Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 846 MB
Genre: eLearning Video | Duration: 21 lectures (1 hour, 41 mins) | Language: English
Spark Scala Framework, Hive, IntelliJ, Maven, Winutil, Postgres, Logging, Exception Handling, log4j, Cloudera QuickStart.
What you'll learn Homepage: https://www.udemy.com/course/spark-scala-coding-best-practices-data-pipeline/
Spark Scala industry standard coding practices - Logging, Exception Handling, Reading from Configuration File
Building a data pipeline using Hive, Spark and PostgreSQL
Spark Scala development with Intellij, Maven
Cloudera QuickStart VM setup on GCP
Requirements
Basic Big Data, Spark knowledge required
Description
This course will bridge the gap between your academic and real world knowledge and prepare you for an entry level Big Data Spark Scala developer role. You will learn the following
Spark Scala coding best practices
Logging - log4j, slf4
Exception Handling
Configuration using Typesafe config
Doing development work using IntelliJ, Maven
Using your local environment as a Hadoop Hive environment
Reading and writing to a Postgres database using Spark
Building a data pipeline using Hadoop , Spark and Postgres
Bonus - Setting up Cloudera QuickStart VM on Google Cloud Platform (GCP)
Prerequisites :
Basic programming skills
Basic database knowledge
Big Data and Spark entry level knowledge
Who this course is for:
Students looking at moving from Big Data Spark academic background to a real world developer role
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