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Reference Solutions & Architectures

Proven patterns for ETL (batch & streaming), on-prem → cloud migrations across AWS/Azure/GCP, and BI flows from data to decisions.

Aligned to TOGAF architecture layers, governed with PMBOK, executed via Agile/Scrum.

ETL Patterns

Batch pipelines for reliability & cost; streaming for low-latency insights with CDC.

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Cloud Migration

Landing zone foundations, security & networking, and wave-based application/data cutovers.

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Reporting & BI

Raw → curated → warehouse → semantic → dashboards with governed self-service.

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ETL: Batch vs Real-time (CDC/Kafka)

Pick the right ingestion style for your use case. Batch for periodic loads and reconciliations; real-time/CDC for reactive apps and fresh analytics.

Batch Pipeline

Typical: Airflow/ADF · dbt/Spark · S3/ADLS/GCS · Snowflake/BigQuery/Redshift/Teradata.

Streaming / CDC Pipeline

Low-latency for apps + durable sink to lake/warehouse for consistency & BI.

On-prem → Cloud Migration (AWS / Azure / GCP)

Start with a secure landing zone, identity, and network. Migrate data and apps in waves: rehost, re-platform, or refactor to cloud-native.

AWS Variant

Control Tower, VPC, TGW; IAM, KMS, GuardDuty
Data: S3 raw/curated, Glue, Lake Formation, Athena
DW: Redshift or Snowflake on AWS
Streaming: MSK/Kinesis; Proc: EMR/Spark

Azure Variant

CAF landing zone; VNets hub-spoke; Private Endpoints
Data: ADLS Gen2, ADF, Synapse/Databricks
DW: Synapse SQL, Snowflake on Azure
Streaming: Event Hubs; Proc: Databricks/Spark
Security: Entra ID, Key Vault, Defender for Cloud

GCP Variant

Landing zone; VPC Service Controls
Data: Cloud Storage, Dataflow (Beam)
DW: BigQuery (with semantic via Looker)
Streaming: Pub/Sub; Proc: Dataproc/Spark
Security: IAM, CMEK, SCC, Cloud Armor

Delivery Approach

Discovery & TCO → pilot → wave-based cutovers with rollback plans, blue/green releases, and performance benchmarks.

Governance: PMBOK (scope, schedule, cost, quality, risk, comms). Execution: Agile sprints.

Security & Compliance

Least privilege; private endpoints; encryption in transit/at rest; data classification; audit trails & retention; FinOps guardrails.

Reporting & BI: From Data to Decisions

Layered flow ensures trusted data, consistent KPIs, and scalable self-service analytics.

Analytics Flow

dbt/Spark modeling · Power BI/Tableau/Looker semantic layers · governed datasets for self-service.

Operational Considerations

Data quality (rules, expectations, SLAs) and lineage/catalog
Role-based access, PII protection, row-level security
Cost controls (storage tiers, query quotas, lifecycle policies)
CI/CD, infrastructure as code, environment parity (dev/test/prod)
Monitoring: freshness, failures, performance; ITSM (incident/problem/change)