Data Architecture

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Data Architecture by Mind Map: Data Architecture

1. Industry Sectors

1.1. Utility

1.2. Healthcare

1.3. Insurance

1.4. Automotive

1.5. Telecomm

1.6. Banking

2. Platform Architecture

2.1. Platform Architecture / Infrastructure

2.1.1. Azure

2.1.2. AWS

2.1.3. GCP

2.2. Data Security

2.2.1. Data Classification

2.2.2. Data Masking Patterns

2.2.3. Authentication

2.2.4. Authorisation

2.2.5. Network security

2.2.6. Data Encryption

2.2.7. Monitoring & logging

2.2.8. Data Privacy

2.3. Code Management

2.3.1. CICD

2.4. Infrastructure Management

2.4.1. Performance / Scale

2.4.2. High Availability / Fault Tolerance

2.4.3. Cost Management

2.4.4. IaC

2.4.5. Virtual Networks

3. Data Ingestion

3.1. Real Time

3.1.1. IoT

3.1.2. Events/stream

3.1.3. Messages

3.2. Batch

3.2.1. Data Extraction

3.2.1.1. Database connectors

3.2.1.2. File based patterns

3.2.1.3. CDC

3.2.1.4. Replication

3.2.2. Data Transformation

3.2.2.1. ETL Patterns

3.2.2.2. ELT Patterns

3.3. Near real time

4. Data Management

4.1. Data Governance

4.2. Data Quality process management

4.3. Meta data - Business Glossary and Data Lineage

5. Data Models

5.1. Industry Reference Conceptual and Logical Models

5.2. Data Vault Modeling

5.2.1. Raw vault

5.2.2. Business Vault

5.3. Traditional techniques

5.3.1. Star Schema

5.3.2. Snowflake

6. Data Analytics

6.1. Machine Learning

6.2. Cognitive Services

6.3. Artificial Intelligence

7. Use Cases

7.1. Industry Specific Use Case

7.2. Customer Related use case

7.2.1. Single View of Customer

7.2.2. Customer Journey Event Store

7.2.3. Social Media Analytics

8. Data Storage

8.1. Operational Data Store

8.2. Data warehouse

8.3. Data Hub

8.3.1. Reference Data Hub

8.3.2. Integration Data Hub

8.3.3. Analytics Data Hub

8.4. Data Lake

8.5. Data Lakehouse

8.6. Geospatial data store

9. Data Consumption

9.1. BI Reporting / Dashboard

9.1.1. Data Preparation

9.1.2. Data Modeling

9.1.3. Data Visualisation

9.1.4. Analyse the data

9.1.5. Deploy & Maintain

9.2. Data Sharing

9.2.1. APIs

9.2.2. Downstream extracts

9.3. Data discovery

9.4. Cubes/OLAP

9.5. Data Virutalisation

10. Others

10.1. Decision trees

10.1.1. Rationale

10.1.2. Industry / Sector Specific

10.2. Design Patterns

10.3. Offerings

10.3.1. Hyper Scalers

10.3.2. Third Party

10.3.2.1. Informatica

10.3.2.2. Talend

10.3.2.3. Snowflake

10.3.3. On prem

10.4. Best Practices