BigDataSchools.com Roadmap 2025 Spark & PySpark GCP AWS Azure Follow on Medium

Welcome to Big Data Schools

Master Data Engineering with practical tutorials on Apache Spark, PySpark, Scala Spark, Pandas, and Cloud Platforms. From beginner to advanced, learn how to build production-ready data pipelines step by step.

🚀 Start with 2025 Data Engineering Roadmap 📚 Browse Spark & PySpark Tutorials

The ultimate resource for data engineers, data analysts, and data scientists.

PySpark Scala Spark Pandas Data Pipelines Azure / GCP / AWS

Latest Data Engineering Updates

December 2025

⭐ Databricks Spark Runtime – December 2025 Updates

Databricks · Apache Spark · Platform Release
Spark Databricks Performance
December 2025

⭐ Bigquery - You can now enable autonomous embedding generation on tables - december 02, 2025

Google Cloud · BigQuery · AI & Analytics
BigQuery AI SQL GCP
December 2025

⭐ Serverless for Apache Spark: Runtime version 3.0 is now generally available – December 04, 2025

Dataproc · Serverless · Cost Optimization
Dataproc Serverless Cost Optimization

Data Engineering Technologies

Learn the most in-demand tools for modern data engineering with clear explanations and hands-on examples.

Scala Spark (Apache Spark)

Apache Spark is a powerful unified analytics engine for large-scale data processing. Learn Spark internals, RDDs, DataFrames, transformations, and actions using Scala.

  • Batch & streaming data processing
  • Joins, aggregations & window functions
  • Optimizing Spark jobs for performance

Scala Spark SQL Performance Tuning

Start Scala Spark Tutorials →

PySpark

PySpark brings the power of Spark to Python. Perfect for data engineers and data scientists who love the Python ecosystem.

  • DataFrames & Spark SQL with Python
  • ETL pipelines on large datasets
  • Integrating PySpark with cloud storage

Python ETL Pipelines Big Data

Start PySpark Tutorials →

Pandas

Pandas is the go-to Python library for data manipulation and analysis. Learn how to clean, transform, and explore datasets efficiently.

  • DataFrames, indexing & filtering
  • Handling missing & messy data
  • Working with CSV, Excel, SQL & more

Data Cleaning EDA Python

Start Pandas Tutorials →

Cloud Data Engineering

Learn how to design scalable, cloud-native data platforms on Azure, GCP, and AWS.

Cloud platforms simplify data ingestion, storage, processing, and analytics at scale. Using tools like Azure Synapse, BigQuery, and Redshift, you can build robust data warehouses, streaming pipelines, and machine learning–ready datasets.

Microsoft Azure

Build end-to-end data solutions with Azure Synapse, Data Factory, Data Lake, and Azure Databricks.

  • Designing data lakes & lakehouses
  • ETL/ELT with Azure Data Factory
  • Spark on Azure Databricks

Explore Azure Data Engineering →

Google Cloud

Use BigQuery, Dataflow, and Dataproc to build modern, serverless data platforms on Google Cloud.

  • Large-scale analytics with BigQuery
  • Streaming & batch pipelines with Dataflow
  • Spark & Hadoop with Dataproc

Explore GCP Data Engineering →

Amazon - AWS

AWS provides Glue, Redshift, EMR, and more for scalable data processing and analytics. Tutorials coming soon!

  • AWS Glue for ETL & cataloging
  • Redshift for data warehousing
  • EMR for Spark & Hadoop workloads

Coming Soon – AWS Tutorials

Why Learn with Big Data Schools?

Hands-on Focus

Concepts are explained with practical examples that you can run and modify yourself.

Modern Stack

Up-to-date coverage of tools used in real-world Data Engineering roles in 2025.

Structured Path

Roadmaps and topic ordering so you always know what to learn next.