Pattern Library
A curated library of reusable architecture patterns for enterprise integration, data, and security. Use the search bar, category filters, and tags below to find and apply proven design solutions.
Archived Storage
Retaining petabytes of rarely accessed historical and compliance data on primary high-performance storage drives exorbitant infrastructure costs. Without tiering, per-GB hot-storage fees compound as archives grow, inflating the platform bill while capacity that could serve active workloads is consumed by dormant data.
Data Mart
Exposing an entire enterprise data warehouse directly to business users buries them in irrelevant tables and complex global schemas. Without curation, analysts waste time deciphering structures they don't need, query performance degrades under mixed workloads, and trust in the platform erodes.
Direct Query Mode
Copying entire datasets into BI tool memory yields stale snapshots, hits memory limits, and scatters sensitive data outside the governed warehouse. Refreshing these extracts strains pipelines, dashboards diverge from reality, and audit gaps widen as copies proliferate beyond central controls.
Document Store
Rigid relational schemas struggle to store and query rapidly evolving, hierarchical, or semi-structured data whose shape varies per record. Forcing such data into normalized tables triggers costly migrations, brittle joins, and object-relational mapping overhead that slows iteration.
Dynamic Data Masking
Maintaining physically separate, redacted copies of datasets for each user clearance level is expensive and drives uncontrolled data sprawl. Duplicate copies multiply storage costs, drift out of sync, and widen the attack surface as sensitive data is replicated across many locations.
Federated Query (Query-in-Place)
Physically consolidating every dataset into one central warehouse before it can be queried is slow, costly, and duplicates data. By the time reports run they reflect stale snapshots, engineering teams drown in fragile pipelines, and storage plus data-movement costs steadily escalate.
Geospatial Store
Standard B-tree indexes cannot efficiently resolve multi-dimensional proximity, boundary-intersection, or distance queries over spatial data. Without spatial indexing, these operations degrade into full-table scans with costly per-row geometry math, inflating latency and compute costs as data volumes grow.
Graph Store
Traversing deep, many-to-many relationships in a relational model forces exponentially costly recursive JOINs that degrade as connections grow. As connections multiply, query latency, index bloat, and engineering complexity spiral, making real-time relationship analysis and pattern detection effectively impossible.
GraphQL Data API
REST APIs force clients to over-fetch unused fields or under-fetch, chaining multiple round-trips to assemble linked, deeply nested data models. Without a unified query layer, teams accumulate brittle endpoint sprawl, inflated bandwidth costs, and latency that degrades the user experience as data relationships grow.
Integration AI / ML Services (Cloud)
An internal workload needs to enrich transactions using a managed third-party AI service — a hosted large language model, vision, or speech endpoint — that executes outside the organisation's trust boundary and whose model version, availability, and pricing are controlled by the provider. Sending raw business records to a provider-controlled endpoint risks disclosing regulated content, while unbounded token consumption turns each inference call into an uncapped cost, latency, and availability dependency.
Integration AI / ML Services (External)
An organization must exchange training datasets and real-time predictions with external third-party partners across trust boundaries. Without controlled ingestion, validation, and serving paths, internal machine learning pipelines and private feature stores risk direct exposure to the public internet, inviting data poisoning, leakage, and unauthorized model access.
Integration AI / ML Services (Internal)
An organization runs machine learning pipelines that ingest internal training data and serve predictions to internal workloads. Without strict isolation, exposing these datasets, training jobs, or inference endpoints to the public internet risks data leakage, unauthorized access, and compliance violations.
Integration API Management (Cloud)
An internal application must consume or expose synchronous APIs across a public cloud or SaaS boundary, where the counterparty is a managed service outside the organisation's network perimeter and its endpoints, credentials, and rate limits are controlled entirely by the provider. Without a governed egress path and a resilience layer, every SaaS dependency becomes an unmonitored outbound connection and a single point of failure for the calling workload.
Integration API Management (External)
An internal application must synchronously exchange data or services with an external user, partner, or system across a trust boundary in real time. Without a governed edge, inbound and outbound traffic bypasses perimeter controls, exposing internal systems to untrusted networks and leaving connections unsecured, unfiltered, and unlogged.
Integration API Management (Internal)
Internal applications and microservices must exchange data and invoke each other in real time without exposing endpoints to the public internet. Without governed private connectivity, teams resort to ad hoc calls, inconsistent authentication, and unmonitored traffic that erode security, reliability, and observability across the estate.
Integration Change Data Capture (Cloud)
External systems require a robust, near-real-time mechanism to synchronize recently modified data without direct system-to-system database access or significant modifications to existing applications. This pattern addresses the challenge of propagating data changes across organizational boundaries or disparate environments securely and efficiently.
