HHSHOP68

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

The third edition of Understanding the Linux Kernel takes you on a guided tour of the most significant data structures, algorithms, and programming tricks used in the kernel. Probing beyond superficial features, the authors offer valuable insights to people who want to know how things really work inside their machine. Important Intel-specific features are discussed. Relevant segments of code are dissected line by line. But the book covers more than just the functioning of the code; it explains the theoretical underpinnings of why Linux does things the way it does.

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With Hands-On Programming with R, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.