HHSHOP68

Cryptography and Network Security: Principles and Practice 8th Global Edition is a textbook that provides a practical survey of the principles and practices of cryptography and network security. It is designed for both academic and professional audiences, ideally suited for a one-semester undergraduate course in the subject.

Observability predominantly focuses on achieving a better understanding of the way software operates in the real world. Therefore, this book is most useful for software engineers responsible for developing production applications. However, anyone who supports the operation of software in production will also greatly benefit from the content in this book.

Additionally, managers of software delivery and operations teams who are interested in understanding how the practice of observability can benefit their organization will find value in this book, particularly in the chapters that focus on team dynamics, culture, and scale.

Anyone who helps teams deliver and operate production software (for example, product managers, support engineers, and stakeholders) and is curious about this new thing called “observability” and why people are talking about it should also find this book useful.

Data Engineering with Databricks Cookbook

What you will learn

• Perform data loading, ingestion, and processing with Apache Spark
• Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark
• Manage and optimize Delta tables with Apache Spark and Delta Lake APIs
• Use Spark Structured Streaming for real-time data processing
• Optimize Apache Spark application and Delta table query performance
• Implement DataOps and DevOps practices on Databricks
• Orchestrate data pipelines with Delta Live Tables and Databricks Workflows
• Implement data governance policies with Unity Catalog

Architecting Data and Machine Learning Platforms

You'll learn how to:

• Design a modern and secure cloud native or hybrid data analytics and machine learning platform
• Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform
• Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities
• Enable your business to make decisions in real time using streaming pipelines
• Build an MLOps platform to move to a predictive and prescriptive analytics approach

 Star Schema The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.

Data Engineering with Python

What you will learn

• Understand how data engineering supports data science workflows
• Discover how to extract data from files and databases and then clean, transform, and enrich it
• Configure processors for handling different file formats as well as both relational and NoSQL databases
• Find out how to implement a data pipeline and dashboard to visualize results
• Use staging and validation to check data before landing in the warehouse
• Build real-time pipelines with staging areas that perform validation and handle failures
• Get to grips with deploying pipelines in the production environment