Choose serverless well
Know when serverless and managed services beat running your own infrastructure.
Architecture Path · Platform Skills · Step 3
Serverless Data Architect
Design modern, serverless data platforms across the major clouds and lakehouse engines. Learn the architecture patterns, the lakehouse, streaming and change-data-capture, and the cost discipline that make a platform scalable and AI-ready, the platform depth an enterprise architect must be able to direct.
8
Modules
40
Chapters
Lakehouse
+ Serverless
Capstone
Reference architecture
The journey
Eight modules, one arc
The eight modules build cumulatively toward a real capstone. Watch the work move, and the value compound, at every stage.
Each module builds the capability the next one depends on, ending in a portfolio-ready capstone.
Outcomes
What you'll be able to do
Design a lakehouse
Architect a lakehouse with open table formats and clear layers.
Move data in real time
Apply streaming and change-data-capture patterns correctly.
Serve AI and analytics
Design the serving and feature layers that AI and analytics consume.
Architect across clouds
Reason about Snowflake, Databricks, BigQuery, and Delta Lake trade-offs.
Govern cost
Apply FinOps so a serverless platform improves economics rather than inflating them.
Curriculum
8 modules, 40 chapters, ending in a capstone
Eight modules of five chapters each, sequenced so the material builds cumulatively to a real, portfolio-ready capstone. Expand any module for its focus and lessons.
01 Serverless and the Modern Data Platform
Understand the serverless model and why it reshaped data platforms.
- What serverless means for data, and its trade-offs
- Managed services versus self-managed infrastructure
- The building blocks: storage, compute, orchestration, catalog
- Elasticity, separation of storage and compute, and cost
- Assessing a workload for a serverless fit
02 Cloud Platform Foundations
Work fluently with the managed data services of the major clouds.
- Object storage as the foundation of the platform
- Serverless compute and query engines
- Identity, access, and security in the cloud
- Orchestration and event-driven data workflows
- Comparing the equivalent services across clouds
03 The Lakehouse Architecture
Design a lakehouse that unifies lake flexibility with warehouse reliability.
- Why the lakehouse emerged: lake plus warehouse
- Open table formats: Delta, Iceberg, and Hudi
- Medallion layers: bronze, silver, and gold
- ACID, time travel, and schema evolution on the lake
- Designing the layers for a real subject area
04 Warehouses and Query Engines
Use serverless warehouses and engines for fast, governed analytics.
- Snowflake, BigQuery, and Databricks SQL at a glance
- Modeling and performance in a serverless warehouse
- Workload isolation and concurrency
- Governance, sharing, and access in the warehouse
- Choosing an engine for a given analytical need
05 Streaming and Change Data Capture
Bring data in continuously and keep the platform fresh.
- Batch versus streaming, and when each fits
- Streaming ingestion patterns on serverless platforms
- Change-data-capture from operational systems
- Exactly-once, ordering, and late data
- Designing a near-real-time ingestion path
06 Serving Analytics and AI
Design the layers that analytics, BI, and AI consume.
- Serving layers for BI and self-service analytics
- Feature stores and data for machine learning
- Vector and semantic-search data for AI applications
- APIs and reverse-ETL for operational consumption
- Designing an AI-ready serving layer
07 Reliability, Security and FinOps
Make the platform dependable, secure, and cost-disciplined.
- Reliability and observability for serverless data
- Security, encryption, and access across the platform
- FinOps: cost visibility and avoiding the cloud blowout
- Guardrails, budgets, and cost attribution
- Right-sizing a serverless workload for cost and speed
08 Capstone: Design a Serverless Data PlatformCapstone
Produce a defensible reference architecture for a serverless lakehouse platform.
- Gather requirements and constraints for a scenario
- Design the lakehouse, ingestion, and serving layers
- Choose clouds and engines with justified trade-offs
- Add reliability, security, and a FinOps plan
- Capstone: present a serverless reference architecture as a portfolio artifact
Who it's for
Built for Architects and engineers designing modern serverless data platforms
Data architects
Architects designing serverless, lakehouse-based target platforms.
Platform and data engineers
Engineers building on managed and serverless cloud services.
Cloud and solution architects
Architects extending into the data-platform domain.
Technical leads
Leads who must choose platforms and defend the choice.
Program formats
How you learn
Self-paced
Work through it at your own pace, with lifetime access to every module and the capstone.
Mentor-led cohort
A guided cohort with live sessions, reviews, and a peer group working the same path.
Private corporate
A closed cohort for your team, tailored to your platforms, domains, and priorities.
Portfolio-building
Every module produces an artifact; the capstone assembles them into a portfolio deliverable.
For teams
Bring it to your team
Run Serverless Data Architect as a private, closed cohort tailored to your platforms, domains, and priorities, as part of building the architecture capability your organization needs.
Related programs
Explore the path
FAQ
Serverless Data Architect - answered
Who is Serverless Data Architect for?
Data architects, platform and data engineers, cloud and solution architects, and technical leads designing modern serverless, lakehouse-based data platforms.
How does this relate to Cloud & Data Platforms?
This program is the architecture-and-design view of serverless platforms and sits within the architecture path; the Cloud & Data Platforms track goes deeper on hands-on, engine-by-engine skills. They are complementary, and cross-linked.
Is it tied to one cloud?
No. It teaches patterns across Snowflake, Databricks, BigQuery, and Delta Lake so you can architect and choose deliberately rather than by default.
What is the capstone?
A defensible serverless reference architecture for a scenario: lakehouse layers, ingestion, serving, cloud and engine choices, reliability, security, and FinOps, as a portfolio artifact.
Do I need deep coding skills?
You should be comfortable with data and the cloud. The emphasis is architecture and design decisions rather than implementation in a single language.
Self-paced or cohort?
Both, plus private corporate cohorts tailored to your cloud and platforms.
Take the next step on the path
Enrol, enquire, or explore the full IC-to-Head of Data Architecture path.