a hierarchical structure that includes a broader range of google cloud services

1.        Based on Structure

1.1.     Structured Databases

1.1.1.   Relational Databases (SQL)

1.1.1.1.   Online transaction processing (OLTP)

1.1.1.1.1.   Cloud SQL

1.1.1.1.1.1.    MySQL

1.1.1.1.1.2.    PostgreSQL

1.1.1.1.1.3.    Oracle, SQL Server

1.1.1.1.2.   Cloud Spanner

1.1.1.1.2.1.    Horizontally Scalable Relational Database

1.1.1.1.2.2.    Global Consistency

1.1.1.2.    Online analytical processing (OLAP)

1.1.1.2.1.   Data Warehousing and Analytics

1.1.1.2.1.1.    Cloud BigQuery

1.1.1.2.1.1.1.     Fully Managed Data Warehouse

1.1.1.2.1.1.2.     Supports SQL Queries

1.1.1.2.1.1.3.     Designed for Large-scale Data Analysis

1.1.1.2.2.   Integration and Advanced Use-Cases

1.1.1.2.2.1.    Integration with Analytics Tools

1.1.1.2.2.1.1.     BigQuery for Analyzing Data from Databases

1.1.1.2.2.1.2.     Integration with AI/ML Services

1.1.1.2.2.2.    Real-time Data Handling

1.1.1.2.2.2.1.     Firestore for Real-time Applications

1.1.1.2.2.2.2.     Firebase Realtime Database for Syncing Across Devices

1.2.     Semi-Structured Databases

1.2.1.   . Document-Oriented Databases

1.2.1.1.   Cloud Firestore

1.2.1.1.1.    Real-time Database

1.2.1.1.2.    Supports complex queries and indexing

1.2.1.1.3.    Hierarchical data structure with collections and documents

1.2.1.2.   Cloud Datastore

1.2.1.2.1.    Scalable NoSQL database

1.2.1.2.2.    ACID transactions

1.2.1.2.3.    Built for automatic scaling and high availability

1.2.1.3.   Firebase Realtime Database

1.2.1.3.1.    JSON Tree Structure

1.2.1.3.2.    Real-time syncing across clients

1.2.1.3.3.    Simple and optimized for mobile and web applications

1.2.2.    Key-Value Stores

1.2.2.1.   Firebase Realtime Database (Can be used as a key-value store)

1.2.2.1.1.    Scalable key-value database

1.2.2.1.2.    Optimized for real-time updates

1.2.3.   Wide-Column (Column-Family) Stores

1.2.3.1.   Cloud Bigtable

1.2.3.1.1.    Massively scalable

1.2.3.1.2.    Ideal for time-series, IoT data, and analytical workloads

1.2.3.1.3.    Supports wide-column data structure with flexible schema

1.2.4.   Graph Databases

1.2.4.1.   Graph Databases (General category, not specific to Google)

1.2.4.1.1.    Optimized for managing relationships between entities

1.2.4.1.2.    Ideal for social networks, fraud detection, and recommendation engines

1.2.4.1.3.    Neo4j (Example, not part of Google Cloud)

1.2.5.   Some other Databases (General category, not specific to Google)

1.2.5.1.    Flexible schema, optimized for specific use cases.

1.2.5.2.    Scalable and handles large volumes of unstructured data.

1.2.5.3.   Categories:

1.2.5.3.1.    Document-Oriented: JSON-like documents, e.g., MongoDB.

1.2.5.3.2.    Key-Value Stores: Simple key-value pairs, e.g., Redis.

1.2.5.3.3.    Column-Family Stores: Tables with rows and dynamic columns, e.g., Cassandra.

1.2.5.3.4.    Graph Databases: Nodes and edges representing relationships, e.g., Neo4j.

1.3.     Unstructured Databases

1.3.1.   Object Storage: Amazon S3, Google Cloud Storage

1.3.2.   Blob Storage: Azure Blob Storage

2.        Based on Data Model

2.1.     Relational (SQL) Databases

2.1.1.   Online transaction processing (OLTP)

2.1.1.1.   Cloud SQL

2.1.1.1.1.    MySQL

2.1.1.1.2.    PostgreSQL

2.1.1.1.3.    Oracle, SQL Server

2.1.1.2.   Cloud Spanner

2.1.1.2.1.    Horizontally Scalable Relational Database

2.1.1.2.2.    Global Consistency

2.1.2.   Online analytical processing (OLAP)

2.1.2.1.   Data Warehousing and Analytics

2.1.2.1.1.   Cloud BigQuery

2.1.2.1.1.1.    Fully Managed Data Warehouse

2.1.2.1.1.2.    Supports SQL Queries

2.1.2.1.1.3.    Designed for Large-scale Data Analysis

2.1.2.2.   Integration and Advanced Use-Cases

2.1.2.2.1.   Integration with Analytics Tools

2.1.2.2.1.1.    BigQuery for Analyzing Data from Databases

2.1.2.2.1.2.    Integration with AI/ML Services

2.1.2.2.2.   Real-time Data Handling

2.1.2.2.2.1.    Firestore for Real-time Applications

2.1.2.2.2.2.    Firebase Realtime Database for Syncing Across Devices

2.2.     Non-Relational (NoSQL) Databases

2.2.1.   . Document-Oriented Databases

