Udemy - Data Processing with Spark Kafka (Data Engineering Vol2 AWS)

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Free Download Udemy - Data Processing with Spark Kafka (Data Engineering Vol2 AWS)

Published: 4/2025
Created by: Soumyadeep Dey
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Level: All | Genre: eLearning | Language: English | Duration: 13 Lectures ( 3h 35m ) | Size: 1.28 GB
Batch & Stream Processing using Spark and Kafka on AWS
What you'll learn
Deep dive on Spark and Kafka using AWS EMR, Glue, 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, Glue, MSK
Requirements
Good to have AWS and SQL knowledge
Description
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 like EMR, S3, Glue and MSK. I will also show you Spark integration with other services like AWS RDS, Redshift and DynamoDB.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:5 Projects with different datasets. Total dataset size of 250 GB or more.Contains training of data modelling - Normalization & ER Diagram for OLTP systems. Dimensional modelling for OLAP/DWH systems.Other technologies covered - EC2, EBS, VPC and IAM.Optional Python Course
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
Homepage:

https://www.udemy.com/course/data-processing-with-spark-kafka-data-engineering-vol2-aws/

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