PySpark for Data Engineering: Apache Spark Hands-On

Understand how spark applications start with the spark session, the driver-level entry point that coordinates with executors, while spark context remains a lower-level component managed by the session.

Spark DataFrames are the main way to work with data in Spark, offering a distributed, partitioned, pandas-like, high-level API that scales across a cluster and hides complexity.

Read a csv in Spark on Databricks by uploading transaction.csv to a volume called Spark Formation. Read with header true and infer schema true, then df.show and count 100 rows.

Learn to select columns and filter rows in Spark dataframes using df.filter or .where, with country FR, amount > 100, and easing on product category (books or electronics).

Explore creating and renaming columns in Spark dataframes with withColumn, derive new columns such as 10 amount, cast amounts to integral, and rename to amount int.

Learn to aggregate data with Spark DataFrames using group by, count, sum, and average to summarize transactions by country and product category.

Learn how to perform joins in Spark dataframes using the dataframe join method on user and transaction data, joining on user ID, and employing inner and left joins.

Write Spark data frames to csv files with header, control output with overwrite or append modes, and manage partitions with coalesce to produce a single file or multiple partition files.

Learn spark sql in practice by rewriting data transformations from the dataframe api into sql, using temporary views and the spark session sql method for readable, familiar analytics.

Design an end-to-end Spark ETL pipeline that cleans and enriches raw CSV data, enforces schemas, converts amounts to euro, and produces an analytics-ready dataset with business metrics.

Create clean, production-ready PySpark data pipelines by modularizing steps into functions. Centralize configurations, apply schema, cleaning, enrichment, and business rules, and validate data after each stage.

Set up a centralized configuration in Databricks and create a spark project volume. Import and read transactions and users data with Spark session, and implement a reusable read function.

Define explicit schemas for transactions and users, create a reusable read function to load dataframes, and centralize configuration to ensure Spark reads with correct types.

Explore a PySpark data cleaning workflow that enforces data quality rules by filtering transactions for positive amounts, non-null dates, and currencies supported by the dataset.

This lecture explains logging as a data engineering practice, showing how to configure a logger with info, warning, and error levels and replace prints with meaningful logs.

Enrich transactions by joining with the users data on user id via an inner join, keep only valid users, and log pre- and post-enrichment counts.

Compute business metrics by grouping data by country and customer segment to derive total revenue, transaction count, and average transaction value per country and segment.

Recap the end-to-end Spark project from raw data ingestion to parquet analytics-ready outputs, covering schema, cleaning, enrichment, currency conversions, and ETL-focused production readiness.

Learn how Spark distinguishes transformations and actions, and how lazy evaluation builds a plan until an action like show triggers execution and optimization.

Open a spark environment, create a spark session, load real datasets in CSV or Parquet, and clean, transform, and join with spark DataFrames to build ETL pipelines.