Nw Data Architecture Patterns

Data Architecture Patterns

Architecture Selection Decision Tree

Structured only -> Data Warehouse | Mixed + SQL analytics -> Data Lakehouse | Mixed + ML-primary -> Data Lake | Large org + autonomous domains -> Data Mesh

Data Warehouse

Schema: structured, schema-on-write | Data: tables, rows, columns | Governance: centralized | Query: SQL analytics, BI | Architecture: centralized single source of truth

Schema Patterns

Star Schema: Central fact table (measures) surrounded by denormalized dimension tables. Best for BI dashboards, standard reporting.

Snowflake Schema: Normalized dimensions (dimensions reference other dimensions). Reduces storage, increases JOIN complexity. Best when storage cost matters more than query speed.

Kimball vs Inmon

Kimball (Bottom-Up): Build data marts first, integrate later | Star schema, business-process driven | Faster initial delivery | Best for quick wins, department-level analytics

Inmon (Top-Down): Build enterprise DW first, derive data marts | Normalized 3NF enterprise model | Higher upfront effort | Best for large enterprises needing single source of truth

Technology: Snowflake | Amazon Redshift | Google BigQuery | Azure Synapse Analytics

Data Lake

Schema-on-read, flexible | All formats (structured, semi-structured, unstructured) | Raw data in native format | Query via Athena, Spark SQL, PySpark, Pandas | Risk: "data swamp" without governance

Organization

Zones: raw (landing, original format) -> curated (cleaned, validated) -> processed (transformed for use cases) -> archive (cold storage)

Anti-Patterns

• No metadata catalog -> undiscoverable data
• No access controls -> security/compliance risk
• No data quality checks -> garbage in/out
• No retention policy -> unbounded cost growth

Technology: S3 + Athena/Glue | Azure Data Lake Storage + Synapse | HDFS + Hive

Data Lakehouse

Combines warehouse reliability with lake flexibility | Schema enforcement on write with evolution support | ACID transactions on lake storage | Supports both BI/SQL and ML/data science workloads

Medallion Architecture (Bronze / Silver / Gold)

Bronze: Raw data as-is, append-only for auditability, partitioned by ingestion date, schema-on-read Silver: Quality rules (null checks, range validation, referential integrity) | Deduplication on business keys | Schema enforced | SCD applied Gold: Business-level aggregations | Dimensional models (star/snowflake) | Pre-computed metrics/KPIs | Optimized for BI/reporting

Technology: Databricks (Delta Lake) | Apache Iceberg | Apache Hudi

Data Mesh

Core Principles (Martin Fowler)

1. Domain-oriented ownership: Data owned by domain teams, not central
2. Data as a product: Each domain publishes discoverable, trustworthy, self-describing data products
3. Self-serve data platform: Infrastructure team provides platform for domain teams
4. Federated computational governance: Global standards with domain autonomy

Use when: Large org with autonomous domain teams | Central data team is bottleneck | Domain expertise needed | Platform engineering maturity exists Avoid when: Small team (<50 engineers) | Simple data needs | No platform capability | Unclear domain boundaries

ETL vs ELT Pipeline Design

ETL (Extract-Transform-Load)

Transform before loading via dedicated engine (Informatica, Talend, SSIS). Best for complex transforms, constrained targets, regulatory requirements. Scaling limited by transform engine.

ELT (Extract-Load-Transform)

Load raw first, transform using target compute (dbt, Snowflake SQL, BigQuery SQL). Best for cloud DWs with elastic compute, preserving raw data. Scales with target system.

Pipeline Design Principles

• Idempotency: Re-running produces same result (use MERGE/upsert, not INSERT)
• Incremental processing: Process only new/changed data (watermarks, CDC)
• Schema evolution: Handle added/removed columns gracefully (schema registry)
• Data quality gates: Validate between stages (null rates, row counts, value ranges)
• Observability: Log metrics (rows processed, duration, errors, freshness)

Orchestration

Apache Airflow: DAG-based, Python-native, wide adoption | Prefect: modern, dynamic workflows | Dagster: software-defined assets

Streaming Architecture

Apache Kafka

Distributed event streaming platform. Concepts: topics, partitions, consumer groups, offsets. At-least-once delivery (exactly-once with transactions). Use as event bus, message broker, stream storage.

Apache Flink

Stateful stream processing engine. Concepts: DataStreams, windows (tumbling, sliding, session), state management. Exactly-once with checkpointing. Common pattern: Sources -> Kafka (durable event buffer) -> Flink (stateful compute) -> Sinks.

Architecture Selection

Streaming: real-time dashboards, fraud detection, IoT, event-driven | Batch: overnight reporting, historical analysis, ML training | Lambda: parallel batch + stream (complex, prefer Kappa) | Kappa: stream-only, reprocess from Kafka log (simpler)

Scaling Strategies

Vertical (Scale Up)

Add CPU/RAM/storage to existing server | Simpler ops, no app changes | Hard limit: largest hardware | Use first for moderate growth

Horizontal (Scale Out)

Read Replicas: Replicate to read-only copies | Route reads to replicas, writes to primary | Trade-off: replication lag (eventual consistency) | Use for read-heavy workloads

Partitioning (Single Server): Range (date, alphabetical) | List (region, category) | Hash (even distribution) | Benefits: query pruning, maintenance (drop old partitions)

Sharding (Multiple Servers): Distribute data across DB instances by shard key | Strategies: range-based, hash-based, directory-based, geographic

Shard Key Selection (most impactful decision):

• High cardinality for even distribution
• Even access frequency to avoid hot shards
• Query alignment: most queries target single shard
• Avoid monotonically increasing keys (hot spots)

Challenges: Cross-shard queries need scatter-gather | Distributed transactions (2PC) complex/slow | Resharding expensive | App complexity increases

Scaling Decision Guide

Not exceeding single server -> optimize queries/indexes first | Read-heavy -> add read replicas | Write-heavy + partitionable -> partition then shard | Write-heavy + not partitionable -> write-optimized DBs (Cassandra, DynamoDB)

Normalization vs Denormalization

Normalize (3NF): OLTP with frequent writes | Data integrity paramount | Storage optimization | Write > read performance Denormalize: OLAP/analytics (star schema) | Read-heavy, predictable queries | Query > write performance | Acceptable redundancy

Practical approach: Start normalized for transactional tables | Add denormalized/materialized views for reporting | Denormalize selectively based on measured performance | Document decisions and rationale

Source: nWave-ai/nWave → nWave/skills/nw-data-architecture-patterns/SKILL.md

Also appears in: nWave-ai/nWave/plugins/nw/skills/nw-data-architecture-patterns/SKILL.md