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Fundamentals of Data Engineering learning path: everything to study, topic by topic

Assessment of data engineering fundamentals based on Reis & Housley: the data engineering lifecycle (generation, storage, ingestion, transformation, serving), its undercurrents, source systems, storage abstractions, ingestion patterns, modeling, serving data, and architecture & technology trade-offs.

Short answer

To learn Fundamentals of Data Engineering, work through 6 modules and 24 topics in the order they build on each other. It starts with Lifecycle & Undercurrents, then moves on to Source Systems & Storage and Ingestion. Use the map as a checklist, or measure your level and study only what is missing.

6
modules
24
topics
50
min to assess

What you will study

Topics are grouped by module. Open one to see its objectives and key concepts.

1 · Lifecycle & Undercurrents4 topics
  • The Data Engineering Lifecycle

    The five stages of the data engineering lifecycle — generation, storage, ingestion, transformation, and serving — and how data flows through them.

    Objectives

    • Name and order the five lifecycle stages
    • Explain how data moves between stages

    Key concepts

    • generation
    • storage
    • ingestion
    • transformation
    • serving
  • Undercurrents of the Lifecycle

    The cross-cutting undercurrents that run through every lifecycle stage: security, data management, DataOps, data architecture, orchestration, and software engineering.

    Objectives

    • List the six undercurrents
    • Explain why undercurrents apply across all stages

    Key concepts

    • security
    • data management
    • DataOps
    • data architecture
    • orchestration
    • software engineering
  • Data Maturity & Team Context

    Stages of data maturity (starting with data, scaling, leading with data) and how a data engineer's focus shifts with organizational maturity.

    Objectives

    • Identify an organization's data maturity stage
    • Adapt priorities to maturity

    Key concepts

    • data maturity stages
    • scaling with data
    • leading with data
    • build vs buy mindset
    • technical debt
  • Role of the Data Engineer

    What a data engineer does, upstream and downstream stakeholders, and collaboration with data producers and consumers.

    Objectives

    • Distinguish upstream from downstream stakeholders
    • Describe core data engineer duties

    Key concepts

    • upstream stakeholders
    • downstream stakeholders
    • data producers
    • data consumers
    • data engineer responsibilities
2 · Source Systems & Storage4 topics
  • Source Systems

    Where data originates: databases, application backends, APIs, files, IoT, and the characteristics that shape ingestion (schemas, frequency, data contracts).

    Objectives

    • Classify common source system types
    • Explain why data contracts matter

    Key concepts

    • databases
    • APIs
    • files
    • IoT
    • data contracts
    • schemas
  • Storage Raw Ingredients

    The physical and primitive building blocks of storage: disk, memory, networking, serialization, compression, and caching.

    Objectives

    • Compare storage raw ingredients
    • Explain trade-offs of compression and serialization

    Key concepts

    • disk vs memory
    • serialization
    • compression
    • caching
    • networking
    • CPU
  • Storage Abstractions

    Higher-level storage systems and abstractions: object storage, file systems, block storage, and storage tiers (hot/warm/cold).

    Objectives

    • Differentiate storage abstractions
    • Match data to an appropriate storage tier

    Key concepts

    • object storage
    • file storage
    • block storage
    • storage tiers
    • hot/warm/cold
    • metadata
  • Warehouse, Lake & Lakehouse

    Data warehouse vs data lake vs lakehouse, schema-on-read vs schema-on-write, and when each storage architecture fits.

    Objectives

    • Contrast warehouse, lake, and lakehouse
    • Choose a storage architecture for a use case

    Key concepts

    • data warehouse
    • data lake
    • lakehouse
    • schema-on-read
    • schema-on-write
    • open table formats
3 · Ingestion4 topics
  • Batch vs Streaming Ingestion

    Bounded batch ingestion vs unbounded streaming ingestion, latency vs throughput, and when real-time genuinely adds value.

    Objectives

    • Decide between batch and streaming
    • Explain latency vs throughput trade-offs

    Key concepts

    • batch ingestion
    • streaming ingestion
    • bounded vs unbounded
    • latency
    • throughput
    • micro-batch
  • Push vs Pull Patterns

    Push vs pull ingestion models, polling, webhooks, and message/event-based ingestion.

    Objectives

    • Distinguish push from pull ingestion
    • Select a model for a given source

    Key concepts

    • push model
    • pull model
    • polling
    • webhooks
    • message queues
    • events
  • Change Data Capture

    Change data capture approaches — log-based, query-based, and trigger-based — and their impact on source systems and downstream freshness.

    Objectives

    • Compare CDC strategies
    • Assess CDC impact on source load

    Key concepts

    • CDC
    • log-based CDC
    • query-based CDC
    • trigger-based CDC
    • binlog
    • freshness
  • Ingestion Design Patterns

    Reliability and quality concerns in ingestion: idempotency, error handling, backfills, schema evolution, and data contracts at the boundary.

    Objectives

    • Design resilient ingestion
    • Handle schema drift and backfills

    Key concepts

    • idempotency
    • error handling
    • backfill
    • schema evolution
    • data contracts
    • dead-letter queues
4 · Queries, Transformation & Modeling4 topics
  • Queries & Query Performance

    How queries execute and the fundamentals that drive performance: indexes, partitioning, clustering, joins, and avoiding full scans.

