Ultimate Data Engineering with Databricks

Name: Ultimate Data Engineering with Databricks
Brand: Orange Education PVT Ltd
Price: 24.49 EUR
Availability: OnlineOnly

Mayank Malhotra(Autor*in)

Orange Education PVT Ltd (Verlag)

1. Auflage

Erschienen am 14. Februar 2024

268 Seiten

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978-81-969947-5-4 (ISBN)

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Beschreibung

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

Weitere Details

Inhalt

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Technical Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Fundamentals of Data Engineering
Introduction
Structure
Role of Data Engineering in Modern Organizations
Data Engineering's Role in Enabling Data Analytics and Machine Learning
Data Engineering Supports Data-Driven Decision-Making
Understanding Data Engineering Concepts and Principles
Core Concepts and Principles in Data Engineering
Overview of Data Pipelines, Data Integration, and Data Transformation
Overview of Databricks
Databricks as a Unified Analytics Platform
Key Features and Benefits of Using Databricks for Data Engineering
Overview of Databricks Architecture and Components
Components of Databricks Architecture
Understanding the Databricks Workspace and Its Functionalities
Introducing Databricks Notebooks and Its Role in Data Engineering
Setting Up Databricks Environment and Workspace
Best Practices for Managing Databricks Notebooks
Conclusion
Key Terms
2. Mastering Delta Tables in Databricks
Introduction
Structure
Understanding the Benefits of Delta Tables in Databricks
Enhanced Data Reliability and Consistency with Delta Tables
Efficient Data Processing and Query Optimization
Improved Data Versioning and Time Travel Capabilities in Delta Tables
Data Versioning
Time Travel
Use Case Example
Overview of Delta Lake Architecture and Concepts
Introduction to Delta Lake
Delta Lake Architecture
Understanding Delta Lake's File Organization and Transaction Log
File Organization in Delta Lake
Transaction Log in Delta Lake
Implementing Schema Evolution and Table Updates with Delta
Handling Schema Changes and Evolution Using Delta
Introduction to Schema Evolution
Understanding Schema Evolution Challenges
Leveraging Delta Lake for Schema Evolution
Techniques for Handling Schema Changes
Best Practices for Schema Evolution
Conclusion
Key Terms
3. Data Ingestion and Extraction
Introduction
Structure
Ingesting Data into Databricks
Understanding the Importance of Data Ingestion
Choosing the Appropriate Data Ingestion Method
Identifying Key Considerations for Data Ingestion
Implementing Data Ingestion Best Practices
Implementing Efficient Data Extraction Techniques
Overview of Data Extraction Methods from Various Sources
Extracting Data from Databases Using Connectors and JDBC
Working with JDBC Connector
Best Practices for Efficient Extraction
Techniques for Extracting Data from Cloud Storage Platforms
AWS S3 Connector Sample Code
Implementing Efficient Data Extraction Techniques
Working with Structured Data Formats
Handling Unstructured and Semi-Structured data
Techniques for Extracting Data from APIs and Web Scraping
Conclusion
Key Terms
4. Data Transformation and ETL Processes
Introduction
Structure
Building Scalable Data Transformation Pipelines
Understanding the Importance of Data Transformation in Data Engineering
Designing Scalable and Efficient Data Transformation Pipelines
Techniques for Data Transformation in Databricks
Applying ETL Methodologies
Overview of the ETL Process and Its Key Components
Extracting Data from Various Sources for Transformation
Example: Extracting Data from a Relational Database
Applying Transformations and Business Logic to Prepare Data for Analysis
Example: Data Transformation with Apache Spark
Loading Transformed Data into Target Systems or Data Warehouses
Case Study: Loading Transformed Data into a Data Warehouse
Optimizing Data Processing and Performance in Databricks
Strategies for Optimizing Data Processing in Databricks
Leveraging Databricks Runtime Configurations for Performance Improvements
Performance Tuning for Data Transformation Operations in Databricks
Conclusion
Key Terms
5. Data Quality and Validation
Introduction
Structure
Ensuring Data Quality and Integrity in Databricks
Importance of Data Quality
Designing Data Quality Checks and Validation Processes
Techniques for Identifying and Handling Data Quality Issues
Implementing Data Profiling and Monitoring in Databricks
Implementing Data Validation Techniques and Rules
Overview of Data Validation Methodologies
Applying Data Validation Rules and Constraints
Techniques for Validating Data Completeness, Consistency, and Accuracy
Implementing Automated Data Validation Processes in Databricks
Case Study
Handling Data Anomalies and Outliers
Strategies for Managing and Correcting Data Anomalies and Outliers
