Analytics Storage and AI, Data Prep and Data Lakes with Google Cloud

Overview

Explore Google Cloud's innovations for analytics storage and AI, focusing on data preparation, data lakes, and unified data lakehouses using open formats like Apache Iceberg to optimize performance and address common challenges.

Syllabus

• Introduction to Google Cloud for Analytics
Overview of Google Cloud services for analytics storage
Key benefits of using Google Cloud for AI and data storage
• Data Storage Innovations on Google Cloud
Introduction to data lakes and data lakehouses
Comparison of traditional data warehouses and data lakes
• Open Formats for Data Lakes
Understanding Apache Iceberg
Benefits of open data formats
Implementing Apache Iceberg with Google Cloud Storage
• Data Preparation with Google Cloud
Tools for data ingestion and transformation
Google Cloud Dataflow and Dataprep overview
Best practices for data cleansing and transformation
• Creating and Managing Data Lakes on Google Cloud
Designing scalable and efficient data lakes
Integration with BigQuery and other Google Cloud services
• Performance Optimization for Data Lakes
Strategies to improve data lake performance
Caching, indexing, and partitioning techniques
• Unified Data Lakehouses
Concept and benefits of unified data lakehouses
Building a data lakehouse architecture using Google Cloud
• AI and Machine Learning Integration
Leveraging Google Cloud AI tools for data lakes
Use cases for AI in analytics storage
• Common Challenges and Solutions
Addressing data consistency and quality issues
Security and access control in data lakes
• Case Studies and Real-World Applications
Examining successful implementations
Lessons learned and best practices
• Course Summary and Future Trends
Recap of key learning points
Emerging trends in analytics storage and AI on Google Cloud

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Subjects

Programming