Spark ETL with SQL Database (MySQL | PostgreSQL)

In this video, we are discussing about Spark ETL with MySQL and PostgreSQL.

Meduim Blob:
https://medium.com/@developershome/list/spark-etl-elt-ca309fb81acc

Github Repo:
https://github.com/developershomes/SparkETL/tree/main/Chapter1

Blogs Link
https://developershome.blog/category/data-engineering/spark/

YouTube Playlist:
https://www.youtube.com/playlist?list=PLYqhYQOVe-qNwwWJdhiLM_In2l9kDwkAa

we will discuss about Spark ETL pipelines with all of below different types of sources. Detailed Plan:

0. Chapter0 – Spark ETL with Files (JSON | Parquet | CSV | ORC | AVRO)
1. Chapter1 – Spark ETL with SQL Database (MySQL | PostgreSQL)
2. Chapter2 – Spark ETL with NoSQL Database (MongoDB)
3. Chapter3 – Spark ETL with Azure (Blob | ADLS)
4. Chapter4 – Spark ETL with AWS (S3 bucket)
5. Chapter5 – Spark ETL with Hive tables
6. Chapter6 – Spark ETL with APIs
7. Chapter7 – Spark ETL with Lakehouse (Delta)
8. Chapter8 – Spark ETL with Lakehouse (HUDI)
9. Chapter9 – Spark ETL with Lakehouse (Apache Iceberg)
10. Chapter10 – Spark ETL with Lakehouse (Delta vs Iceberg vs HUDI)
11. Chapter11 – Spark ETL with Lakehouse (Delta table Optimization)
12. Chapter12 – Spark ETL with Lakehouse (Apache Kafka)
13. Chapter13 – Spark ETL with GCP (Big Query)
14. Chapter 14 – Spark ETL with Hadoop (Apache Sqoop)