Integration Change Data Capture (External)
A backend workload must continuously share incremental data changes with an external system or partner. Without an efficient capture strategy, repeated full-table exports or intrusive polling burden the source, erode its operational integrity, and leave the partner working from stale, high-latency data.
Integration Change Data Capture (Internal)
A backend workload or microservice requires timely, non-invasive access to incremental data changes originating from another backend workload's persistent data store. The goal is to facilitate data synchronization, enable event-driven processing, or power analytical insights without directly impacting the source system's performance or requiring intrusive modifications to its schema or application logic.
Integration ETL / Batch Processing (Cloud)
Enterprises must ingest, transform, and load diverse datasets from on-premises, third-party, and cloud sources into cloud-native data platforms. Without a scalable, cost-efficient approach, throughput bottlenecks, rising costs, and lapses in data integrity and governance degrade downstream analytics.
Integration ETL / Batch Processing (External)
An internal backend workload must periodically exchange large data volumes with an external system. Real-time synchronization is unnecessary here, but without disciplined bulk handling, data consistency and integrity break down, corrupting records and eroding trust between the systems.
Integration ETL / Batch Processing (Internal)
An internal backend workload must periodically ingest large, infrequently changing datasets from another internal source within the private subnet. Without an efficient bulk transfer mechanism, ad-hoc or row-by-row movement overloads systems, breaks data consistency, and fails to scale as source volumes grow.
Integration Event Streaming (Cloud)
Two external systems or organizations must exchange data in real time, but direct peer-to-peer integration is impractical or undesirable. Without an intermediated streaming layer, brittle point-to-point links create tight coupling and unreliable delivery, leaving events lost, delayed, or duplicated across organizational boundaries.
Integration Event Streaming (External)
A backend workload must exchange streaming data in real time with external partners, both ingesting inbound events and publishing outbound feeds. Without a governed streaming boundary, external event flows bypass security controls or devolve into brittle point-to-point links that cannot authenticate parties or scale.
Integration Event Streaming (Internal)
Multiple internal backend workloads in a private subnet must exchange data and event notifications asynchronously without tight coupling. Without a shared messaging backbone, services resort to direct synchronous calls that create brittle dependencies, block on slow peers, and cannot scale or absorb traffic bursts.
Integration Manual / Ad-hoc Transfer (Cloud)
Two disparate systems, often external to the primary cloud, must exchange data at exceptionally low volume and frequency. Building a fully automated integration pathway for such sporadic transfers is economically and operationally prohibitive, leaving the exchange without a defined, secure, or auditable process.
Integration Manual / Ad-hoc Transfer (External)
An internal backend workload must exchange data with an external partner or system only occasionally, at low or sporadic volume. At this volume a fully automated integration is not economically viable, yet ad hoc transfers risk data exposure, lost accountability, and compliance gaps.
Integration Manual / Ad-hoc Transfer (Internal)
Some internal data exchanges between backend workloads occur too rarely to justify a fully automated integration. Without a defined approach, teams either build costly programmatic pipelines that sit idle or fall back on ad hoc, unrepeatable transfers that risk errors and data loss.
Integration Master Data Management (Cloud)
Numerous external systems across the extended enterprise depend on the same reference data to run their operations. Without a single authoritative source, each system keeps its own copy, breeding conflicting values, broken business processes, and data-integrity failures that ripple across every integration.
Integration Master Data Management (External)
Both internal and external workloads must stay synchronized in real time with the organization's authoritative MDM system. Without reliable, secure integration, reference data drifts across systems, producing inconsistent records, integrity violations, and flawed downstream decisions.
Integration Master Data Management (Internal)
Maintaining a unified, consistent, and authoritative view of critical business entities (master data) across disparate internal backend workloads and microservices. Without a single source of truth, data inconsistencies can lead to operational inefficiencies, poor decision-making, and compliance risks.
Integration Middleware Services (Cloud)
Disparate external systems must exchange data and services reliably, securely, and in real time across organizational and protocol boundaries. Direct point-to-point integration is often technically infeasible, tightly couples systems, and lacks the orchestration and transformation capabilities these exchanges demand.
Integration Middleware Services (External)
A backend workload requires sophisticated real-time or near real-time data exchange with an external system, necessitating protocol mediation, data transformation, or asynchronous communication patterns beyond a direct API interface. This integration may involve both inbound consumption by external parties and outbound publication to external destinations.
Integration Middleware Services (Internal)
Internal backend workloads must exchange data and invoke services across systems using incompatible protocols and formats. Without a mediating layer, brittle point-to-point links proliferate, each forced to handle protocol translation, transformation, and orchestration, jeopardizing reliable near-real-time delivery.