2.2.1.1.   Cloud Firestore

2.2.1.1.1.    Real-time Database

2.2.1.1.2.    Supports complex queries and indexing

2.2.1.1.3.    Hierarchical data structure with collections and documents

2.2.1.2.   Cloud Datastore

2.2.1.2.1.    Scalable NoSQL database

2.2.1.2.2.    ACID transactions

2.2.1.2.3.    Built for automatic scaling and high availability

2.2.1.3.   Firebase Realtime Database

2.2.1.3.1.    JSON Tree Structure

2.2.1.3.2.    Real-time syncing across clients

2.2.1.3.3.    Simple and optimized for mobile and web applications

2.2.2.    Key-Value Stores

2.2.2.1.   Firebase Realtime Database (Can be used as a key-value store)

2.2.2.1.1.    Scalable key-value database

2.2.2.1.2.    Optimized for real-time updates

2.2.3.   Wide-Column (Column-Family) Stores

2.2.3.1.   Cloud Bigtable

2.2.3.1.1.    Massively scalable

2.2.3.1.2.    Ideal for time-series, IoT data, and analytical workloads

2.2.3.1.3.    Supports wide-column data structure with flexible schema

2.2.4.   Graph Databases

2.2.4.1.   Graph Databases (General category, not specific to Google)

2.2.4.1.1.    Optimized for managing relationships between entities

2.2.4.1.2.    Ideal for social networks, fraud detection, and recommendation engines

2.2.4.1.3.    Neo4j (Example, not part of Google Cloud)

2.2.5.   Some other Databases (General category, not specific to Google)

2.2.5.1.    Flexible schema, optimized for specific use cases.

2.2.5.2.    Scalable and handles large volumes of unstructured data.

2.2.5.3.   Categories:

2.2.5.3.1.    Document-Oriented: JSON-like documents, e.g., MongoDB.

2.2.5.3.2.    Key-Value Stores: Simple key-value pairs, e.g., Redis.

2.2.5.3.3.    Column-Family Stores: Tables with rows and dynamic columns, e.g., Cassandra.

2.2.5.3.4.    Graph Databases: Nodes and edges representing relationships, e.g., Neo4j.

3.        Based on Deployment Model

3.1.     Public Cloud Databases

3.1.1.   Managed by third-party cloud providers.

3.1.2.   Examples: Amazon RDS, Google Cloud SQL, Azure SQL Database.

3.2.     Private Cloud Databases

3.2.1.   Hosted on private infrastructure.

3.2.2.   Provides more control over data and compliance.

3.3.     Hybrid Cloud Databases

3.3.1.   Combines public and private cloud features.

3.3.2.   Balances scalability with control and compliance.

4.        Based on Storage Type

4.1.     Block Storage

4.1.1.   High performance, used for databases like SQL

4.1.2.   Examples: AWS EBS, Google Persistent Disk

4.2.     File Storage

4.2.1.   Shared storage, suitable for applications needing file systems

4.2.2.   Examples: AWS EFS, Google Filestore.

4.3.     Object Storage

4.3.1.   Standard Storage / Hot Storage (Primary databases, real-time analytics)

4.3.1.1.   Fast access, low latency

4.3.1.2.   It can be stored for at least 1-7 days

4.3.1.3.   Examples: AWS Aurora, Google BigQuery

4.3.2.   Nearline Storage/Warm Storage (Near real-time data processing, less frequent queries)

4.3.2.1.   Moderately fast, lower cost than hot storage

4.3.2.2.   It can be stored for at least 30 days

4.3.2.3.   Examples: AWS S3 Standard-IA, Google Cloud Nearline

4.3.3.   Coldline Storage (Backup, infrequent access)

4.3.3.1.   Slow retrieval times, lowest cost

4.3.3.2.   It can be stored for at least 90 days

4.3.3.3.   Examples: AWS Glacier, Google Cloud Coldline

4.3.4.   Archive Storage (archival)

4.3.4.1.   Scalable storage for unstructured data

4.3.4.2.   It can be stored for at least 365 days.

4.3.4.3.   Examples: AWS S3, Google Cloud Storage

2.1.       Cloud Pub/Sub (Messaging Service)

2.1.1.     Real-time Messaging

2.1.2.     Event-driven Applications

2.2.       Cloud Dataflow (Stream and Batch Data Processing)

2.2.1.     Unified Stream and Batch Processing

2.2.2.     Apache Beam Runner

2.3.       Cloud Dataproc (Managed Spark and Hadoop)

2.3.1.     Managed Hadoop/Spark Ecosystem

2.3.2.     Scalable and Cost-effective Data Processing

2.4.       Cloud Dataprep (Data Preparation)

2.4.1.     Data Cleaning and Preparation

2.4.2.     Visual Data Exploration

2.5.      Cloud Composer (Workflow Orchestration)

2.5.1.     Managed Apache Airflow

2.5.2.     Workflow Automation