Data Engineering Essentials Databricks, PySpark, and SQL

What you'll learn

⚡ Explain Spark's distributed architecture (driver, executors, cluster manager) and how jobs run across a cluster
⚡ Build and optimize DataFrames using both the Spark DataFrame API and Spark SQL
⚡ Apply Spark's built-in optimizations — Catalyst Optimizer, AQE, Tungsten, bucketing, partitioning, and caching — to speed up real workloads
⚡ Read and monitor Spark jobs using the Spark UI: jobs, stages, tasks, and storage
⚡ Connect Spark to external data sources including S3 and JDBC databases
⚡ Build Spark Structured Streaming pipelines and integrate with Kafka for real-time data

Requirements

❗ Basic Python programming knowledge (variables, functions, loops)
❗ No prior Spark, Databricks, or big data experience needed — covered from first principles
❗ A computer with internet access to use Databricks Community Edition (free) or a similar Spark environment

Description

This course is designed for professionals already in tech roles, such as software engineers, analysts, and backend developers, who aim to transition into data engineering or strengthen their existing data skills. It offers a practical, hands-on approach to Apache Spark, PySpark, Databricks, and SQL, emphasizing how production data pipelines operate rather than merely syntax.
Participants will explore Spark's core APIs, including RDDs, DataFrames, and Spark SQL, then delve into key concepts that differentiate a functional pipeline from a production-ready one: partitioning, caching, the Catalyst optimizer, adaptive query execution, skew handling, and debugging performance issues via the Spark UI. The course also covers integration with external data sources like S3 and introduces Spark Structured Streaming for real-time data processing, used in actual streaming pipelines.
The primary goal of this course is to build genuine, production-level data engineering expertise, not just pass a test. That said, the concepts covered here, partitioning strategy, query optimization, skew handling, and debugging via the Spark UI, are exactly the topics that come up in real data engineering interviews and underpin what's tested in industry certifications like the Databricks exams. Mastering the material here gives you a real foundation for both, even though the course itself isn't structured as interview or certification prep.
Each module features practical notebooks for immediate application of concepts.
Taught by an experienced data engineer active since 2012, working on real-time streaming and data platform engineering, the course shares insights from real-world data systems, including failure modes and trade-offs not covered in syntax tutorials.
By course end, you'll be equipped to design, build, and debug Spark pipelines with the expertise expected of a professional data engineer, beyond just completing exercises.

Who this course is for

⭐ Aspiring or junior data engineers who want to build production-ready Spark skills
⭐ Python/SQL developers moving into big data or data engineering roles
⭐ Data analysts or scientists who need to understand distributed processing to scale their work

Homepage

https://www.udemy.com/course/data-engineering-essentials-databricks-pyspark-and-sql

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