Data Engineering Vol2 AWS : Data Processing - Spark & Kafka | Udemy [Update 10/2025]
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Genre: eLearning[/center]
Batch & Stream Processing using Spark (PySpark) and Kafka on AWS (EMR & Databricks)
What you'll learn
Deep dive on Spark and Kafka using AWS EMR, Databricks, MSK
Understand Data Engineering (Volume 2) on AWS using Spark and Kafka
Batch and Stream processing using Spark and Kafka
Production level projects and hands-on to help candidates provide on-job-like training
Get access to datasets of size 100 GB - 200 GB and practice using the same
Learn Python for Data Engineering with HANDS-ON (Functions, Arguments, OOP (class, object, self), Modules, Packages, Multithreading, file handling etc.
Learn SQL for Data Engineering with HANDS-ON (Database objects, CASE, Window Functions, CTE, CTAS, MERGE, Materialized View etc.)
AWS Data Analytics services - S3, EMR, Databricks, MSK
This is Volume 2 of Data Engineering course. In this course I will talk about Open Source Data Processing technologies - Spark and Kafka, which are the most used and most popular data processing frameworks for Batch & Stream Processing. In this course you will learn Spark from Level 100 to Level 400 with real-life hands on and projects. I will also introduce you to Data Lake on AWS (that is S3) & Data Lakehouse using Apache Iceberg.
I will use AWS as the hosting platform and talk about AWS Services - EMR, S3 and MSK. I will cover Databricks as Spark hosting platform. I will also show you Spark integration with other services like AWS RDS (MySQL or PostgreSQL) and Redshift.
You will get opportunities to do hands-on using large datasets (100 GB - 300 GB or more of data). This course will provide you hands-on exercises that match with real-time scenarios like Spark batch processing, stream processing, performance tuning, streaming ingestion, Window functions, ACID transactions on Iceberg etc.
Some other highlights:
10 Projects with different datasets. Total dataset size of 250 GB or more.
Other technologies covered - EC2, EBS, VPC and IAM.
Optional Python videos
Optional AWS and SQL Essentials videos
I will conclude the Data Engineering course with Volume 3, in which, I will be covering the following Topics.
Flink
Apache Airflow
Apache Pinot
AWS Kinesis
Please provide feedback and suggestions if you want me to add any other topics.
Who this course is for:
Python developers, Application Developers, Big Data Developers
Data Engineers, Data Scientists, Data Analysts
Database Administrators, Big Data Administrators
Data Engineering Aspirants
Solutions Architect, Cloud Architect, Big Data Architect
Technical Managers, Engineering Managers, Project Managers
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