    Objectives

    • Explain what drives query cost
    • Apply partitioning and indexing

    Key concepts

    • query execution
    • indexes
    • partitioning
    • clustering
    • joins
    • predicate pushdown
  • Data Modeling Approaches

    Modeling for analytics: Inmon (normalized corporate information factory), Kimball (dimensional star schemas), and Data Vault — and when each applies.

    Objectives

    • Contrast Inmon, Kimball, and Data Vault
    • Select a modeling approach for a domain

    Key concepts

    • Inmon
    • Kimball
    • dimensional modeling
    • star schema
    • Data Vault
    • normalization
  • Batch Transformations

    Batch transformation patterns: ETL vs ELT, distributed processing, joins, incremental models, and update patterns.

    Objectives

    • Compare ETL and ELT
    • Design incremental batch transforms

    Key concepts

    • ETL
    • ELT
    • distributed processing
    • incremental models
    • MapReduce
    • update patterns
  • Streaming Transformations

    Stream processing concepts: windowing, event time vs processing time, watermarks, stateful processing, and joins on streams.

    Objectives

    • Explain windowing and watermarks
    • Reason about event-time vs processing-time

    Key concepts

    • windowing
    • event time
    • processing time
    • watermarks
    • stateful processing
    • stream joins
5 · Serving Data4 topics
  • Serving Data for Analytics

    Serving data to BI, dashboards, and ad-hoc analytics: aggregation, governance, self-service, and performance for consumers.

    Objectives

    • Serve analytics-ready data
    • Balance self-service with governance

    Key concepts

    • business intelligence
    • dashboards
    • self-service analytics
    • aggregation
    • governance
    • embedded analytics
  • Serving Data for ML

    Serving data to machine learning: feature stores, training vs serving data, data quality for models, and the data engineer / data scientist boundary.

    Objectives

    • Prepare data for ML serving
    • Define the DE/DS responsibility boundary

    Key concepts

    • feature stores
    • training data
    • serving data
    • data quality for ML
    • labeling
    • online vs offline features
  • Reverse ETL

    Reverse ETL: pushing transformed warehouse data back into operational/SaaS systems, and the operational analytics use cases it enables.

    Objectives

    • Explain reverse ETL
    • Identify reverse-ETL use cases

    Key concepts

    • reverse ETL
    • operational analytics
    • SaaS activation
    • data sync
    • warehouse as source
    • syncs
  • Data Products & Semantic Layer

    Treating served data as products: semantic/metrics layers, data contracts with consumers, ownership, and SLAs.

    Objectives

    • Define a data product
    • Apply a semantic layer and SLAs

    Key concepts

    • data products
    • semantic layer
    • metrics layer
    • data contracts
    • ownership
    • SLAs
6 · Architecture & Technology Choices4 topics
  • Good Data Architecture

    Principles of good data architecture: reversible decisions, loose coupling, plan for failure, build for scale, and brownfield vs greenfield contexts.

    Objectives

    • Apply good-architecture principles
    • Navigate brownfield vs greenfield

    Key concepts

    • architecture principles
    • reversible decisions
    • loose coupling
    • plan for failure
    • brownfield
    • greenfield
  • Choosing Technologies & Trade-offs

    Choosing technologies across the lifecycle: build vs buy, open source vs managed vs serverless, location (cloud/on-prem/hybrid), and avoiding lock-in.

    Objectives

    • Choose between build and buy
    • Evaluate serverless vs managed trade-offs

    Key concepts

    • build vs buy
    • open source
    • managed services
    • serverless
    • vendor lock-in
    • interoperability
  • Cost & FinOps

    Cost as a first-class architecture concern: total cost of ownership, opex vs capex, FinOps practices, and the cost implications of technology choices.

    Objectives

    • Apply FinOps thinking
    • Estimate TCO of an architecture

    Key concepts

    • FinOps
    • total cost of ownership
    • opex vs capex
    • cost attribution
    • cost optimization
    • egress costs
  • DataOps & Orchestration

    Operating data systems reliably: orchestration, DataOps (automation, observability, incident response), monitoring, and SLAs/SLOs for data.

    Objectives

    • Apply DataOps practices
    • Design orchestration and monitoring

    Key concepts

    • orchestration
    • DataOps
    • observability
    • monitoring
    • incident response
    • SLAs/SLOs

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Frequently asked questions

Where should I start studying Fundamentals of Data Engineering?

Start with Lifecycle & Undercurrents: The Data Engineering Lifecycle, Undercurrents of the Lifecycle, and Data Maturity & Team Context. The map orders topics by what they depend on, so each one prepares the next.

How long does a full Fundamentals of Data Engineering assessment take?

A full assessment of this path takes about 50 minutes. Study time depends on where you start; a diagnostic shows which topics you can skip.

Do I need an account to use this page?

No. The map, objectives and key concepts are open. You only need an account to be measured, get your gap by topic and receive a plan.