Business Use Case: Sales Forecasting with Outliers
Conclusion
Multiple Choice Questions
Answers
6. Data Modeling and Storage
Introduction
Structure
Designing Effective Data Models
Importance of Data Modeling
Types of Data Modeling
Principles of Effective Data Modeling for Analytical Workloads
Schema-on-Read and Schema-on-Write models
Schema-on-Read
Schema-on-Write
Hybrid Approaches
Best Practices for Designing Data Models
Utilizing Structured and Unstructured Data Storage Options
Overview of Structured and Unstructured Data Storage Options
Structured Data Storage
Unstructured Data Storage
Hybrid Approaches
Utilizing Data Lakes and Data Warehouses
Data Lakes
Data Warehouses
Hybrid Approach
Choosing the Appropriate Storage Format
Data Partitioning and Optimization Strategies
Understanding Data Partitioning
Different Techniques for Partitioning Data
Benefits of Tailored Data Partitioning
Considerations for Data Partitioning
Strategies for Data compaction and Optimization in Databricks
Conclusion
Key Terms
7. Data Orchestration and Workflow Management
Introduction
Structure
Implementing Workflow Automation with Databricks
Overview of Workflow Automation
Introduction to Databricks Jobs and Notebooks for Workflow Automation
Designing and Implementing Automated Data Pipelines in Databricks
Managing Dependencies and Scheduling Data Pipelines
Understanding Dependencies Between Data Pipelines and Tasks
Techniques for Managing Dependencies in Databricks Workflows
Scheduling and Orchestrating Data Pipelines Using Databricks Jobs
Best Practices for Handling Complex Workflows and Task Dependencies
Monitoring and Error Handling in Workflow Execution
Strategies for Monitoring Workflow Execution
Implementing Logging and Alerting Mechanisms for Error Detection
Techniques for Handling Workflow Failures and Retries
Utilizing Databricks Monitoring and Debugging Tools for Workflow Optimization
Conclusion
Key Terms
8. Performance Tuning and Optimization
Introduction
Structure
Techniques for Optimizing Databricks Performance
Overview of Key Performance Optimization Techniques in Databricks
Utilizing Caching and Data Skipping for Faster Query Execution
Practical Use Case: E-commerce Sales Analysis
Techniques for Optimizing Data Shuffling and Data Serialization
Resource Management and Cluster Optimization
Understanding Resource Management in Databricks Clusters
Best Practices for Cluster Configuration and Sizing
Techniques for Optimizing Cluster Utilization and Cost Efficiency
Strategies for Managing Cluster Auto-Scaling and Workload Isolation
Identifying and Resolving Performance Bottlenecks
Techniques for Performance Profiling and Bottleneck Identification
Analyzing Query Execution Plans and Identifying Performance Hotspots
Strategies for Optimizing Complex Transformations and Joins
Conclusion
Key Terms
9. Scalability and Deployment Considerations
Introduction
Structure
Scaling Data Engineering Solutions in Databricks
Importance of Scalability in Data Engineering Solutions
Techniques for Scaling Data Pipelines and Processing Workflows
Horizontal and Vertical Scaling Options in Databricks
Horizontal Scaling (Scale-Out)
Vertical Scaling (Scale-Up)
Strategic Scaling in Databricks
Cloud Deployment Options and Considerations
Overview of Cloud Deployment Options for Databricks
Evaluating Different Cloud Providers and Services
Exploring Databricks with AWS
Handling Data Growth and Future-Proofing Strategies
Strategies for Managing and Accommodating Data Growth in Databricks
Techniques for Future-Proofing Data Engineering Solutions
Evaluating Emerging Technologies and Trends in Data Engineering
Conclusion
Key Terms
10. Data Security and Governance
Introduction
Structure
Unity Catalog
Understanding the Unity Catalog in Databricks
Overview of Metadata Management and Data Discovery in Unity Catalog
Leveraging Unity Catalog for Efficient Data Governance and Metadata Management
Ensuring Data Privacy and Compliance in Databricks
Importance of Data Privacy and Compliance
Implementing Privacy Measures and Techniques in Databricks
Ensuring Compliance with Data Protection Regulations
Techniques for Anonymization, Pseudonymization, and Data Masking
Implementing Access Controls and Data Encryption
Overview of Access Controls and Authorization in Databricks
Designing and Implementing Access Policies for Data Protection
Techniques for Encrypting Data at Rest and in Transit in Databricks
Implementing Key Management and Secure Credential Storage Practices
Data Governance Best Practices
Importance of Data Governance
Techniques for Data Lineage, Metadata Management, and Data Cataloging
Best Practices for Data Documentation, Stewardship, and Data Lifecycle Management
Conclusion
Key Terms
Last Words
Recap of the Journey
Continuous Learning and Upgradation
Encouragement for Practical Application
Final Motivational Note
Index

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