Terminology

2225 terms, tools, patterns and metrics an architect is expected to use precisely. Each one gets a short explanation of what it is, and — where it matters — what it is commonly confused with. Search filters as you type; the column headers sort.

All areas2225 Architecture Fundamentals77 Distributed Systems107 Data Architecture110 Cloud Architecture94 Networking88 API & Integration Architecture82 Reliability & Resilience75 Observability70 Performance & Capacity Engineering72 Security Architecture86 Cost Architecture & FinOps71 Business Architecture67 Architecture Communication67 Enterprise Architecture66 Legacy Modernization71 AI-Era Architecture74 Software Architecture & Engineering71 Architecture Patterns71 Architecture Decision-Making63 The Architect's Meta-Skills61 Delivery & Release Engineering66 Platform Engineering & Developer Experience70 Testing & Quality Architecture66 Data Platform Architecture69 Streaming & Real-Time Data69 Data Governance & Semantics74 Frontend & Experience Architecture66 Edge, Mobile & IoT68 Regulatory & Data Protection Architecture65 Assurance, Audit & Model Risk69

Kind Any concept801 practice736 pattern377 metric127 case-study77 tool62 protocol45

15 terms shown.

Term	Kind	Topic	What it is
Analytics Cost Control	practice	Analytics Cost Control	The set of design and operational choices that determine whether an elastic data platform costs a predictable amount or an alarming one.
Backfill	practice	Batch Orchestration	Reprocessing historical periods through a pipeline after fixing a defect or adding a field, at a scale the pipeline was not sized for.
CDC Pipeline Design	practice	CDC Pipeline Design	The concerns that turn log-based change capture from a demo into something that can be relied on — snapshots, ordering, schema change and replay.
Compaction File Consolidation, OPTIMIZE, Small File Remediation	practice	File Formats & Compaction	Rewriting many small data files into fewer larger ones, with sorting, as a required background process - because streaming ingestion produces small files continuously and a table without compaction degrades si…
Dataset Ownership Boundary Data Product Boundary, Published Dataset Surface	practice	Data Platform Architecture	The line between a producing team's internal data and the surface it publishes - which is what makes decentralised data ownership safe rather than fragmenting.
Grain Declaration	practice	Dimensional Modelling	Stating exactly what one row of a fact table represents, before any column is chosen, because every later decision depends on it.
Logical Date Partitioning Execution Date, Idempotent Reprocessing	practice	Batch Orchestration	Making every batch task a pure function of a logical date, so that any run can be repeated safely and a backfill is a range of independent executions.
Model Graph Pruning Removing Unused Models, DAG Hygiene	practice	Transformation Frameworks	Periodically removing transformation models with no downstream consumers - the cheapest available reduction in a build's cost and duration, and the one nobody owns.
Report Migration Inventory	practice	Warehouse Migration	The enumerated list of every report, extract and downstream consumer of the legacy warehouse, with usage evidence, which is what makes the migration finite.
Row Group Sizing Row Group Tuning, Parquet Block Size	practice	File Formats & Compaction	Choosing how many rows a columnar file groups into one statistics-bearing unit, which sets both the finest granularity a query can skip and the memory a writer and reader need.
Snapshot Expiry Snapshot Retention, Metadata Expiry	practice	Open Table Formats	The scheduled removal of a table's old snapshots and the data files only those snapshots referenced, which is what stops time travel from turning every rewritten row into permanent storage.
Table Maintenance Budget Maintenance Capacity Reservation, Housekeeping Window	practice	Workload Isolation	Treating compaction, clustering and snapshot expiry as a workload class with its own reserved compute and a guaranteed window, rather than as low-priority work that loses to every deadline.
Warehouse Migration	practice	Warehouse Migration	Moving analytics from one platform to another, where the difficulty is almost never the data and almost always the accumulated logic and consumers.
Workload Fit Assessment	practice	Warehouse, Lake & Lakehouse	Choosing between warehouse, lake and lakehouse by the workloads that must run, rather than by which one is currently fashionable.
Workload Isolation	practice	Workload Isolation	Preventing one team's expensive query or backfill from degrading everyone else's analytics, by separating compute rather than sharing one pool.