Stop Guessing. Build a Data Platform That Actually Works
Mars Innovation Technology builds modern cloud data lakehouses on Snowflake, Databricks or BigQuery — with production-grade ELT pipelines, a governed semantic layer, and self-service dashboards that give every team access to trusted data.
Production-grade ELT pipelines from any source — SaaS, databases, APIs, files — to your data lakehouse.
Governed semantic layer (dbt) ensures consistent metric definitions across every dashboard and AI model.
Self-service analytics so business users answer their own questions without waiting for data engineers.
Fixed price, fixed scope. 8–12 weeks from kick-off to your first trusted dashboard in production.
Your Data Is Everywhere — and Nowhere You Can Trust
The average enterprise uses 130+ SaaS applications, each with its own database. Revenue numbers from Salesforce, HubSpot, QuickBooks and your ERP rarely agree. When the CRO and CFO present different revenue figures in the same board meeting, the problem is not the tools — it is the absence of a single, governed source of truth.
Data teams spend 60–80% of their time on data preparation (Anaconda, 2023) rather than on the analysis and models that create business value. Ad hoc SQL queries, spreadsheets passed by email, and BI tools built on unvalidated direct-database connections create a fragile analytics estate that breaks every time the source system changes.
Revenue numbers differ between CRM, ERP and financial reporting — no single source of truth.
Data team spends 70% of time on data preparation rather than analysis.
New BI dashboard requests take 2–4 weeks because data engineers must build custom pipelines.
AI models break in production because training data pipeline and serving pipeline differ.
No data catalogue — no one knows what tables exist, what they mean, or who owns them.
Everything Included in This Launchpad
A production-ready, fixed-price engagement — from architecture to deployment to support.
ELT Data Pipelines
Production-grade ELT pipelines using Airbyte, Fivetran, or custom connectors — ingesting from 100+ SaaS sources, databases, APIs and files into your cloud lakehouse.
dbt Semantic Layer
Transformation and semantic layer in dbt — consistent metric definitions, tested data models, and a data dictionary that everyone trusts.
AI-Ready Data Architecture
Feature store, ML training datasets, and model serving data pipelines designed so your AI models use the same data logic as your dashboards.
Self-Service Analytics
Power BI, Tableau, Looker or Metabase connected to the semantic layer — so analysts and business users build their own reports on trusted, governed data.
Data Governance & Cataloguing
Data catalogue (OpenMetadata or Atlan) with lineage, ownership, classification and quality scores — so your team knows what data exists and whether to trust it.
Data Security & Compliance
Column-level security, PII classification, GDPR/PIPEDA data residency controls, and audit logging for regulated industries.
Business Outcomes You Can Expect
10×
Dashboard Build Speed
Self-service analytics vs. engineer-built pipelines per request.
1 source
Of Truth
All metrics defined once in dbt and shared across tools and models.
60–80%
Less Data Prep Time
Reusable dbt models replace ad hoc SQL and spreadsheet pipelines.
8–12 wks
Time to Production
From kick-off to first trusted dashboard live in production.
How We Deliver
Transparent weekly milestones so you always know what is happening and what comes next.
Infrastructure & Architecture
- Cloud lakehouse provisioning
- ELT tool configuration
- Governance framework
- Source system access
Core Data Pipelines
- Priority source ingestion
- Raw and staging layers
- Data quality tests
- Pipeline monitoring
Semantic Layer (dbt)
- Core dimension models
- Business metric definitions
- dbt documentation
- Data dictionary
Analytics & Cataloguing
- BI tool connection
- Priority dashboards
- Data catalogue deployment
- Lineage visualisation
Governance & Enablement
- Security and access controls
- Self-service training
- Runbook documentation
- Production handoff
Choose Your Starting Point
Every tier is fixed-scope and fixed-price. Start small and scale when ready.
From $3,500
1 week
Data source inventory, current state assessment, architecture recommendation and use-case prioritisation.
- Data source inventory
- Architecture review
- Tool selection
- Use-case roadmap
From $12,000
3 weeks
Deploy one domain data pipeline and semantic layer with a self-service dashboard proof of concept.
- 1 domain pipeline
- dbt models
- 1 BI dashboard
- Data quality tests
From $38,000
8–10 weeks
Full data platform — lakehouse, ELT pipelines, dbt semantic layer, data catalogue and self-service analytics.
- Cloud lakehouse setup
- ELT pipelines (5+ sources)
- dbt semantic layer
- Data catalogue
- Self-service BI
- Team enablement
From $65,000
12–18 weeks
Enterprise data platform with ML feature store, real-time streaming, data mesh domains and enterprise governance.
- ML feature store
- Real-time streaming
- Data mesh architecture
- Enterprise governance
From $6,000/mo
Ongoing
Managed data operations — pipeline monitoring, dbt model updates, new source onboarding and monthly data quality reports.
- Pipeline monitoring
- dbt model updates
- New source onboarding
- Monthly quality report
How We're Different
Compared to generic consultancies and do-it-yourself approaches.
| Feature | Mars Innovation Technology | Generic Consultancy | DIY / In-House |
|---|---|---|---|
Production-grade ELT pipelines | ✓ | POC quality | ✗ |
dbt semantic layer | ✓ | Extra cost | Manual |
AI/ML feature store | ✓ | ✗ | ✗ |
Data catalogue included | ✓ | Extra cost | ✗ |
Fixed price & timeline | ✓ | ✗ | ✗ |
Data governance framework | ✓ | Varies | Manual |
Ongoing managed option | ✓ | ✓ | ✗ |
Frequently Asked Questions
What is the Data Platform Launchpad?
It is a fixed-price engagement that builds a production-grade cloud data lakehouse with ELT pipelines, a governed dbt semantic layer, a data catalogue, and self-service analytics dashboards in 8–12 weeks.
Which cloud data warehouse or lakehouse platforms do you build on?
Snowflake, Databricks (Delta Lake), Google BigQuery, Amazon Redshift, and Azure Synapse Analytics. We recommend the best fit based on your existing cloud provider, data volume, workload type, and team skills.
What is a semantic layer and why does it matter?
A semantic layer (built in dbt) is where you define what metrics mean — "monthly recurring revenue" or "active customers" — as tested, version-controlled code. Every dashboard, report and AI model that uses these metrics gets the same number. This eliminates the "why do your numbers differ from mine?" problem in board meetings.
How many data sources can you connect?
We support 100+ SaaS connectors through Airbyte or Fivetran (Salesforce, HubSpot, Shopify, Stripe, Google Analytics, and more), plus any database or API. The Build tier typically includes 5–8 priority sources. Additional sources are added in Scale or Managed tiers.
How is this different from just using Power BI or Tableau?
BI tools are for visualisation. Without a data platform underneath, each dashboard is built on its own unvalidated SQL query — leading to inconsistent numbers, fragile pipelines, and reports that break when source systems change. The data platform is the foundation that makes BI tools reliable and trustworthy.
Can this support AI and ML workloads?
Yes — it is designed for it. We build a feature store and ML dataset pipelines that use the same dbt transformations as your dashboards, ensuring model training and production serving use identical data logic. This eliminates training-serving skew, one of the most common causes of ML model degradation.
What data governance does this include?
Data catalogue with lineage, ownership and quality scores, column-level security and PII classification, GDPR/PIPEDA data residency controls (data stays in Canada or EU as required), and audit logging for regulatory compliance.
What does the Managed tier include?
Pipeline health monitoring and alerting, dbt model updates when source schemas change, new data source onboarding (one per month), and a monthly data quality report with freshness, completeness and accuracy scores for all core datasets.
