Governed Analytics: End-to-End Analytics Platform with Snowflake, AWS, GitLab CI/CD, dbt, Fivetran, Census, and Power BI

Case Study Summary

Client: A B2B SaaS Company

Users Served: 40+ business users, 2 analysts/engineers

Impact Metrics & Outcomes:

Speed to insight — governed pipeline and live semantic models shorten time from data to decisions.
Reliability — CI/CD + dbt tests and zero-copy clones keep releases and dashboards stable.
Confidence — one semantic model and version-controlled changes ensure trusted, consistent metrics.
Security — private-by-default network (no inbound SSH; S3 stays inside VPC via gateway endpoints).

Business Context & Problem

Each team had “its own truth”, ad-hoc analytics, excel reports. No reliable data due to lack of governance and verision controlled releases. Power BI pipelines were not reuseable.

Core pain points: inconsistent metrics and slow delivery.

Approach Overview

We deployed a modular stack that balances speed, governance, and cost:

Snowflake for elastic compute/storage with environment isolation through zero-copy clone (instant, metadata-only copies).
Dev/QA/Prod via Snowflake zero-copy clones for safe iteration.
AWS S3 as a landing zone and data lake via Snowflake storage integration (IAM role with external ID; no embedded keys).
Private VPC with bastion and S3 gateway endpoint; no inbound SSH or public S3 egress from runners.
GitLab CI/CD on EC2 in a private VPC behind a bastion (no inbound SSH).
dbt core for version-controlled transformations and tests in pipelines.
Fivetran to ingest SaaS/db sources with managed ELT.
Census for reverse ETL back to CRM/ops tools.
Power BI as the governed semantic model consumed via live connections.
FinOps: Snowflake Budgets (alerts/webhooks), Resource Monitors, and account usage cost dashboards.

High-Level Architecture

flowchart TB
    subgraph Ingestion["Ingestion & Landing"]
        S3@{shape: rounded, label: "S3 External Stage"}
    end

    subgraph Snowflake["Snowflake"]
        RAW@{shape: rounded, label: "Raw Data"}
        STG@{shape: rounded, label: "Staging Layer"}
        INT@{shape: rounded, label: "Intermediate Layer"}
        MARTS@{shape: rounded, label: "Mart Layer"}
        ExtStage@{shape: rounded, label: "Storage Integration"}
    end

    subgraph CICD["CI/CD & Orchestration"]
        GitLab@{shape: rounded, label: "GitLab"}
        Runner@{shape: rounded, label: "EC2 Runner"}
    end

    subgraph ELT["ELT & Activation"]
        Fivetran@{shape: rounded, label: "Fivetran"}
        Census@{shape: rounded, label: "Census - Reverse ETL"}
    end

    subgraph BI["Analytics"]
        PowerBI@{shape: rounded, label: "Power BI Semantic Models"}
    end

    %% Data Flow Connections
    Fivetran e1@-->|ELT Ingestion| RAW
    S3 e2@-->|External Stage| ExtStage
    ExtStage e3@--> RAW
    RAW e4@-->|Create views| STG
    STG e5@-->|Cleaning & Build reusable components| INT
    INT e6@-->|Build final consumption layer| MARTS

    GitLab e7@-->|CI/CD Pipeline| Runner
    Runner e8@-->|Orchestrate| Fivetran
    Runner e9@-->|Deploy & Test| Snowflake
    MARTS e10@-->|Reverse ETL| Census
    MARTS e11@-->|Live Connection| PowerBI

    e1@{ animation: fast }
    e2@{ animation: fast }
    e3@{ animation: fast }
    e4@{ animation: fast }
    e5@{ animation: fast }
    e6@{ animation: fast }
    e7@{ animation: fast }
    e8@{ animation: fast }
    e9@{ animation: fast }
    e10@{ animation: fast }
    e11@{ animation: fast }

Data lands in S3 or via connectors -> transformations run in Snowflake (dbt) -> CI guards quality before deploy -> governed models power BI -> activation pushes “last mile” data to operational tools.

Step-by-Step Execution

Snowflake Foundations & Environments
- Dev/QA/Prod via zero-copy clone for instant environment creation and rollback
- Clones are metadata-only and don’t duplicate data files—perfect for safe, fast testing.
CI/CD with GitLab on EC2
- Autoscaling GitLab Runner on EC2 (Docker executor) to spin up ephemeral build agents.
- Pipeline stages:
  1. ELT triggers (call Fivetran API to sync critical connectors).
  2. Custom Python scripts + S3 ingestion
  3. dbt build + tests (unit, unique, not_null, accepted_values, relationships, etc.)
  4. Revese ETL to CRM/ops tools
  5. Docs generation
Transformations & Data Quality with dbt
- Build staging → intermediate → marts
- Generic tests (unique, not_null, accepted_values, relationships).
- Model build based on tests pass.
- Publish dbt docs as an artifact for lineage.
Ingestion with Fivetran & Reverse ETL with Census
- Fivetran covers databases (e.g., Postgres), SaaS apps, and files with managed schemas and incremental syncs.
- Census syncs curated marts back to CRM, marketing, CS, and finance systems—solving the “last mile” activation.
Power BI as the Semantic Layer
- Publish semantic models and let report authors connect.
FinOps: Cost Controls & Observability
- Budgets and resource monitors: set monthly credit limits for account or object groups. Alerts via email.
- Usage dashboards: observability of core resources.
- Snowsight cost management: quick org-level views.

Results

Governed velocity: feature branches promoted through dev→QA→prod with deterministic tests and clones. Releases no longer break dashboards.
Private-by-default network: no inbound SSH. S3 access stays inside the VPC. Fewer egress paths to audit.
Predictable costs: proactive alerts at 60/80/100% of monthly budgets. Warehouse auto-suspend. BI dashboards show top resources.
Business impact: one semantic model powering many Power BI reports. Operational teams act on trusted metrics and receive synced segments in the tools they already use.

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