Data Engineering Essentials using SQL, Python, and PySpark from Udemy

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
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

What's inside

Learning objectives

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.

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.

Syllabus

Detailed overview of the topics related to SQL, Python, Hadoop, Spark, etc covered as part of this course.

Introduction to Data Engineering Essentials Course

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teachers how to process and pipe data, which are essential to handling the scale of today's production problems

Covers SQL and Python, two of the most important tools for modern operations

Develops skills for building production-grade data pipelines that can be used in enterprise settings

Includes building blocks for both batch and streaming pipelines using PySpark

Teaches how to tune the performance of PySpark pipelines for production environments

Requires a basic understanding of Python, SQL, and data engineering principles

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Engineering Essentials using SQL, Python, and PySpark with these activities:

Review SQL Fundamentals

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Refresh your memory on basic SQL concepts to strengthen your foundation. This will make it easier to grasp the more advanced topics covered in the course.

Browse courses on Database Concepts

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Review online tutorials or documentation on SQL basics.
Go through your notes or textbooks from previous SQL courses.
Practice writing simple SQL queries using an online SQL editor or a local database.

Participate in Online Discussion Forums

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Engage with your peers in online discussion forums to share knowledge, ask questions, and get feedback. This will expose you to diverse perspectives and enhance your understanding of the course material.

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Join online discussion forums related to the course topics.
Actively participate in discussions by posting thoughtful questions and responses.
Read and respond to the posts of other students.
Respect different viewpoints and engage in constructive discussions.

Create a Data Engineering Glossary

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Develop a glossary of important terms and concepts related to data engineering. This will help you build your vocabulary and deepen your understanding of the field.

Browse courses on Terminology

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Identify key terms and concepts from the course material.
Research and gather definitions and explanations for each term.
Organize the terms alphabetically or by category.
Create a document or spreadsheet to compile your glossary.

Four other activities

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Show all seven activities

Practice SQL Queries

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Frequent practice is the key to mastering SQL. Solve as many SQL queries as possible to refine your command over the language and improve your problem-solving ability.

Browse courses on SQL Queries

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Connect to a database management system (DBMS) using a SQL client tool of your choice.
Create a new database and tables to work with.
Write SQL queries to perform various operations like data retrieval, insertion, updation, and deletion.
Test your queries and troubleshoot any errors.
Repeat steps 3 and 4 until you are confident in your ability to write SQL queries.

Work on Python Projects

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Hands-on experience is invaluable for solidifying your understanding of Python. Work on projects that involve data manipulation, analysis, and visualization to enhance your practical skills.

Browse courses on Python Programming

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Choose a project idea that aligns with your interests and the course material.
Gather the required resources and set up a development environment.
Implement your project using Python and appropriate libraries.
Test and debug your code to ensure it meets the project requirements.
Document your project and share it with others for feedback and learning.

Explore Spark DataFrames and SQL

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Gain proficiency in working with Apache Spark DataFrames and SQL by practicing various operations and techniques. This will help you master data manipulation and analysis using Spark.

Browse courses on Apache Spark

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Set up a Spark environment using a cloud platform or a local cluster.
Load data into Spark DataFrames and explore the DataFrame API.
Perform data transformations and aggregations using DataFrames.
Write Spark SQL queries to perform complex data analysis.
Optimize your Spark code for performance and scalability.

Build a Data Engineering Pipeline

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Challenge yourself by building a complete data engineering pipeline that involves data ingestion, transformation, and analysis. This project will provide you with a comprehensive understanding of the entire data engineering process.

Browse courses on Data Processing

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Define the scope and requirements of your data engineering pipeline.
Choose appropriate tools and technologies for each stage of the pipeline.
Implement the data ingestion process to load data from various sources.
Develop data transformation and cleansing processes to prepare the data for analysis.
Build data analysis and visualization components to explore and present insights from the data.