Integration Native Connectors (Cloud)
Two cloud-native applications, or a cloud-native app and a SaaS platform, must integrate using their built-in capabilities. Without a defined native path, teams resort to bespoke middleware for synchronous or asynchronous data and service exchange, adding cost, latency, and maintenance burden.
Integration Native Connectors (External)
An internal backend workload must synchronously exchange data or services with an external system. Relying on an existing, application-specific native integration library for this exchange risks ad hoc, insecure connections that bypass network isolation and outbound traffic controls.
Integration Native Connectors (Internal)
Two internal backend workloads in the same private network segment need to exchange data or services directly. Without leveraging their native or vendor-supplied integration components, they must route through a dedicated integration platform, adding needless latency, cost, and operational overhead.
Integration Secure File Transfer (Cloud)
Organizations require a secure, reliable, and auditable mechanism to exchange large volumes of files or batch data with external partners, customers, or third-party systems. Direct system-to-system integrations may not be feasible or appropriate due to varying technical capabilities, security posture differences, or the inherent batch nature of the data exchange.
Integration Secure File Transfer (External)
An internal backend workload must exchange files or bulk data asynchronously with external users, partners, or systems. Without a controlled transfer boundary, exposing internal services directly to outside parties risks data interception, unauthenticated access, and unreliable, unrecoverable delivery of large payloads.
Integration Secure File Transfer (Internal)
Distributed backend workloads and microservices in private subnets must exchange files for batch processing, data sync, and event-driven workflows. Without a secure, managed transfer mechanism, ad-hoc file movement erodes data integrity, breaks auditability, and exposes traffic to interception or loss.
Integration Workflow & Orchestration (Cloud)
A distributed business process requires robust orchestration across multiple internal microservices and secure, event-driven or API-driven interactions with external systems or data sources. Challenges include ensuring transactionality, resilience, and visibility across disparate service boundaries.
Integration Workflow & Orchestration (External)
An organization needs to automate and orchestrate complex, multi-step business processes that span across various internal microservices and securely integrate with external partner systems, third-party APIs, or data sources. The challenge involves managing workflow state, ensuring reliable execution, handling failures across distributed environments, and providing comprehensive observability for these long-running processes.
Integration Workflow & Orchestration (Internal)
An organization must automate complex business processes that span multiple interdependent backend workloads, microservices, and external systems. Without durable state management and comprehensive error handling, long-running orchestrations lose progress on failure, leave inconsistent data across services, and become impossible to monitor or recover reliably.
Master Data Management (MDM)
Disparate systems each maintain their own version of core business entities like Customer and Product, so no single authoritative record exists. The resulting duplicates and conflicting attributes inflate costs, corrupt analytics and reporting, and expose the business to compliance and regulatory risk.
Materialized Views
Recomputing complex multi-table joins and aggregations on every dashboard request drives up query latency and compute costs. As user concurrency and data volume grow, repeated on-the-fly scans throttle interactive analytics and inflate query-driven billing unpredictably.
Medallion Architecture
Dumping all data into a single lake layer with no quality tiers turns it into a 'data swamp' of unreliable, hard-to-query information. Analysts lose trust, pipelines break on schema drift, and there is no clean raw history to replay or audit when errors surface downstream.
Open Table Format
Object storage alone lacks ACID transactions, schema evolution, and time-travel, making concurrent writes to large tables error-prone. Without transactional metadata, teams face partial reads, lost updates, expensive full rewrites for schema changes, and no reliable way to audit or roll back historical states.
Retrieval-Augmented Generation (RAG)
Large Language Models lack access to private enterprise data and confidently fabricate plausible but false answers about internal topics. Without grounding in authoritative sources, users receive unverifiable responses that erode trust, drive costly errors, and expose the organization to compliance risk.
Star Schema
Highly normalized relational schemas force analytical queries through many complex joins, crippling performance on large-scale reporting. As data volumes grow, dashboards slow to a crawl, query costs escalate, and business users struggle to explore data without deep SQL expertise.
Time-Series Store
General-purpose databases buckle when ingesting millions of high-frequency timestamped events and computing temporal aggregations. Without a purpose-built engine, ingestion back-pressures producers, storage costs balloon, and time-window queries slow to a crawl, undermining real-time dashboards and alerting.
Unified Batch & Stream Processing (Kappa)
Maintaining separate batch and stream processing systems forces teams to implement the same business logic twice in divergent codebases. Over time these paths drift, producing conflicting results between historical and real-time views while doubling infrastructure, testing, and operational costs.
Webhooks (Event-Push Ingestion)
Polling source APIs on a fixed schedule adds latency, wastes bandwidth on empty responses, and cannot keep pace with real-time event streams. Without a push model, ingestion pipelines fall behind, incur rising API costs, and hit rate limits that force teams to choose between freshness and reliability.