01
In-Memory Computing
Keep active data close to compute and reduce unnecessary data movement for latency-sensitive workloads.
DISTRIBUTED DATA & AI PLATFORM
Bring Your Data Closer to AI.
A high-performance data architecture combining transactional processing, distributed in-memory computing, SQL, vector search and AI connectivity.
MariaDB + GridGain brings together persistent data and high-velocity in-memory processing to support real-time applications, analytics and agentic AI.
MariaDB
+
GridGain
+
Apache Ignite
+
AI
DISTRIBUTED
DATA
+ COMPUTE + AI
AI
Agents / RAG
VECTOR
Semantic Search
IN-MEMORY
Low Latency
SQL
Transactions
THE CHALLENGE
AI is getting faster. Enterprise data isn't always keeping up.
Modern AI applications need more than a powerful model. They need immediate access to operational data, historical information, documents, business rules and real-time context.
When that information is spread across databases, caches, analytics systems and vector stores, every additional data movement can add latency and operational complexity.
The question is no longer:
“How fast is the AI model?”
It is:
“How fast can the AI get the data it needs?”
THE ARCHITECTURE
One logical data architecture. Multiple access patterns.
The goal is not to eliminate every specialized technology. It is to minimize unnecessary data movement and bring computation closer to the data that matters.
05
INTELLIGENCE
AI Agents
Reasoning & Actions
RAG
Grounded Responses
AI Applications
Enterprise Workloads
AI CONTEXT
↓
04
AI CONNECTIVITY
MCP Server
Standardized AI Access
Tools & APIs
Controlled Actions
RETRIEVAL
↓
03
DATA SERVICES
Distributed SQL
Queries & Transactions
Vector Search
Semantic Retrieval
Key-Value
Low-Latency Access
DATA + COMPUTE
↓
02
DISTRIBUTED IN-MEMORY
NODE 01
Data + Compute
NODE 02
Data + Compute
NODE 03
Data + Compute
Partitioning
Replication
Colocation
Distributed Compute
PERSISTENCE
↓
01
TRANSACTIONAL FOUNDATION
MariaDB
Relational + ACID
Persistent Storage
Durability + Recovery
CORE CAPABILITIES
Five technologies. One architectural direction.
Each capability solves a different part of the modern enterprise data problem.
02
▦
Distributed SQL
Extend familiar SQL access across distributed data, supporting scalable queries, transactions and analytics.
Scale-out SQL
03
⌁
Vector Search
Search enterprise information by semantic similarity and provide relevant context to RAG and AI workloads.
AI retrieval
04
◈
MCP Server
Provide AI applications and agents with a structured, governed interface to enterprise data and tools.
AI connectivity
05
◎
Apache Ignite
The distributed in-memory foundation behind GridGain, designed for data locality, scale-out processing and high-performance workloads.
Distributed foundation
THE PERFORMANCE PRINCIPLE
It's not just about RAM.
The real architectural advantage comes from keeping related data and computation close together.
Partitioning determines where data lives. Colocation keeps related records together. Distributed compute executes where the data already exists.
01
Partition the data
02
Place related data together
03
Run compute near the data
Customer
Account
Transactions
Risk
↓
COMPUTE WHERE DATA LIVES
COLOCATED
PROCESSING
PROCESSING
Less unnecessary movement.
More localized processing.
AI-READY DATA
From live transactions to AI context.
AI agents need more than documents. They need the live operational context surrounding the question.
01
Operational Data
Customers · Orders · Transactions
→
02
Distributed Processing
In-memory · SQL · Compute
→
03
Semantic Retrieval
Embeddings · Vector Search · RAG
→
04
AI Agent
Reason · Decide · Act
WHERE IT MATTERS
Real-time data changes the outcome.
01
Banking & Finance
Fraud detection, risk scoring, payment decisioning, customer 360 and real-time financial intelligence.
Fraud · Risk · Payments
02
Telecom
Network analytics, customer experience, anomaly detection and real-time service optimization.
Network · Analytics · CX
03
Retail & Commerce
Personalization, inventory visibility, recommendations, pricing and omnichannel intelligence.
Retail · Inventory · AI
04
Logistics
Live fleet data, routing, dispatch optimization, ETA prediction and operational intelligence.
Fleet · Routing · Dispatch
05
Government
AI-enabled citizen services, real-time operational platforms and scalable national data infrastructure.
