Data Engineering Learning Path

DORA — ICT Risk Management

10 concepts · 7 paths · 1 pillar

Progress 0 / 10 complete

Flow

DORA — ICT Risk Management
Distributed Systems for Data
Data Mesh
Data Contracts

All Paths

DORA — ICT Risk Management → Distributed Systems for Data → Data Mesh → Data Contracts
3 steps
DORA — ICT Risk Management → Distributed Systems for Data → Data Mesh → Lakehouse Architecture
3 steps
DORA — ICT Risk Management → Data Contracts → Data Governance → Data Quality
3 steps
DORA — ICT Risk Management → Data Governance → Data Quality → Data Pipeline Architecture
3 steps
DORA — ICT Risk Management → Data Observability → Data Pipeline Architecture
2 steps
DORA — ICT Risk Management → Distributed Systems for Data → Data Pipeline Architecture
2 steps
DORA — ICT Risk Management → Workflow Orchestration → Data Pipeline Architecture
2 steps

Concepts in this Path

1 DORA — ICT Risk Management
Mark Done Understand
Data-engineering 2 items Review on Dashboard →
Regulatory Compliance for Data Platforms: DORA, NIS2, EU AI Act, and Data Act Learn The Risk-Based Approach in AML: A Practical Introduction Research
2 Distributed Systems for Data
Mark Done Evaluate
Data-engineering 5 items Review on Dashboard →
Nearly-Optimal Time-Sample Trade-off in Consensus Dynamics Research Only Accepted Tokens Count: An Energy Accounting of Speculative Decoding Under Latency Targets Research Power Adjusting Games Under Block-Dependent Pool Payout Schemes Research
3 Data Mesh
Mark Done Create
Data-engineering 4 items Review on Dashboard →
Designing a Production Data Platform: From Requirements to Architecture Learn Data Mesh: Architecture, Governance, and Real-World Adoption Learn The Modern Data Catalog: Metadata, Discovery, and AI-Driven Automation Learn
4 Data Contracts
Mark Done Create
Data-engineering 3 items Review on Dashboard →
Data Contracts & Data Lineage: Building Trust in Modern Data Pipelines Learn Data Contracts: Schema as API for the Analytics Team Research Compliance-by-Design: When the Rulebook Lives in the Pipeline Knowledge
5 Lakehouse Architecture
Mark Done Create
Data-engineering 3 items Review on Dashboard →
Data Warehouse vs Data Lake: Choosing the Right Architecture Learn PySpark Fundamentals: Distributed Data Processing at Scale Learn Tessera: Fast Join-Aggregates over Skewed Sparse Relations in Database Engines Research
6 Data Governance
Mark Done Create
Data-engineering 3 items Review on Dashboard →
Data Contracts & Data Lineage: Building Trust in Modern Data Pipelines Learn The Modern Data Catalog: Metadata, Discovery, and AI-Driven Automation Learn Regulatory Compliance for Data Platforms: DORA, NIS2, EU AI Act, and Data Act Learn
7 Data Quality
Mark Done Create
Data-engineering 8 items Review on Dashboard →
Data Quality, Observability, and Cost Optimization at Scale Learn Learning Hub Learn DataOps: Principles, Practices, and Pipeline Architecture Learn
8 Data Pipeline Architecture
Mark Done Create
Data-engineering 1 items Review on Dashboard →
DataOps: Principles, Practices, and Pipeline Architecture Learn
9 Data Observability
Mark Done Analyze
Data-engineering 9 items Review on Dashboard →
Designing a Comprehensive AML Compliance Program Learn Data Quality, Observability, and Cost Optimization at Scale Learn Data Quality Engineering: Testing, Monitoring, and Expectations at Scale Learn
10 Workflow Orchestration
Mark Done Evaluate
Data-engineering 4 items Review on Dashboard →
ML Pipeline Orchestration: From Notebook to Production with Feast and MLflow Research Airflow vs Prefect vs Dagster: Choosing the Right Orchestrator in 2026 Research Dagster's Asset-Centric Orchestration Model Research

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