From Raw Data to Business Insights in Hours, Not Days
Mars Innovation Technology designs and deploys production-grade data pipelines that ingest, transform and deliver clean, reliable data to your analytics and AI systems — on any cloud.
Reduces data engineering lead time from weeks to days with reusable pipeline templates.
Supports batch and streaming ingestion from databases, APIs, SaaS tools and IoT sources.
Includes data quality validation, lineage tracking and automated alerting.
Compatible with Snowflake, BigQuery, Redshift, Azure Synapse and Databricks.
Business Decisions Are Made on Stale, Unreliable Data
Data teams spend 60–80% of their time on data wrangling instead of analysis. Manual ETL jobs run overnight and fail silently. Analysts trust their spreadsheets more than the data warehouse because the warehouse has been wrong too many times. Leadership makes decisions based on data that is 24–72 hours out of date.
Modern businesses generate data from dozens of sources — SaaS applications, transactional databases, IoT sensors, APIs, event streams — but connecting, transforming and delivering that data reliably at scale requires engineering discipline that most teams have not had time to build.
Data warehouse is days behind because ETL jobs run nightly and fail silently.
Data quality issues discovered by the business, not detected by the pipeline.
Each new data source requires a bespoke integration built from scratch.
No data lineage — when something breaks, nobody knows which upstream source caused it.
Analysts spend more time cleaning data than producing insights.
Everything Included in This Launchpad
A complete, production-ready package — from architecture to deployment to ongoing support.
Batch & Streaming Ingestion
Connectors for databases (Postgres, MySQL, SQL Server), SaaS (Salesforce, HubSpot, Shopify), event streams (Kafka, Kinesis) and file sources (S3, SFTP).
Transformation Layer (dbt / Spark)
SQL or PySpark transformations with version control, testing, documentation and lineage built in.
Data Quality Validation
Row-count checks, schema validation, freshness assertions and anomaly detection on every pipeline run.
Orchestration (Airflow / ADF)
Dependency-aware pipeline scheduling with retry logic, SLA monitoring and alerting on failures.
Data Lineage & Cataloguing
Automated metadata capture, column-level lineage and a searchable data catalogue for your team.
Cloud Data Warehouse Integration
Optimised loading patterns for Snowflake, BigQuery, Redshift, Azure Synapse and Databricks Delta Lake.
Business Outcomes You Can Expect
60–80%
Data Prep Time Saved
Engineers spend more time on analysis, less on wrangling.
<15 min
Data Freshness
Near-real-time streaming pipelines replace overnight batch jobs.
99%+
Pipeline Reliability
Automated retries, alerting and quality checks eliminate silent failures.
10×
Faster New Source Onboarding
Reusable connector templates reduce each new integration from weeks to days.
How We Deliver
A transparent, structured delivery plan with weekly milestones so you always know what is happening and what comes next.
Data Discovery
- Source system access
- Schema documentation
- Data quality baseline
- Architecture design
Ingestion Layer
- Source connectors
- Raw landing zone
- Schema validation
- Initial data load
Transformation Layer
- dbt project setup
- Core transformation models
- Unit tests
- Documentation generated
Orchestration & Quality
- Airflow/ADF DAGs
- Data quality assertions
- Alerting rules
- Retry + backfill logic
Lineage & Catalogue
- Column-level lineage
- Data catalogue setup
- Access controls
- Analyst onboarding
Production Cutover
- Production deployment
- Parallel run validation
- Runbook documentation
- Team training
Choose Your Starting Point
Every tier is a fixed-scope, fixed-price engagement. Start small and scale when ready.
From $2,500
1 week
Audit your data sources, current ETL processes, and warehouse structure. Produce a modernisation roadmap.
- Data source inventory
- ETL process review
- Warehouse audit
- Roadmap document
From $8,500
2 weeks
Build and deploy a working pipeline for 2–3 priority data sources into your data warehouse.
- 2–3 source connectors
- Basic transformation layer
- Scheduling + alerting
- Data quality checks
From $22,000
5–7 weeks
Full production data pipeline with orchestration, quality validation, lineage and documentation.
- 5+ source connectors
- dbt/Spark transforms
- Airflow/ADF orchestration
- Data quality suite
- Lineage + catalogue
- Runbook + training
From $38,000
8–12 weeks
Enterprise data platform with real-time streaming, data mesh patterns, and BI layer integration.
- Kafka/Kinesis streaming
- Data mesh architecture
- BI layer integration
- Cost optimisation
From $4,000/mo
Ongoing
Managed pipeline operations — monitoring, incident response, schema change management and monthly reporting.
- Pipeline monitoring
- Incident response
- Schema change management
- Monthly report
How We're Different
Compared to generic consultancies and do-it-yourself approaches.
| Feature / Criterion | Mars Innovation Technology | Generic Consultancy | DIY / In-House |
|---|---|---|---|
End-to-end pipeline design | |||
Data quality built in | |||
Lineage + catalogue | Extra cost | ||
Fixed price & timeline | |||
dbt + Airflow expertise | Limited | Learning curve | |
Streaming + batch | Batch only | Variable | |
Managed operations option |
Frequently Asked Questions
What is the Data Pipeline Launchpad?
It is a fixed-price engagement that designs, builds and deploys a production-grade data ingestion, transformation and delivery pipeline on AWS, Azure or GCP — including orchestration, data quality, lineage and documentation.
What data sources can you connect?
We connect relational databases (PostgreSQL, MySQL, SQL Server, Oracle), SaaS applications (Salesforce, HubSpot, Shopify, Stripe), event streams (Apache Kafka, Amazon Kinesis), flat files (S3, SFTP, GCS) and REST APIs.
Which data warehouses do you support?
Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics, and Databricks Delta Lake are all supported. We design the loading and transformation patterns appropriate for each platform.
Do you support real-time streaming?
Yes. Streaming ingestion via Apache Kafka, Amazon Kinesis, or Azure Event Hubs is available in the Build tier and fully productised in the Scale tier for near-real-time use cases.
What is dbt and why does it matter?
dbt (data build tool) is the standard for SQL-based data transformation. It version-controls your transformations, generates documentation automatically, runs tests on every model, and produces column-level data lineage. It turns your data warehouse into a reliable product rather than a collection of ad hoc queries.
How do you handle data quality?
Every pipeline run includes automated row-count checks, schema validation, freshness assertions, and statistical anomaly detection. Failures trigger alerts and stop downstream processes from consuming bad data.
Can this replace our current overnight ETL jobs?
Yes. We document your current ETL jobs during the Assess tier, build equivalent or improved pipelines, and run in parallel before cutover to ensure nothing is lost.
What does the Managed tier include?
Managed Data Pipeline covers 24/5 pipeline monitoring, incident response for failures, schema change management when upstream sources change, monthly data quality reports, and a quarterly architecture review.
Related Launchpads
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