This book provides 25+ carefully crafted project blueprints designed to help you understand how to integrate various Big Data and Cloud technologies into real-world data engineering workflows.
We have used a comprehensive tech stack, including:
Hadoop, Spark, Hive, Airflow, S3, Athena, Redshift, QuickSight, Apache Flink, Kafka, Google BigQuery, DataProc, Cloud Spanner, Cosmos DB, Azure Data Factory, Google DMS, Cloud SQL, Bigtable, AWS Lambda, Kinesis, PostgreSQL, Elasticsearch, Kibana, Tableau, PySpark, Apache Nifi, Azure Synapse Serverless SQL, Azure Batch, and Azure Event Hubs.
Each blueprint breaks down the scope, technology stack, use cases, challenges, and solutions, helping you gain a deep understanding of data pipelines, real-time processing, data lakes, cloud migration, and big data architectures.
Whether you're a fresher looking to enter data engineering or a mid-level professional preparing for interviews and resume building, this book will provide practical insights to boost your expertise and confidence.
🚀 Start your Data Engineering journey today!
✔ Blueprints, Not Implementations – These are structured project ideas, giving you clarity on what to build and how to connect the tools. They don’t contain implementation code but serve as a foundation to start your own projects.