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Genre: eLearning
What you’ll learn
Setup Development Environment to learn building Data Engineering Applications on GCP
Database Essentials for Data Engineering using Postgres such as creating tables, indexes, running SQL Queries, using important pre-defined functions, etc.
Data Engineering Programming Essentials using Python such as basic programming constructs, collections, Pandas, Database Programming, etc.
Data Engineering using Spark Dataframe APIs (PySpark). Learn all important Spark Data Frame APIs such as select, filter, groupBy, orderBy, etc.
Data Engineering using Spark SQL (PySpark and Spark SQL). Learn how to write high quality Spark SQL queries using SELECT, WHERE, GROUP BY, ORDER BY, ETC.
Relevance of Spark Metastore and integration of Dataframes and Spark SQL
Ability to build Data Engineering Pipelines using Spark leveraging Python as Programming Language
Use of different file formats such as Parquet, JSON, CSV etc in building Data Engineering Pipelines
Setup self support single node Hadoop and Spark Cluster to get enough practice on HDFS and YARN
Understanding Complete Spark Application Development Life Cycle to build Spark Applications using Pyspark. Review the applications using Spark UI.
Who this course is for:
Computer Science or IT Students or other graduates with passion to get into IT
Data Warehouse Developers who want to transition to Data Engineering roles
ETL Developers who want to transition to Data Engineering roles
Database or PL/SQL Developers who want to transition to Data Engineering roles
BI Developers who want to transition to Data Engineering roles
QA Engineers to learn about Data Engineering
Application Developers to gain Data Engineering Skills
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