Future Proof the Environment and Tech Stack

Abstract

A modern Data Office requires a cloud-native, modular, lakehouse-based architecture.
Key layers include ingestion, lakehouse, cataloging, quality, BI, and security.
Preferred tools include Databricks, dbt, Collibra/Purview, Great Expectations, and Tableau (or Power BI as an optional enhancement).
Execution takes 24 months across foundation, pilot, scale, and optimization phases.
Environment should support governance, analytics, AI, and compliance equally.
Total 2-year cost: $2M–$3M, excluding staffing.

Objective of the Environment Strategy

To build a robust, secure, and cloud-native data environment that supports enterprise data governance, analytics, quality management, and real-time operations, while remaining modular and adaptable to emerging technologies and AI initiatives.

Benefits

End-to-end visibility and control over enterprise data
Easier collaboration between IT, business, and data teams
Higher data quality and trust across business-critical domains
Foundation for AI, predictive maintenance, connected vehicle analytics
Regulatory and cybersecurity compliance at scale
Vendor-agnostic and modular design for future tech evolution

Ideal Environment Principles

Modular: Easily replaceable components (e.g., switch from Airbyte to Informatica if scaling)
Open Standards: Prefer open-source (dbt, Airflow, Great Expectations) where practical
Security First: Central identity access management (Azure AD / AWS IAM) + encryption
Federated Governance: Allow business units to contribute to metadata and policies
AI-Ready: Tag and prepare datasets for model training, monitoring, and drift detection

Modernized Architecture with Existing Stack

Below is how to integrate and evolve your current tools into a robust, future-proof architecture:

1. Data Ingestion & Integration Layer

Current Tools:

Informatica: ETL for batch loads
Cleo: B2B/EDI file transfer
Apigee: API gateway

Recommendations:

Keep Informatica for legacy ETL and large-volume batch processing
Modernize by introducing dbt + Airflow/Prefect for ELT and transformation modularity
Use AWS Glue + Lambda for event-driven or near-real-time ingestion
Standardize API-based ingestion through Apigee for all external-facing data flows
Use Cleo for secure B2B data, but plan eventual migration to REST+JSON or SFTP+AWS Transfer Family

2. Storage & Processing Layer

Current Tools:

Snowflake: Scalable cloud warehouse
PostgreSQL: Operational databases

Recommendations:

Continue using Snowflake as your primary enterprise data warehouse
Use PostgreSQL for operational data and staging areas
Create a logical data lake by integrating Snowflake with S3 and optionally layering Apache Iceberg for future data lake flexibility
If multi-cloud or on-prem integration is needed, consider Starburst/Trino for federated querying

3. Transformation & Data Engineering Layer

Current Tools:

AWS Glue + Lambda
Informatica

Recommendations:

Introduce dbt (Data Build Tool) on top of Snowflake for declarative, modular SQL transformations
Use Airflow or Prefect to orchestrate dbt, Glue jobs, and notifications
Retain Glue for Spark-based jobs, but refactor toward dbt when feasible (especially for team collaboration, versioning, and testability)

4. Governance & Metadata Layer

Current Tools:

None at the moment, RFQ in review process (Informatica and Collivra are top contenders as of May 2025)

Recommendations:

Pick the top choice from Informatica, Collibra, AWs or Sales Force as a unified data catalog and governance layer
Auto-ingest metadata from Snowflake, PostgreSQL, Glue, dbt, and APIs
Define business glossaries, tag PII data, and integrate with Dynatrace for observability/lineage

5. Data Quality & Observability Layer

Current Tools:

Dynatrace (mainly app observability)

Recommendations:

Continue using Dynatrace for system and API monitoring
Introduce Great Expectations or Soda.io for data quality checks, or build on Sales Force / Informatica offerings depending on the choice of the ongoing RFQ
Integrate with dbt and Snowflake to monitor data freshness, completeness, and anomalies
Long term: unify dashboards in Power BI or Grafana for both data + system observability

6. Access & Analytics Layer

Current Tools:

Tableau (primary BI platform)

Recommendations:

Continue using Tableau, fully integrated with Snowflake for governed, performant analytics.
Optionally pilot Power BI for Microsoft-native integration and cost efficiency (especially if bundled with Microsoft 365 or Purview).
Enable self-service analytics via role-based access to curated views and datasets in Snowflake.
Embed dashboards into apps via Tableau REST API or Apigee → leverage dynamic filters and row-level security.
For field and mobile use cases, consider lightweight embedded analytics or Tableau Mobile as part of the delivery strategy.

