Data Platform Basics
A basic introduction: why data platforms exist, how ETL/ELT loops work, and how maturity is measured
Slide contents
1. Data Platform Basics
A basic introduction: why data platforms exist, how ETL/ELT loops work, and how maturity is measured
2. Alexander Polomodov
Technical Director & Fellow, T-Technologies
Architecture and engineering R&D.
AI adoption in SDLC.
Focus: data platform as product.
3. 01. Why a data platform exists
From raw events to reliable decisions, products and AI scenarios
4. Data becomes a production loop
5. Stores and Scripts Break
Accountability breaks
Teams rediscover sources and access.
Business logic hides in unowned SQL.
Data incidents are noticed too late.
Cost is not tied to value.
6. The platform connects data to decisions
7. 02. ETL, ELT and hybrid loop
How data moves from source to consumer
8. ETL vs ELT
9. ETL, ELT and the hybrid lakehouse loop
10. Reference data platform architecture
11. Pipelines Must Be Replayable
12. 03. Platform operating model
Who owns the platform, data products and quality
13. A data platform is also org design
Technology will not work without clear accountability
Centralized — One team sets tools and rules.
Hybrid — Platform owns capabilities; domains own products and SLAs.
Federated — Data Mesh: domains + shared standard.
14. Central → hybrid → federated model
15. Data Mesh needs a platform
Federation needs a shared layer
Domain ownership
Meaning, rules, priorities.
Data products and consumers.
Freshness, completeness, compatibility.
Platform ownership
Self-service ingestion, storage, compute, orchestration.
Catalog, lineage, access, quality.
Financial and engineering guardrails.
16. 04. Governance and reliability
Contracts, lineage, quality, observability and cost
17. Basic Governance Loop
18. Where data platforms usually break
Accountability breaks before tooling does
One giant DAG — Local change becomes systemic risk.
Hidden logic — SQL rules lack tests, versions and review.
No economics — Mart and storage cost stays invisible.
19. Data Service Level Must Be Explicit
20. 05. Platform maturity metrics
How to know the platform became a product, not a tool collection
21. Data platform maturity metrics
22. First 120 Days of Maturity
Start with map, owners, change rules
0-30: sources, consumers, owners, incidents.
30-60: contracts, catalog, quality, freshness.
60-120: templates, runbooks, showback.
Then: metrics → product value.
23. What to take away
A data platform is a managed path from source to decision
ETL/ELT/hybrid answer different trade-offs.
Operating model matters as much as stack.
Maturity = speed, quality, freshness, economics.
Next: DataOps/MLOps in production.
Next: DataOps/MLOps
24. Links and materials
Reference sources
System Design Space: https://system-design.space/chapter/data-pipeline-etl-elt-architecture/.
System Design Space: https://system-design.space/chapter/data-platforms-2025-film.
OpenLineage, Apache Airflow and dbt documentation.
Data Mesh and operating model materials.