Databricks Five Ways To Create Tables | pySpark and Spark SQL
Databricks supports managed and unmanaged tables. Unmanaged tables are also called external tables. This tutorial demonstrates five different ways to create tables in Databricks. It covers:
👉 What's the difference between managed and external tables?
👉 How to mount S3 bucket to Databricks and read CSV to spark dataframe?
👉 How to create a database in Databricks?
👉 How to create a managed table from a spark datafram using pySpark?
👉 How to create a managed table from an existing table using SQL?
👉 How to create a managed table from a spark datafram using SQL?
👉 How to create an unmanaged table from a spark datafram using pySpark?
👉 How to create an unmanaged table from a spark datafram using SQL?
👉 How to delete managed and external tables?
⏰ Timecodes ⏰
0:00 - Intro
1:03 - Step 1: Managed vs. Unmanaged Tables
1:42 - Step 2: Mount S3 Bucket And Read CSV To Spark Dataframe
3:34 - Step 3: Create Database In Databricks
4:36 - Step 4: Create Managed Table From Spark Dataframe Using pySpark (Method 1)
6:05 - Step 5: Create Managed Table From Existing Table Using SQL (Method 2)
6:27 - Step 6: Create Managed Table From Spark Dataframe Using SQL (Method 3)
7:16 - Step 7: Create Unmanaged Table From Spark Dataframe Using pySpark (Method 4)
7:41 - Step 8: Create Unmanaged Table From Spark Dataframe Using SQL (Method 5)
8:35 - Step 9: Delete Managed And Unmanaged Tables
9:31 - Summary
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