporary views, UDFs, DataFrame vs. Dataset vs. SparkSQL, Parquet, and tuning considerations.

## Navigation

- Previous lesson: [Data Impact Communication & Visualization - Creating Executive Summary and Exploratory Dashboards with events.csv Dataset (Day 2 Lab) ](https://julie.techexpert.io/lesson/visual-impact-day-2-lab-v2.md)
- Next lesson: [Spark Batch Processing - Caching, DataFrame, Dataset, SparkSQL, and Bucketing in Iceberg (Day 2 Lab)](https://julie.techexpert.io/lesson/spark-batch-day-2-lab-v2.md)