Master SQL, Python, and Apache Spark (PySpark) with Hands-On Projects using Databricks on Google Cloud

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
Why Learn Data Engineering?Data Engineering is one of the fastest-growing fields in the tech industry. Organizations of all sizes rely on Data Engineers to build and maintain the infrastructure that powers big data analytics, reporting, and machine learning. Data Engineers design, implement, and optimize data pipelines to efficiently process and manage data for business intelligence, real-time analytics, and AI applications.With SQL, Python, and Apache Spark, Data Engineers can handle large-scale data processing efficiently. These skills are highly sought after in finance, healthcare, e-commerce, and every data-driven industry.If you are looking for an industry-relevant and practical course that teaches you how to work with SQL, Python, Apache Spark (PySpark), and Databricks on Google Cloud Platform (GCP), this course is the perfect place to start.What You Will Learn in This CourseThis course is designed to take you from a beginner to an intermediate level in Data Engineering. You will gain hands-on experience working with SQL, Python, Apache Spark (PySpark), and Databricks by building real-world batch and streaming data pipelines.SQL for Data Engineering (PostgreSQL)Install and configure PostgreSQL to practice SQL queriesLearn fundamental SQL concepts such as SELECT, WHERE, JOIN, GROUP BY, HAVING, and ORDER BYPerform advanced SQL operations including window functions, ranking, cumulative aggregations, and complex joinsLearn how to optimize SQL queries for performance and debuggingPython for Data EngineeringUnderstand Python fundamentals for data processingWork with Python Collections to efficiently process structured dataUse Pandas to manipulate, clean, and analyze dataBuild real-world Python projects, including a File Format Converter and a Database LoaderLearn how to troubleshoot and debug Python applicationsUnderstand performance tuning strategies for Python-based data pipelinesApache Spark (PySpark) for Big Data ProcessingLearn Spark SQL to process structured data at scaleWork with PySpark DataFrame APIs to manipulate big dataCreate and manage Delta Tables and perform CRUD operations (INSERT, UPDATE, DELETE, MERGE)Perform advanced SQL transformations using window functions, ranking, and aggregationsLearn how to optimize PySpark jobs using Spark Catalyst Optimizer and Explain PlansDebug, monitor, and optimize Spark jobs using Spark UIDeploying Data Pipelines on Databricks (Google Cloud Platform - GCP)Set up and configure Databricks on Google Cloud Platform (GCP)Learn how to provision and manage Databricks clustersDevelop PySpark applications on Databricks and execute jobs on multi-node clustersUnderstand the cost, scalability, and benefits of using Databricks for Data EngineeringPerformance Tuning and Optimization in Data EngineeringLearn query performance optimization techniques in SQL and PySparkImplement partitioning and columnar storage formats to improve efficiencyExplore debugging techniques for troubleshooting SQL and PySpark applicationsAnalyze Spark execution plans to improve job execution performanceCommon Challenges in Learning Data Engineering and How This Course HelpsMany learners struggle with setting up a proper Data Engineering environment, finding structured learning material, and gaining hands-on experience with real-world projects.This course eliminates these challenges by providing:A step-by-step guide to setting up PostgreSQL, Python, and Apache SparkHands-on exercises that simulate real-world Data Engineering problemsPractical projects that reinforce learning and build confidenceCloud-based Data Engineering with Databricks on Google Cloud, making it easier to work with large-scale dataWho Should Take This Course?This course is designed for:Beginners who want to start a career in Data EngineeringAspiring Data Engineers who want to learn SQL, Python, Apache Spark (PySpark), and DatabricksSoftware Developers and Data Analysts who want to transition into Data EngineeringData Science and Machine Learning Practitioners who need a deeper understanding of data pipelinesAnyone interested in Big Data, ETL processes, and cloud-based Data EngineeringWhy Take This Course?Beginner-Friendly ApproachThis course starts with the fundamentals and gradually builds up to advanced topics, making it accessible for beginners.Hands-On Learning with Real-World ProjectsYou will work on real-world projects to reinforce your skills and gain practical experience in building Data Pipelines.Cloud-Based Training on Databricks (GCP)This course teaches cloud-based Data Engineering using Databricks on Google Cloud, a platform widely used by companies for Big Data processing and machine learning.Comprehensive Curriculum Covering All Key Data Engineering SkillsThis course covers SQL, Python, Apache Spark (PySpark), Databricks, ETL, Big Data Processing, and Performance Optimization—all essential skills for a Data Engineer.Performance Tuning and DebuggingYou will learn how to analyze Spark execution plans, optimize SQL queries, and debug PySpark jobs, which are crucial for real-world Data Engineering projects.Lifetime Access and UpdatesYou get lifetime access to the course content, which is regularly updated to keep up with industry trends and new technologies.Course FeaturesStep-by-step instructions with detailed explanationsHands-on exercises to reinforce learningReal-world projects covering batch and streaming data pipelinesComplete Databricks setup guide for Google CloudPerformance optimization techniques for SQL and PySparkBest practices for debugging and tuning Spark jobsEnroll Today and Start Your Data Engineering JourneyIf you are serious about learning Data Engineering and want to master SQL, Python, Apache Spark (PySpark), and Databricks on Google Cloud, this course will provide you with the essential skills and hands-on experience needed to succeed in this field.Take the first step in your Data Engineering journey today—enroll now!

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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