[Udemy Course] Data Engineering Essentials using SQL, Python, and PySpark by Durga Viswanatha Raju Gadiraju, Vaishnavi Kalidindi

What you'll learn

Setup Environment to learn SQL and Python essentials for Data Engineering

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) using Databricks. 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 Hadoop and Spark Cluster on GCP using Dataproc

Understanding Complete Spark Application Development Life Cycle to build Spark Applications using Pyspark. Review the applications using Spark UI.

 

As part of this course, you will learn all the Data Engineering Essentials related to building Data Pipelines using SQL, Python as Hadoop, Hive, or Spark SQL as well as PySpark Data Frame APIs. You will also understand the development and deployment lifecycle of Python applications using Docker as well as PySpark on multinode clusters. You will also gain basic knowledge about reviewing Spark Jobs using Spark UI.

 

About Data Engineering

 

Data Engineering is nothing but processing the data depending on our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc.

 

Here are some of the challenges the learners have to face to learn key Data Engineering Skills such as Python, SQL, PySpark, etc.

 

Having an appropriate environment with Apache Hadoop, Apache Spark, Apache Hive, etc working together.

 

Good quality content with proper support.

 

Enough tasks and exercises for practice

 

This course is designed to address these key challenges for professionals at all levels to acquire the required Data Engineering Skills (Python, SQL, and Apache Spark).

 

Setup Environment to learn Data Engineering Essentials such as SQL (using Postgres), Python, etc.

 

Setup required tables in Postgres to practice SQL

 

Writing basic SQL Queries with practical examples using WHERE, JOIN, GROUP BY, HAVING, ORDER BY, etc

 

Advanced SQL Queries with practical examples such as cumulative aggregations, ranking, etc

 

Scenarios covering troubleshooting and debugging related to Databases.

 

Performance Tuning of SQL Queries

 

Exercises and Solutions for SQL Queries.

 

Basics of Programming using Python as Programming Language

 

Python Collections for Data Engineering

 

Data Processing or Data Engineering using Pandas

 

2 Real Time Python Projects with explanations (File Format Converter and Database Loader)

 

Scenarios covering troubleshooting and debugging in Python Applications

 

Performance Tuning Scenarios related to Data Engineering Applications using Python

 

Getting Started with Google Cloud Platform to setup Spark Environment using Databricks

 

Writing Basic Spark SQL Queries with practical examples using WHERE, JOIN, GROUP BY, HAVING, ORDER BY, etc

 

Creating Delta Tables in Spark SQL along with CRUD Operations such as INSERT, UPDATE, DELETE, MERGE, etc

 

Advanced Spark SQL Queries with practical examples such as ranking

 

Integration of Spark SQL and Pyspark

 

In-depth coverage of Apache Spark Catalyst Optimizer for Performance Tuning

 

Reading Explain Plans of Spark SQL Queries or Pyspark Data Frame APIs

 

In-depth coverage of columnar file formats and Performance tuning using Partitioning

 

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Product Details: https://bit.ly/3GimtTI

 

File size: 26.1GB

 

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