Data Engineering Essentials using SQL, Python, and PySpark
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.
Requirements
- Laptop with decent configuration (Minimum 4 GB RAM and Dual Core)
- Sign up for GCP with the available credit or AWS Access
- Setup self support lab on cloud platforms (you might have to pay the applicable cloud fee unless you have credit)
- CS or IT degree or prior IT experience is highly desired
Description
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
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
Instructors
- 4.4 Instructor Rating
- 19,357 Reviews
- 323,985 Students
- 22 Courses
20+ years of experience in executing complex projects using a vast array of technologies including Big Data and the Cloud.
ITVersity, Inc. - is a US-based organization that provides quality training for IT professionals and we have a track record of training hundreds of thousands of professionals globally.
Building an IT career for people with required tools such as high-quality material, labs, live support, etc to upskill and cross-skill is paramount for our organization.
At this time our training offerings are focused on the following areas:
* Application Development using Python and SQL
* Big Data and Business Intelligence
* Cloud
* Datawarehousing, Databases
- 4.4 Instructor Rating
- 3,272 Reviews
- 61,147 Students
- 1 Course
- 4.4 Instructor Rating
- 9,478 Reviews
- 109,826 Students
- 11 Courses