Digital Government · AI
06
Healthcare
Real-time operational intelligence, clinical context, intelligent search and AI-assisted applications.
Healthcare · AI · Data
MODERNIZE WITHOUT A BIG BANG
Start with the workload.
Not every organization needs to replace its database. ComputingERA can identify the workloads where distributed in-memory processing provides the greatest architectural value.
Discuss your current architecture →
YOUR EXISTING ENVIRONMENT
Oracle
MariaDB
PostgreSQL
Teradata
Apache Ignite
↓
COMPUTINGERA MODERNIZATION PATH
Assess
→
Architect
→
Benchmark
→
Modernize
THE COMPUTINGERA ARCHITECTURE VIEW
Don't optimize one component. Design the system.
The next generation of enterprise AI infrastructure will not be defined by GPUs alone.
High-performance AI requires an architecture that connects Data + Compute + Network + Storage + AI as one integrated platform.
“
Keep transactional data reliable. Bring active data closer to compute. Execute workloads where the data lives. Minimize unnecessary data movement. Add semantic retrieval where AI requires it. Then expose the right context securely to AI applications and agents.
That is fundamentally different from simply adding a cache to a database.
ComputingERA designs the complete architecture.
Data → Compute → Network → Storage → AI
01
Data Architecture
Partitioning, replication, colocation and workload placement.
02
Database Modernization
Assess existing platforms and identify practical modernization paths.
03
AI Data Layer
Structured data, vector search, RAG and governed AI access.
04
Infrastructure Integration
Servers, GPUs, storage, networking, Kubernetes and data platforms.
05
Performance Engineering
Benchmarking based on your workload rather than generic claims.
WHY COMPUTINGERA
Technology is only half the solution.
The other half is knowing how the technologies fit together inside a real enterprise environment.
01
Architecture-first approach
02
Saudi enterprise focus
03
Database + infrastructure expertise
04
AI-ready integration
05
Benchmark-driven decisions
READY TO EXPLORE?
Find where distributed data fits in your architecture.
Bring your existing database, application or AI workload. We'll evaluate the architecture against your actual requirements.
DISTRIBUTED DATA & AI PLATFORM
Bring Your Data Closer to AI.
A high-performance data architecture combining transactional processing, distributed in-memory computing, SQL, vector search and AI connectivity.
MariaDB + GridGain brings together persistent data and high-velocity in-memory processing to support real-time applications, analytics and agentic AI.
MariaDB
+
GridGain
+
Apache Ignite
+
AI
DISTRIBUTED
DATA
+ COMPUTE + AI
AI
Agents / RAG
VECTOR
Semantic Search
IN-MEMORY
Low Latency
SQL
Transactions
THE CHALLENGE
AI is getting faster. Enterprise data isn't always keeping up.
Modern AI applications need more than a powerful model. They need immediate access to operational data, historical information, documents, business rules and real-time context.
When that information is spread across databases, caches, analytics systems and vector stores, every additional data movement can add latency and operational complexity.
The question is no longer:
“How fast is the AI model?”
It is:
“How fast can the AI get the data it needs?”
THE ARCHITECTURE
One logical data architecture. Multiple access patterns.
The goal is not to eliminate every specialized technology. It is to minimize unnecessary data movement and bring computation closer to the data that matters.
05
INTELLIGENCE
AI Agents
Reasoning & Actions
RAG
Grounded Responses
AI Applications
Enterprise Workloads
AI CONTEXT
↓
04
AI CONNECTIVITY
MCP Server
Standardized AI Access
Tools & APIs
Controlled Actions
RETRIEVAL
↓
03
DATA SERVICES
Distributed SQL
Queries & Transactions
Vector Search
Semantic Retrieval
Key-Value
Low-Latency Access
DATA + COMPUTE
↓
02
DISTRIBUTED IN-MEMORY
NODE 01
Data + Compute
NODE 02
Data + Compute
NODE 03
Data + Compute
Partitioning
Replication
Colocation
Distributed Compute
PERSISTENCE
↓
01
TRANSACTIONAL FOUNDATION
MariaDB
Relational + ACID
Persistent Storage
Durability + Recovery
CORE CAPABILITIES
Five technologies. One architectural direction.
Each capability solves a different part of the modern enterprise data problem.
