Database Design and Modeling with Google Cloud Learn Database Design and Development to Take Your Data to Applications, Analytics, and AI
Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needs Key Features Familiarize yourself with business and technical considerations involved in modeling the right database Take your data to applications, analytics, and AI with...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited
2023.
Birmingham, England : [2023] |
| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009790334706719 |
Tabla de Contenidos:
- Preface
- Part 1: Database Model: Business and Technical Design Considerations
- 1
- Data, Databases, and Design
- Data
- Databases
- A teeny-tiny bit about the evolution of databases
- DBMS
- Database design
- Data modeling
- Database modeling
- Considerations for a good database design
- Business aspect
- Ingestion
- Technical aspect
- Choosing the right database
- Relational database
- NoSQL database
- Summary
- 2
- Handling Data on the Cloud
- Types of cloud services
- Use case categories
- The benefits of cloud computing
- Data applications on cloud
- Storage
- Backup and disaster recovery
- Analytics and insights
- Application development
- User experience and personalization
- Managed, unmanaged, and database as a service
- Managed databases
- Unmanaged databases
- Database as a service
- Cloud database considerations
- A quick follow-up
- Summary
- Part 2: Structured Data
- 3
- Database Modeling for Structured Data
- Structured data
- Rows and columns
- Transactional applications
- Analytical applications
- Using an RDBMS for structured data
- Atomicity
- Consistency
- Isolation
- Durability
- Considerations for your RDBMS
- Structured query language
- Sample SQL queries
- Summary
- 4
- Setting Up a Fully Managed RDBMS
- Fully managed databases
- Fully managed RDBMS
- Cloud SQL
- Setting up and configuring a fully managed RDBMS
- Creating a Cloud SQL instance for MySQL
- Connecting to the instance
- Creating a database
- Creating a table
- Inserting values
- Querying values
- Creating an application with the Cloud database
- Configuring the Cloud Functions service account
- Creating a Cloud Function
- Operational aspects of cloud relational databases
- Migration
- Monitoring
- Query Insights
- Security
- Summary
- 5
- Designing an Analytical Data Warehouse.
- Understanding how data warehouses are different from databases
- Significance of ETL in data warehouse
- Learning about BigQuery
- Features of BigQuery
- Setting up and configuring a fully managed data warehouse with BigQuery
- Enabling BigQuery from the console
- Creating a BigQuery dataset
- Using an existing public dataset
- Creating a table in the dataset
- Performing simple analytics
- Summary of operational aspects and design considerations
- Summary
- Part 3: Semi-Structured, Unstructured Data, and NoSQL Design
- 6
- Designing for Semi-Structured Data
- Semi-structured data
- Pros and cons of semi-structured data
- Use cases of semi-structured data
- NoSQL for semi-structured data
- Data structures supported by NoSQL databases
- Firestore and its features
- Setting up Firestore
- Collection
- Document
- Subcollection
- Security
- Client libraries and APIs
- Indexing
- Single-field index
- Composite index
- Collection group query
- Data model considerations
- Hierarchical format
- Denormalized format
- Easy querying with RunQuery API
- API endpoint and method
- The parent parameter
- JSON body format
- StructuredQuery
- The from clause
- The where clause
- Putting the pieces together
- Implementing RunQuery API programmatically
- Summary
- 7
- Unstructured Data Management
- Use cases
- Processing unstructured data
- Storage options in Google Cloud
- Cloud Storage, classes, and features
- Unstructured data storage with BigQuery
- External sources
- External connections
- Unstructured data analytics with BigQuery
- Summary
- Part 4: DevOps and Databases
- 8
- DevOps and Databases
- Upgrades, updates, and patching
- Security, privacy, and encryption
- Replication and availability
- Scalability
- Performance and throughput
- SLA, SLI, and SLO
- Data federation.
- Continuous integration/continuous delivery (CI/CD)
- Migrating to cloud databases
- Database Migration Service
- System, query, and performance insights
- Summary
- Part 5: DevOps and Databases
- 9
- Data to AI - Modeling Your Databases for Analytics and ML
- Modeling considerations for analytics, AI, and ML
- Data to AI
- Google Cloud ETL services
- Google Cloud Dataflow at a glance
- Real-world use cases for Google Cloud Dataflow
- Step-by-step guide to Google Cloud Dataflow
- Taking your data to AI
- Summary
- 10
- Looking Ahead - Designing for LLM Applications
- Capturing the evolution of LLMs
- Getting started with LLMs
- Understanding the underlying principles of LLMs
- Comparing real-world applications of LLMs and traditional analytics
- Understanding the differences in data modeling for traditional analytics and LLMs
- Data model design considerations for applications that use LLMs
- Learning about data modeling principles and techniques
- Ethical and responsible practices
- Hands-on time - building an LLM application
- Step 1 - create a table
- Step 2 - insert data into the table
- Step 3 - create an external connection for BigQuery to access the Vertex AI model
- Step 4 - grant permissions to the service account to access the Vertex AI service
- Step 5 - create the remote model in BigQuery
- Step 6 - query the dataset
- Step 7 - generate text (create an LLM application) using only SQL
- Vector databases
- Summary
- Onward and upward!
- Index
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