Data Engineering for Beginners: Learn SQL, Python & Spark
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Unclaimed ProfileThis comprehensive 56-hour program is designed for aspiring data professionals who want to transition into data engineering by mastering the industry's core trifecta: SQL, Python, and Apache Spark. Led by experienced instructors Durga Viswanatha Raju Gadiraju, Phani Bhushan Bozzam, and Vinay Gadiraju, the course provides a robust foundation starting from database essentials in PostgreSQL to advanced big data processing. Students will learn how to write efficient SQL queries, manipulate databases, and leverage Python for data manipulation using libraries like Pandas. The curriculum transitions seamlessly into the world of Big Data, where learners leverage PySpark on Databricks to manage large-scale data processing pipelines. You will master both Spark DataFrame APIs and Spark SQL, exploring crucial concepts like the Spark Metastore and file format optimization (such as Parquet, JSON, and CSV). Additionally, the course covers cloud-native deployments, guiding you through setting up Hadoop and Spark clusters on Google Cloud Platform (GCP) using Dataproc. Whether you are a software developer, data analyst, or recent graduate, this course equips you with the practical, hands-on skills required to build and deploy production-grade data pipelines.
About the creator
Durga Viswanatha Raju Gadiraju, Phani Bhushan Bozzam, Vinay Gadiraju
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Reviews
Durga Viswanatha Raju Gadiraju, Phani Bhushan Bozzam, and Vinay Gadiraju are the collaborative force driving ITVersity, an acclaimed training platform dedicated to demystifying complex data engineering concepts. With decades of collective industry experience spanning big data architectures, enterprise software development, and cloud computing, this instructor trio has successfully guided hundreds of thousands of learners globally. Durga Viswanatha Raju Gadiraju is renowned for his meticulously structured, step-by-step technical pedagogy, helping students master core tools like Apache Spark, Python, and SQL from the ground up. Alongside Phani Bhushan Bozzam and Vinay Gadiraju, who contribute deep practical expertise in software engineering and database…Show more
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Program Overview
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Learning format
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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.
Best For
- Aspiring data engineers looking to build a career in big data infrastructure.
- Software developers or data analysts wanting to transition into data pipeline development.
- Learners who prefer a hands-on, project-based approach to mastering SQL, Python, and Spark.
- Professionals seeking experience with cloud-based big data deployments on Google Cloud Platform.
Not For
- Absolute beginners with no prior interest or experience in programming or database concepts.
- Experienced data engineers looking for advanced architectural patterns or niche distributed systems theory.
- Individuals seeking a purely theoretical academic overview without practical coding exercises.
- Professionals looking for courses focused exclusively on machine learning model development rather than data engineering pipelines.
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