01
⚡
In-Memory Computing
Keep active data close to compute and reduce unnecessary data movement for latency-sensitive workloads.
Low latency
02
▦
Distributed SQL
Extend familiar SQL access across distributed data, supporting scalable queries, transactions and analytics.
Scale-out SQL
03
⌁
Vector Search
Search enterprise information by semantic similarity and provide relevant context to RAG and AI workloads.
AI retrieval
04
◈
MCP Server
Provide AI applications and agents with a structured, governed interface to enterprise data and tools.
AI connectivity
05
◎
Apache Ignite
The distributed in-memory foundation behind GridGain, designed for data locality, scale-out processing and high-performance workloads.
Distributed foundation
THE PERFORMANCE PRINCIPLE
It's not just about RAM.
The real architectural advantage comes from keeping related data and computation close together.
Partitioning determines where data lives. Colocation keeps related records together. Distributed compute executes where the data already exists.
01
Partition the data
02
Place related data together
03
Run compute near the data
Customer
Account
Transactions
Risk
↓
COMPUTE WHERE DATA LIVES
COLOCATED
PROCESSING
PROCESSING
Less unnecessary movement.
More localized processing.
AI-READY DATA
From live transactions to AI context.
AI agents need more than documents. They need the live operational context surrounding the question.
01
Operational Data
Customers · Orders · Transactions
→
02
Distributed Processing
In-memory · SQL · Compute
→
03
Semantic Retrieval
Embeddings · Vector Search · RAG
→
04
AI Agent
Reason · Decide · Act
WHERE IT MATTERS
Real-time data changes the outcome.
01
Banking & Finance
Fraud detection, risk scoring, payment decisioning, customer 360 and real-time financial intelligence.
Fraud · Risk · Payments
02
Telecom
Network analytics, customer experience, anomaly detection and real-time service optimization.
Network · Analytics · CX
03
Retail & Commerce
Personalization, inventory visibility, recommendations, pricing and omnichannel intelligence.
Retail · Inventory · AI
04
Logistics
Live fleet data, routing, dispatch optimization, ETA prediction and operational intelligence.
Fleet · Routing · Dispatch
05
Government
AI-enabled citizen services, real-time operational platforms and scalable national data infrastructure.
Digital Government · AI
06
Healthcare
Real-time operational intelligence, clinical context, intelligent search and AI-assisted applications.
Healthcare · AI · Data
MODERNIZE WITHOUT A BIG BANG
Start with the workload.
Not every organization needs to replace its database. ComputingERA can identify the workloads where distributed in-memory processing provides the greatest architectural value.
Discuss your current architecture →
YOUR EXISTING ENVIRONMENT
Oracle
MariaDB
PostgreSQL
Teradata
Apache Ignite
↓
COMPUTINGERA MODERNIZATION PATH
Assess
→
Architect
→
Benchmark
→
Modernize
THE COMPUTINGERA ARCHITECTURE VIEW
Don't optimize one component. Design the system.
The next generation of enterprise AI infrastructure will not be defined by GPUs alone.
High-performance AI requires an architecture that connects Data + Compute + Network + Storage + AI as one integrated platform.
“
Keep transactional data reliable. Bring active data closer to compute. Execute workloads where the data lives. Minimize unnecessary data movement. Add semantic retrieval where AI requires it. Then expose the right context securely to AI applications and agents.
That is fundamentally different from simply adding a cache to a database.
ComputingERA designs the complete architecture.
Data → Compute → Network → Storage → AI
01
Data Architecture
Partitioning, replication, colocation and workload placement.
02
Database Modernization
Assess existing platforms and identify practical modernization paths.
03
AI Data Layer
Structured data, vector search, RAG and governed AI access.
04
Infrastructure Integration
Servers, GPUs, storage, networking, Kubernetes and data platforms.
05
Performance Engineering
Benchmarking based on your workload rather than generic claims.
WHY COMPUTINGERA
Technology is only half the solution.
The other half is knowing how the technologies fit together inside a real enterprise environment.
01
Architecture-first approach
02
Saudi enterprise focus
03
Database + infrastructure expertise
04
AI-ready integration
05
Benchmark-driven decisions
READY TO EXPLORE?
Find where distributed data fits in your architecture.
Bring your existing database, application or AI workload. We'll evaluate the architecture against your actual requirements.