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Download IgniteARCHITECTURAL BACKBONE
CORE FEATURES
DATA PLACEMENT & PROCESSING
OPERATIONAL EXCELLENCE
ADDITIONAL USE CASES
Download IgniteARCHITECTURAL BACKBONE
CORE FEATURES
DATA PLACEMENT & PROCESSING
OPERATIONAL EXCELLENCE
ADDITIONAL USE CASES
Fast Data Marts
Warehouse Latency OR Data Mart Limits? Neither.
Sub-second queries on curated datasets without cache complexity
Sub-second queries on curated datasets without cache complexity
When Dashboards Need Speed
Executives refresh dashboards during board meetings. Customer portals display real-time analytics. API endpoints serve aggregated metrics to partner integrations. The warehouse query takes 30 seconds. Users see spinners instead of insights.
Pre-aggregating data shifts latency from query time to refresh time. Caching layers add invalidation complexity and lose SQL flexibility. Specialized OLAP systems deliver speed but multiply infrastructure costs. Operational dashboards need warehouse-quality data at cache-like speeds.
How Apache Ignite Solves This
Apache Ignite provides purpose-built fast data marts without cache complexity through memory-first architecture
Sub-Second Analytics
Memory-first storage delivers low-latency queries for dashboards and operational reports. Complex aggregations, joins, and GROUP BY operations without specialized query languages. Horizontal scalability handles concurrent dashboard users without performance degradation. Partition-aware routing minimizes query overhead.
Not a Cache
ETL or CDC-populated data store eliminates cache invalidation complexity. Purpose-built analytical repository with durable storage and ACID guarantees. Full SQL support for complex analytics without key-value limitations. Scheduled refreshes or real-time updates without cache warming or TTL management.
Architecture Pattern
ETL-Populated Analytical Data Store
Data warehouses or operational systems populate Apache Ignite data marts through ETL pipelines or CDC streams, enabling sub-second analytics on curated datasets.
Integration Pattern: ETL pipelines or CDC streams populate department-specific tables in Apache Ignite (sales_mart, finance_mart, customer_mart). Applications query data marts directly for dashboards, customer APIs, and operational reports. Scheduled refreshes (hourly, daily) or real-time updates based on business requirements.
Consistency Model: ACID guarantees ensure data consistency during ETL updates. No cache invalidation logic required. Queries read consistent snapshots without blocking concurrent updates. Point-in-time consistency for reporting periods.
Performance Characteristics: Memory-first architecture delivers sub-second query latency. Partition-aware routing distributes analytical workload across cluster. Horizontal scalability handles growing data volumes and concurrent users. Consistent performance as data marts scale.
When This Pattern Works
This architecture pattern is best for:
- Department-specific dashboards requiring sub-second response times
- Customer-facing analytics APIs serving in-app dashboards
- Operational reporting on curated datasets
- Organizations replacing relational database analytical data marts
Example Use Cases:
- E-Commerce: Sales team dashboard with daily revenue, conversion metrics, and top products refreshed hourly
- SaaS Applications: Customer usage analytics API serving portal dashboards with real-time CDC updates
- Financial Services: Fraud detection dashboard with transaction patterns and risk scores updated every 5 minutes
Key Benefits
Sub-Second Query Performance
Memory-first storage eliminates data warehouse query latency for department dashboards and operational reports. Partition-aware routing minimizes query overhead. Horizontal scalability handles concurrent users without performance degradation. Consistent response times as data volumes grow.
Purpose-Built Repository
Not positioned as cache. No staleness concerns or invalidation complexity. ETL or CDC-populated data store with proper governance. Curated datasets reduce query complexity and improve performance. Full SQL support for complex analytics without specialized query languages.
Infrastructure Consolidation
Replace multiple relational database departmental data marts with single platform. Eliminate caching layers and database combinations. Reduce operational overhead of managing multiple systems. Single platform for all departmental analytics with consistent query interface.
Operational Simplicity
Scheduled ETL refreshes or CDC real-time updates without cache warming. No cache invalidation logic or TTL management required. ACID guarantees for data consistency during updates. Standard SQL interface with JDBC and ODBC connectivity for BI tools.
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Use Cases Overview