Cloud Data Platform Architecture at Petabyte Scale
Architect and build cloud data platforms that scale to petabytes.
When the question is bigger than one vendor, we design the whole platform: ingestion, storage, processing, orchestration, serving, and governance — engineered to handle petabyte-scale workloads and stay cost-efficient as they grow.
Every platform is built on engineering practice, not tool choice: version-controlled transformations, automated orchestration, CI/CD, testing, and observability — so the system is reliable and easy for your team to extend after we leave.
What we do
- Reference architecture across AWS, Azure, and Google Cloud
- Distributed processing and large-scale ETL/ELT with Apache Spark
- Streaming and high-volume batch ingestion pipelines
- Orchestration with Airflow, Dagster, or native schedulers
- Data quality, testing, and observability
- Governance, security, lineage, and access control
- Cost and performance benchmarking at your real data volumes
What you get
- A production-ready platform proven at your data volumes
- Documented architecture and data models
- Cost and performance benchmarks with tuning guidelines
- Runbooks and knowledge transfer for your team
How we work
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01
Discover
Assess data volumes, sources, SLAs, and the target architecture.
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02
Design
Produce a scalable reference architecture and delivery roadmap.
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03
Build
Implement ingestion, processing, and serving in reviewable increments.
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04
Scale
Load-test, optimize for cost and performance, and hand over.
A low-risk way to start
Short, fixed scope, clear deliverable. You get a plan you can act on — with us or without us.
Platform Migration Plan
A migration plan you can budget against — target architecture, sequencing, cutover approach, risks, and cost.
- Target architecture with the trade-offs written down
- Phased migration plan and cutover runbook
- Cost model for the target platform
Related work
Platforms we have built in this space, with the team setup and the architecture behind each one.
Oracle Data Warehouse to AWS Redshift
AbeBooks (an Amazon subsidiary)
Migrating a marketplace off Oracle, PL/SQL and Crystal Reports onto Redshift and Tableau.
Product Analytics for a Voice Assistant
Amazon Alexa — Natural Language Understanding
Replacing manual CSV extracts with an automated Redshift and Tableau product analytics stack.
Analytics for a Pre-IPO SaaS Product
A Canadian SaaS product company (pre-IPO)
An open-source data stack on AWS Athena, carrying finance reporting through an IPO.
Serverless Data Lake for a Telecom
One of the largest North American telecom companies
A config-driven AWS data lake where every job spins up its own EMR cluster and tears it down.
Open Source Analytics for a FinTech
One of the largest North American FinTech trading companies
A small data team running an open-source analytics stack at a trading company.
Frequently asked questions
We do not know which platform to pick yet. Can you help?
Yes, that is a normal starting point. We assess your workloads, team skills, existing cloud commitments, and budget, then give you a recommendation with the trade-offs written down. We work with Databricks, Snowflake, and the native cloud stacks, so the answer is not pre-decided.
Do you hand over, or do you stay?
We hand over by default — documented architecture, runbooks, and knowledge transfer. Ongoing support is available if you want it, but the goal is your team owning the platform.
How do you handle streaming?
With Kafka, Kinesis, Event Hubs, or Structured Streaming depending on your stack. We are also happy to tell you when micro-batch is good enough — streaming has a real operational cost and is often not needed.
Ready to talk about Cloud Data Platforms?
Tell us what you are working on. We reply within one business day, and we will tell you plainly if we are not the right fit.