Ultimate Data Engineering with Databricks: Develop Scalable Data Pipelines Using Data Engineering's Core Tenets Such as Delta Tables, Ingestion, ... Security, and Scalability (English Edition) - Tapa blanda

Sinopsis

Navigating Databricks with Ease for Unparalleled Data Engineering Insights.

Book Description
Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike.

Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions.

Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as a data engineering professional in today's competitive job market.

What you will learn
● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines.
● Design and implement high-performance data solutions for scalability.
● Apply essential best practices for ensuring data integrity in pipelines.
● Explore advanced Databricks features for tackling complex data tasks.
● Learn to optimize data pipelines for streamlined workflows.

Table of Contents
1. Fundamentals of Data Engineering
2. Mastering Delta Tables in Databricks
3. Data Ingestion and Extraction
4. Data Transformation and ETL Processes
5. Data Quality and Validation
6. Data Modeling and Storage
7. Data Orchestration and Workflow Management
8. Performance Tuning and Optimization
9. Scalability and Deployment Considerations
10. Data Security and Governance
Last Words
Index

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