Value-Add Smart Suggestions

Need	Smart Suggestion
Transformation Scalability	Add dbt Cloud for analytics engineering best practices, with version control and CI/CD
API Governance	Use Apigee + AWS API Gateway for full lifecycle management of internal and external APIs
Real-Time Analytics	Add Kinesis (or Kafka if vendor-neutral) to enable vehicle telemetry or service event streams
Data Science / AI	Use Snowflake’s Snowpark or SageMaker Studio for AI model development on governed datasets
Lineage & Collaboration	Informatica, Sales Force, AWS or Collibra integration with dbt, Glue, and Snowflake for end-to-end lineage and steward workflows
BI Consolidation	Use Tableau as the core BI layer. Optionally evaluate Power BI if pursuing full Microsoft stack.

Hybrid Data Architecture with Data Office Integration

flowchart TD
    %% Source Systems
    A1[ERP / CRM / Legacy Systems]
    A2[IOT Devices / Vehicle Sensors]
    A3["3rd Party APIs / Partners (via Apigee)"]
    A4["Flat Files / FTP (via Cleo)"]

    %% Ingestion Layer
    A1 --> B1[Informatica ETL Jobs]
    A2 --> B2[AWS Lambda Functions]
    A3 --> B3[Apigee Gateway]
    A4 --> B4[Cleo Connectors]

    B1 --> C1["Landing Zone (S3 / RDS / Postgres)"]
    B2 --> C1
    B3 --> C1
    B4 --> C1

    %% Processing & Transformation
    C1 --> D1[AWS Glue ETL]
    C1 --> D2[Lambda Transformations]
    D1 --> E1[Snowflake Data Warehouse]
    D2 --> E1

    %% Serving & Access
    E1 --> F1[Power BI / Tableau]
    E1 --> F2[Custom Apps / Dashboards]
    E1 --> F3["Data Science Sandbox (Jupyter, SageMaker)"]

    %% Governance & Metadata
    E1 --> G1[Microsoft Purview / Atlan]
    C1 --> G1
    G1 --> G2[Data Catalog & Lineage]
    G1 --> G3["Data Classification (Level 0/1/2)"]
    G1 --> G4["Data Access Policies (Snowflake Roles)"]

    %% Monitoring & Quality
    E1 --> H1[Dynatrace / Soda.io]
    D1 --> H1

    %% Compliance & Privacy
    G3 --> I1["CCPA / GDPR Request Workflow (ServiceNow)"]
    I1 --> D1

Key Concepts:

Hybrid Ingestion: Mix of batch (Informatica, Cleo) and streaming/serverless (Lambda, Apigee)
Modern Warehouse: Snowflake as a central cloud warehouse
Flexible Transformation: Using Glue for ETL and Lambda for event-driven cases
Governance Layer: Purview or Atlan integrates with both raw and modeled data
Compliance Hooks: PII tagging and deletion/redaction triggered from Purview to Glue
Monitoring: Dynatrace + optional Soda.io for quality and observability

Execution Timeline (Phased Upgrade)

Phase	Months	Focus
Phase 1	0–3	Introduce dbt + Airflow; formalize metadata strategy
Phase 2	3–6	Integrate catalog (Purview/Atlan); enable observability with Soda/GE
Phase 3	6–12	Shift legacy pipelines to dbt; enrich lineage, implement PII tagging
Phase 4	12–18	Enable ML, real-time pipelines; build self-service BI & API ecosystems

Estimated Additional Cost (First 2 Years)

Category	Add-On Tools	Annual Estimate
dbt Cloud (Team Plan)	Transformation	$20K–$50K
Atlan / Purview	Governance & Catalog	$80K–$200K
Soda / Great Expectations	Data Quality	$20K–$40K
Tableau (existing license)	Analytics	Already licensed
Optional: Power BI	Analytics (if added)	$50K–$100K
Airflow (managed)	Orchestration	$10K–$30K
Training & Integration	Vendor setup, team enablement	~$100K
Total New Spend (Year 1–2)	–	$300K–$500K/year on top of existing stack

Risks & Remedies

Risks:

Legacy systems blocking integration or standardization
Downtime or pipeline failures with growing complexity
Escalating cloud storage and compute costs

Remedies:

Incrementally retire legacy tools by building adapters and transition plans
Monitor pipeline SLAs and introduce retry/failover mechanisms
Implement FinOps practices and cost alerts for Snowflake, AWS, etc.