Data Engineering Essentials – SQL, Python and Spark, Build Data Engineering Pipelines using SQL, Python and Spark.
As part of this course, you will learn all the Data Engineering Essentials related to building Data Pipelines using SQL, Python as well as Spark.
About Data Engineering
Data Engineering is nothing but processing the data depending up 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.
Course Details
As part of this course, you will be learning Data Engineering Essentials such as SQL, Programming using Python and Spark. Here is the detailed agenda for the course.
- Database Essentials – SQL using Postgres
- Getting Started with Postgres
- Basic Database Operations (CRUD or Insert, Update, Delete)
- Writing Basic SQL Queries (Filtering, Joins and Aggregations)
- Creating Tables and Indexes
- Partitioning Tables and Indexes
- Predefined Functions (String Manipulation, Date Manipulation and other functions)
- Writing Advanced SQL Queries
- Programming Essentials using Python
- Perform Database Operations
- Getting Started with Python
- Basic Programming Constructs
- Predefined Functions
- Overview of Collections – list and set
- Overview of Collections – dict and tuple
- Manipulating Collections using loops
- Understanding Map Reduce Libraries
- Overview of Pandas Libraries
- Database Programming – CRUD Operations
- Database Programming – Batch Operations
- Setting up Single Node Cluster for Practice
- Setup Single Node Hadoop Cluster
- Setup Hive and Spark on Single Node Cluster
- Introduction to Hadoop eco system
- Overview of HDFS Commands
- Data Engineering using Spark SQL
- Getting Started with Spark SQL
- Basic Transformations
- Managing Tables – Basic DDL and DML
- Managing Tables – DML and Partitioning
- Overview of Spark SQL Functions
- Windowing Functions
- Data Engineering using Spark Data Frame APIs
- Data Processing Overview
- Processing Column Data
- Basic Transformations – Filtering, Aggregations and Sorting
- Joining Data Sets
- Windowing Functions – Aggregations, Ranking and Analytic Functions
- Spark Metastore Databases and Tables
Desired Audience
Here are the desired audience for this course.
- College students and entry level professionals to get hands on expertise with respect to Data Engineering. This course will provide enough skills to face interviews for entry level data engineers.
- Experienced application developers to gain expertise related to Data Engineering.
- Conventional Data Warehouse Developers, ETL Developers, Database Developers, PL/SQL Developers to gain enough skills to transition to be successful Data Engineers.
- Testers to improve their testing capabilities related to Data Engineering applications.
- Any other hands on IT Professional who want to get knowledge about Data Engineering with Hands-On Practice.
Prerequisites
- Logistics
- Computer with decent configuration (At least 4 GB RAM, however 8 GB is highly desired)
- Dual Core is required and Quad Core is highly desired
- Chrome Browser
- High Speed Internet
- Desired Background
- Engineering or Science Degree
- Ability to use computer
- Knowledge or working experience with databases and any programming language is highly desired
