Databricks & Spark Masterclass Real– Time Data Engineering

Master Azure Databricks, PySpark & Cloud Storage with Real-Time Data Engineering Projects & Interview Prep
What you'll learn
- Set up and navigate Azure Databricks environments - Create clusters, manage notebooks, and interact with DBFS using real-world workflows
- Integrate Databricks with cloud storage systems - Mount Azure Blob, ADLS Gen2, and AWS S3 using keys, SAS tokens, and app registrations.
- Master PySpark fundamentals and Spark architecture - Understand RDDs, DataFrames, transformations, actions, and Spark's internal mechanisms.
- Apply advanced Spark optimization techniques - Tune performance using partitioning, caching, file formats, and configuration strategies.
- Build real-time data engineering projects from scratch - Implement joins, aggregations, JSON flattening, and handle diverse file types in Spark.
- Prepare for interviews with hands-on coding challenges - Practice SQL and PySpark questions commonly asked in Databricks and Spark interviews.
Requirements
- No prior experience with Databricks or Spark is required — this course starts from the fundamentals and builds up to advanced, real-time projects.
- Basic understanding of Python will be helpful but not mandatory. All Python concepts used in the course are explained with practical examples.
A laptop or desktop with internet access to follow along with Databricks notebooks and cloud integrations.
We will teach you how to Databricks community which is completely Free setup
- Curiosity, commitment, and a willingness to learn through hands-on, project-based exercises.
Description
Unlock the full power of Azure Databricks and Apache Spark with this hands-on, project-driven masterclass designed for aspiring and professional data engineers. Whether you're just starting out or looking to sharpen your skills, this course offers a comprehensive journey through real-world data engineering workflows — from foundational setup to advanced Spark optimizations.Led by a seasoned data architect and trainer, you'll learn how to build scalable pipelines, integrate cloud storage, master PySpark transformations, and prepare for interviews with confidence. Every concept is taught through practical examples, real-time scenarios, and enterprise-grade projects — not just theory.By the end of this course, you'll be able to:Set up and navigate Azure Databricks environments with easeMount and manage cloud storage (Blob, ADLS Gen2, AWS S3) securelyBuild and optimize Spark jobs using PySpark, DataFrames, and RDDsConnect Databricks to external databases like Azure SQL, Synapse, and SnowflakeHandle diverse file formats including CSV, Parquet, Avro, ORC, Excel, and JSONApply performance tuning techniques like partitioning, caching, and join strategiesWrite clean, modular Python code for data engineering workflowsSolve real-time data problems and prepare for technical interviewsThis course is ideal for learners who value practical experience, clarity, and future-proof skills. If you're ready to move beyond generic certification prep and build authentic expertise — this masterclass is for you.
Who this course is for
Beginners who want to break into data engineering with a strong foundation in Spark and Databricks
Professionals preparing for interviews or transitioning into cloud-based data roles
Students and self-learners seeking practical experience with PySpark, storage integration, and performance optimization
Anyone tired of generic, certification-focused training and looking for authentic, project-based learning from a real-time practitioner
Homepage

https://www.udemy.com/course/databricksbycloudpandith67/

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