Data Management

Cloud Adoption

Cloud strategy and migration involve planning and executing the transition of IT infrastructure, applications, and data from on-premises or legacy systems to the cloud.

1. Public Cloud (AWS, Azure, GCP)
2. Private Cloud (On-premise, VMware, OpenStack)
3. Hybrid Cloud (Combination of public & private)
4. Multi-Cloud (Using multiple cloud providers)

1. Rehost (Lift and Shift): Moving applications as-is
2. Replatform: Making minor optimizations
3. Refactor: Redesigning for cloud-native architecture
4. Repurchase: Switching to SaaS solutions
5. Retire: Decommissioning obsolete systems
6. Retain: Keeping some systems on-premise

1. Implementing FinOps for cost tracking
2. Using cloud-native automation tools to optimize spending

1. Assessment: Identify applications, dependencies, and business goals.
2. Planning: Choose a cloud provider, migration strategy, and architecture.
3. Planning: Choose a cloud provider, migration strategy, and architecture.
4. Optimization: Fine-tune performance, security, and cost-efficiency.

AWS Infrastructure

AWS architecture involves designing scalable, secure, and cost-effective cloud solutions following best practices.

1. Scalability & Elasticity: Use Auto Scaling, Load Balancers, and EC2 Spot Instances. Multi-AZ deployments with Route 53, RDS Multi-AZ, and S3 Cross-Region Replication.
2. High Availability & Fault Tolerance: Paying taxes as required by law (e.g., advance and withholding tax).

1. Identity & Access Management (IAM roles and policies).
2. AWS KMS (Key Management Service) for encryption.
3. AWS WAF & Shield for protection against cyber threats

1. API Gateway, Lambda, DynamoDB for event-driven architectures
2. Amazon ECS & Kubernetes for containerized applications

1. Compute: EC2, Lambda, Fargate, Auto Scaling
2. Storage: S3, EBS, Glacier, FSx
3. Databases: RDS, DynamoDB, Aurora, Redshift
4. Networking: VPC, Route 53, CloudFront, API Gateway
5. Security & Compliance: IAM, GuardDuty, Security Hub, AWS Config

Database Architecture

Database design and administration ensure data is structured, stored, and retrieved efficiently while maintaining security and performance.

1. Normalization & Indexing: Optimize database structure for performance.
2. ACID Compliance vs. BASE:

ACID (Atomicity, Consistency, Isolation, Durability) for SQL databases

BASE (Basically Available, Soft state, Eventual consistency) for NoSQL databases

1. Backup & Recovery: Automate backups using AWS Backup, RDS Snapshots.
2. Performance Tuning:

Use query optimization, indexing, and partitioning.

Implement read replicas & caching (Redis, Memcached).

3. Security Best Practices:

Use IAM roles & access controls.

Encrypt data at rest and in transit with KMS & TLS/SSL.

1. Relational Databases (SQL): MySQL, PostgreSQL, Microsoft SQL Server, Amazon Aurora
2. NoSQL Databases: DynamoDB, MongoDB, Cassandra, CouchDB
3. Data Warehousing: Redshift, Snowflake, BigQuery

Cost Optimization

Cloud cost optimization focuses on reducing expenses while maintaining performance and reliability.

1. Rightsizing Resources: Adjust compute, storage, and database sizes to actual usage.
2. Using Reserved Instances & Savings Plans: Commit to long-term plans for lower rates.
3. Auto Scaling & Spot Instances: Use Auto Scaling Groups and EC2 Spot Instances to reduce idle costs.
4. Storage Lifecycle Policies:

Move infrequent data to S3 Glacier.

Delete obsolete snapshots and backups.

5. Serverless Architectures: Use Lambda, Fargate, and API Gateway to eliminate unnecessary resource usage.

1. AWS Cost Explorer & Compute Optimizer
2. Azure Cost Management
3. GCP Billing Reports
4. FinOps frameworks for continuous cost monitoring

Security & Compliance

Security and compliance focus on protecting cloud environments, data, and applications while adhering to regulatory standards.

1. Identity & Access Management (IAM): Least privilege access control.
2. Encryption & Data Protection:

   Use AWS KMS, Azure Key Vault, GCP Cloud KMS for encryption.

   Enable TLS/SSL for data in transit.

3. Network Security:

   Use VPC, Security Groups, and Firewalls.

   Implement AWS WAF & Shield for DDoS protection.

4. Threat Detection & Monitoring:

   AWS GuardDuty, Security Hub, and SIEM tools (Splunk, IBM QRadar).

   Automated incident response using AWS Lambda and Step Functions.

1. ISO 27001 (Information Security)
2. NIST Cybersecurity Framework
3. GDPR (Data Privacy in Europe)
4. HIPAA (Healthcare Security & Privacy in the US)
5. PCI-DSS (Credit Card Security Compliance)

Data Intelligence

Data analysis and big data focus on processing, analyzing, and visualizing large datasets for insights.

1. ETL Pipelines: Use AWS Glue, Apache NiFi, or Apache Airflow.
2. Real-Time Data Streaming: Kafka, AWS Kinesis, Apache Flink
3. Big Data Storage:

   AWS S3, Google Cloud Storage, Hadoop Distributed File System (HDFS).

   Data warehousing with Amazon Redshift, BigQuery, Snowflake.

4. Data Visualization & Business Intelligence:

   Use Tableau, Power BI, AWS QuickSight for dashboards.

   Build predictive models with SageMaker, TensorFlow, and PyTorch.

1. Apache Hadoop & Spark: Distributed data processing.
2. AWS EMR & Glue: Managed big data processing.
3. Databricks: Unified data and AI analytics platform.