Apache Spark for Beginners teaches batch data processing with PySpark. Using small retail datasets, you will turn CSV and JSON files into typed DataFrames, clean records, calculate sales reports, and save results that another run can read.
The course includes 11 Guided Labs and 2 Challenges. Complete examples lead to small changes and independent tasks using new inputs. You will inspect real execution evidence and submit a reusable application rather than depend on an interactive session.
What You Will Learn
After completing this course, you will be able to:
- create DataFrames with explicit schemas and read CSV and nested JSON;
- filter and transform columns, parse values safely, and remove identical duplicate rows;
- aggregate decimal sales amounts and join customer and product reference data;
- express familiar operations with Spark SQL and session-local views;
- write partitioned Parquet datasets and verify their logical records;
- inspect execution plans and errors, then submit and rerun a parameterized batch application.
Who This Course Is For
This course is for Python learners beginning data engineering or preparing their first Spark batch workflow.
Prerequisites: Basic Python variables, collections, function calls, and running scripts, plus basic terminal use. Prior Spark, pandas, Hadoop, Hive, or Java knowledge is not required.
Learning environment: LabEx provides a prepared Linux terminal in your browser. Real PySpark runs in local[2] mode on one machine with two execution threads; distributed cluster deployment is outside the course.
Frequently Asked Questions
How much Python do I need, and must I finish Hadoop or Hive first?
You should understand Python variables, collections, function calls, and running scripts. Hadoop and Hive provide useful context but are not prerequisites. The course introduces DataFrames and the Spark SQL it uses.
Will I learn streaming as well as batch processing?
This course covers batch processing of CSV and JSON files, ending with a reusable sales pipeline. Spark Streaming and RDD programming are outside its scope.
Do I need to install Spark or rent multiple machines?
No. LabEx provides PySpark and the datasets in a prepared browser environment. You use two execution threads on one machine, learning real APIs and execution behavior without deploying a distributed cluster.
