Terminology

2185 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 areas2185 Architecture Fundamentals77 Distributed Systems107 Data Architecture110 Cloud Architecture89 Networking88 API & Integration Architecture82 Reliability & Resilience75 Observability70 Performance & Capacity Engineering72 Security Architecture81 Cost Architecture & FinOps66 Business Architecture67 Architecture Communication67 Enterprise Architecture66 Legacy Modernization66 AI-Era Architecture69 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 Architecture64 Streaming & Real-Time Data69 Data Governance & Semantics69 Frontend & Experience Architecture66 Edge, Mobile & IoT68 Regulatory & Data Protection Architecture65 Assurance, Audit & Model Risk64

Kind Any concept785 practice729 pattern362 metric125 case-study77 tool62 protocol45

10 terms shown.

Term	Kind	Topic	What it is
Discord's Message Store Migrations	case-study	Data Architecture	Discord moved from MongoDB to Cassandra to ScyllaDB as message volume grew from millions to trillions, each time for a specific and different reason.
Discord: Hot Partitions at Trillions of Messages Discord Message Storage	case-study	Partitioning & Sharding	Discord partitions messages by channel and time bucket, because a single very busy channel would otherwise concentrate load on one partition.
Figma's Postgres Sharding	case-study	Data Architecture	Figma delayed sharding for years using replicas and vertical partitioning, then sharded Postgres horizontally without downtime using logical shards and a proxy layer.
LinkedIn Databus: Change Capture as a Product Databus	case-study	Change Data Capture	LinkedIn built a change capture system so that derived stores — search, graph, caches — could stay current without every application dual-writing to them.
Netflix's Recommendation Architecture	case-study	Data Architecture	Netflix splits personalisation into offline, nearline and online layers so that expensive computation happens ahead of time and the request path stays fast.
Pinterest's MySQL Sharding	case-study	Data Architecture	Pinterest sharded MySQL by embedding the shard ID inside every primary key, making any object's location computable from its ID alone with no lookup service.
Salesforce's Metadata-Driven Multi-Tenancy	case-study	Data Architecture	Salesforce serves every customer from shared infrastructure with a single physical schema, storing customer-specific data structures as metadata rather than as separate tables.
Slack Flannel: Caching at the Edge for a Chat Client Flannel	case-study	Caching Strategies	Slack pushed user and channel metadata into an application-aware edge cache because clients were downloading enormous amounts of it on every connection.
Uber H3: Hexagonal Spatial Indexing H3, Hexagonal Hierarchical Index	case-study	Indexing	Uber built and open-sourced a hexagonal grid index because uniform neighbour distance matters when you are analysing supply and demand across space.
Uber's H3 Spatial Index	case-study	Data Architecture	Uber indexes the world with hexagons rather than squares, because uniform neighbour distance makes supply, demand and pricing computations correct as well as fast.