Career center

Learners who complete Data Engineering Essentials using SQL, Python, and PySpark will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data Engineers are responsible for building, testing, and deploying data pipelines. They work closely with data scientists and other stakeholders to ensure that data meets their needs. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Data Engineer, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data engineering and prepare you for a career in this in-demand field.

See salaries and explore the career path for Data Engineer

Data Scientist

Data Scientists use data to solve business problems. They work with data engineers to build data pipelines and with data analysts to analyze data and develop insights. This course provides a strong foundation in the skills and knowledge needed to be a successful Data Scientist, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data science and prepare you for a career in this exciting field.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use data to analyze trends and patterns. They work with data engineers and data scientists to build data pipelines and develop insights. This course provides a strong foundation in the skills and knowledge needed to be a successful Data Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data analysis and prepare you for a career in this growing field.

See salaries and explore the career path for Data Analyst

Database Administrator

Database Administrators are responsible for managing and maintaining databases. They work with data engineers and data scientists to ensure that data is stored and accessed efficiently. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Database Administrator, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in database administration and prepare you for a career in this essential field.

See salaries and explore the career path for Database Administrator

Software Engineer

Software Engineers design, build, and maintain software applications. They work with data engineers and data scientists to build data pipelines and develop insights. This course provides a strong foundation in the skills and knowledge needed to be a successful Software Engineer, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in software engineering and prepare you for a career in this in-demand field.

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Data Architect

Data Architects design and implement data architectures for organizations. They work with data engineers and data scientists to ensure that data is stored and accessed efficiently. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Data Architect, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data architecture and prepare you for a career in this essential field.

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Business Analyst

Business Analysts use data to solve business problems. They work with data engineers and data scientists to build data pipelines and develop insights. This course provides a strong foundation in the skills and knowledge needed to be a successful Business Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in business analysis and prepare you for a career in this growing field.

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Project Manager

Project Managers plan, execute, and close projects. They work with data engineers and data scientists to ensure that data projects are delivered on time and within budget. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Project Manager, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in project management and prepare you for a career in this essential field.

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Data Warehouse Analyst

Data Warehouse Analysts design and implement data warehouses for organizations. They work with data engineers and data scientists to ensure that data is stored and accessed efficiently. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Data Warehouse Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data warehousing and prepare you for a career in this essential field.

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Database Developer

Database Developers design and develop databases for organizations. They work with data engineers and data scientists to ensure that data is stored and accessed efficiently. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Database Developer, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in database development and prepare you for a career in this essential field.

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Data Visualization Analyst

Data Visualization Analysts use data to create visualizations that communicate insights to stakeholders. They work with data engineers and data scientists to build data pipelines and develop insights. This course provides a strong foundation in the skills and knowledge needed to be a successful Data Visualization Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data visualization and prepare you for a career in this growing field.

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Data Governance Analyst

Data Governance Analysts develop and implement data governance policies and procedures for organizations. They work with data engineers and data scientists to ensure that data is used in a consistent and ethical manner. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Data Governance Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data governance and prepare you for a career in this essential field.

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Information Security Analyst

Information Security Analysts protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with data engineers and data scientists to ensure that data is stored and accessed securely. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Information Security Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in information security and prepare you for a career in this essential field.

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Data Privacy Analyst

Data Privacy Analysts develop and implement data privacy policies and procedures for organizations. They work with data engineers and data scientists to ensure that data is used in a compliant manner. This course provides a comprehensive overview of the skills and knowledge needed to be a successful Data Privacy Analyst, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data privacy and prepare you for a career in this essential field.

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Data Science Consultant

Data Science Consultants help organizations to use data to make better decisions. They work with data engineers and data scientists to build data pipelines and develop insights. This course provides a strong foundation in the skills and knowledge needed to be a successful Data Science Consultant, including SQL, Python, and PySpark. With hands-on exercises and real-world examples, this course will help you build a foundation in data science consulting and prepare you for a career in this growing field.

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