What do you think?
Rate this book
Building the Future: How to Succeed as a Data Engineer.
What You'll
Understanding Data Gain a clear understanding of what data engineering is, the roles and responsibilities of a data engineer, and the essential skills required to excel in this field.
Building a Strong Learn the fundamentals of programming languages such as Python, SQL, and PySpark. Understand basic database concepts and data warehousing techniques, and get introduced to ETL (Extract, Transform, Load) processes.
Advanced Data Engineering Dive deeper into big data technologies like Databricks and Apache Spark. Explore data pipeline orchestration with Apache Airflow and cloud data engineering using AWS, Google Cloud Platform (GCP), and Microsoft Azure.
Data Modeling and Discover the importance of data modeling and learn about different data modeling techniques, including Star Schema, Snowflake Schema, and Normalized Data Model. Design scalable and efficient data architectures.
Tools and Get acquainted with essential tools and technologies in the data engineering toolkit, including Databricks, Apache Spark, SQL, Python, PySpark, Apache Airflow, cloud data warehouses, and data lakes.
Real-World Applications and Case See how data engineering is applied across various industries such as healthcare, retail, finance, telecommunications, and manufacturing. Explore detailed case studies that demonstrate the impact of data engineering in solving complex problems.
Career Growth and Learn strategies for continuous learning and networking. Understand the importance of certifications and get tips for career advancement. Discover how to build a strong professional network and develop a successful career in data engineering.
Future of Data Stay ahead of the curve by exploring emerging trends and technologies in data engineering. Understand the evolving role of data engineers and prepare for future challenges and opportunities.
Additional
Engaging Quotes and Each chapter includes unique quotes and artistic diagrams that make learning fun and relatable. These elements help to reinforce key concepts and keep you motivated throughout your journey.
Practical Get additional tips for using Databricks Notebooks for SQL and PySpark practice, and Google Colab for PySpark and Python practice. These tools enhance your productivity and make your practice sessions more effective.
Understanding Data Gain a clear understanding of what data engineering is, the roles and responsibilities of a data engineer, and the essential skills required to excel in this field.
Building a Strong Learn the fundamentals of programming languages such as Python, SQL, and PySpark. Understand basic database concepts and data warehousing techniques, and get introduced to ETL (Extract, Transform, Load) processes.
Advanced Data Engineering Dive deeper into big data technologies like Databricks and Apache Spark. Explore data pipeline orchestration with Apache Airflow and cloud data engineering using AWS, Google Cloud Platform (GCP), and Microsoft Azure.
Data Modeling and Discover the importance of data modeling and learn about different data modeling techniques, including Star Schema, Snowflake Schema, and Normalized Data Model. Design scalable and efficient data architectures.
Tools and Get acquainted with essential tools and technologies in the data engineering toolkit, including Databricks, Apache Spark, SQL, Python, PySpark, Apache Airflow, cloud data warehouses, and data lakes.
Real-World Applications and Case See how data engineering is applied across various industries such as healthcare, retail, finance, telecommunications, and manufacturing. Explore detailed case studies that demonstrate the impact of data engineering in solving complex problems.
Career Growth and Learn strategies for continuous learning and networking. Understand the importance of certifications and get tips for career advancement. Discover how to build a strong professional network and develop a successful career in data engineering.
Future of Data Stay ahead of the curve by exploring emerging trends and technologies in data engineering. Understand the evolving role of data engineers and prepare for future challenges and opportunities.
Additional
Engaging Quotes and Each chapter includes unique quotes and artistic diagrams that make learning fun and relatable. These elements help to reinforce key concepts and keep you motivated throughout your journey.
Practical Get additional tips for using Databricks Notebooks for SQL and PySpark practice, and Google Colab for PySpark and Python practice. These tools enhance your productivity and make your practice sessions more effective.
51 pages, Paperback
Published August 26, 2024
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.