How to Optimise OLAP and OLTP Systems for Better Performance
Most data teams struggle to balance their transaction processing needs with analytical requirements. They remain trapped in inefficient architectures that either slow down critical business operations or make analytics prohibitively expensive. But the real challenge isn’t a choice between OLTP and OLAP, it’s implementing both effectively within a cohesive data strategy that doesn’t break your budget or create technical debt.
The Problem: Inefficient Data Architecture Cripples Performance
As data volumes grow exponentially, companies find themselves facing a fundamental architecture challenge. Business applications require fast, reliable transaction processing (OLTP), while decision-makers need powerful analytical capabilities (OLAP). These competing needs create significant tension in how data is structured, stored, and processed.
Yet many organisations struggle with implementing proper data architecture because:
- Traditional ETL tools charge the same row-based rates regardless of data type, making it financially punitive to maintain both OLTP and OLAP systems
- Setting up dual-purpose environments is technically complex, requiring specialised skills and significant investment
- Synchronising data between systems introduces lag and inconsistencies, potentially compromising decision quality
- Moving between architectures creates migration challenges, leading to disruptions and reliability issues
These challenges leave many teams making painful compromises, either sacrificing analytical performance or slowing down critical business operations. The consequences are predictable: frustrated users, delayed insights, and competitive disadvantage.
What Smart Data Teams Do Differently
Forward-thinking data teams understand that OLTP and OLAP aren’t competing approaches, they’re complementary systems that each serve essential business needs. The key is implementing them strategically within a cohesive data architecture.
They Understand the Fundamental Differences
Smart data teams recognise that OLTP and OLAP systems are designed for fundamentally different purposes:
| Characteristic | OLTP (Online Transaction Processing) | OLAP (Online Analytical Processing) |
| Primary Purpose | Record-keeping and transaction management | Analysis and decision support |
| Data Model | Highly normalised (reduces redundancy) | Denormalised (optimised for queries) |
| Query Type | Simple transactions affecting few records | Complex queries across many records |
| Update Pattern | Frequent, small updates | Periodic bulk loads |
| Storage Focus | Row-oriented (optimised for lookups) | Column-oriented (optimised for aggregation) |
| Optimisation | Speed of individual transactions | Speed of complex analytical queries |
This clear understanding helps them architect systems that serve both operational and analytical needs effectively.
They Implement Purpose-Built Data Stores
On the Matatika platform, data architectures are structured using workspaces that allow for specialised data handling. Smart teams implement this separation:
- OLTP workspace with transaction-optimised databases for business applications
- Staging workspace where data transformation occurs
- OLAP workspace with columnar storage for high-performance analytics
This setup ensures each system excels at its primary purpose without compromising the other. Think of it like having a Formula 1 car for racing and an SUV for off-road, each vehicle optimised for its specific terrain.
They Optimise for Cost-Efficiency
Processing data multiple times across different systems can be wasteful and resource-intensive. With Matatika’s performance-based pricing, you can implement these optimisations:
- Incremental data processing that only moves changed records between systems
- Intelligent data syncing timed for minimal business impact
- Smart caching strategies that reduce redundant processing
Unlike row-based pricing models that charge for every processed row regardless of purpose, Matatika’s performance-based pricing means you pay for the infrastructure you actually use. Nothing more. There are no arbitrary row counts or compute inflation from inefficient syncs, just transparent costs that align with actual usage.
They Use Mirror Mode for Risk-Free Transitions
Moving between OLTP and OLAP architectures, or modernising either system, has traditionally been high-risk. Matatika’s Mirror Mode allows teams to implement new architectural approaches in parallel with existing systems. This four-step process ensures safety throughout:
- ASSESS – We review your existing data architecture and identify opportunities for improvement
- BUILD – We mirror your data ecosystem in parallel, without disrupting operations
- VALIDATE – Both systems run with real workloads, allowing you to verify everything works
- TRANSITION – Once confident, we coordinate a clean cutover timed with your renewal
This approach eliminates the uncertainty that typically makes architectural changes stressful and risky. As one data leader described it: “It’s like rebuilding the engine while the car is still running, and somehow making it go faster in the process.”
Learn more about Mirror Mode and how it works
How to Implement an Efficient OLTP-OLAP Architecture
A common challenge for data teams is implementing complementary OLTP and OLAP systems without doubling costs or creating maintenance nightmares. Here are three viable approaches supported by Matatika:
| Approach | Description | Pros | Cons |
| ETL-Based Integration | Traditional extraction, transformation, loading between systems | Well-established, reliable | Potential latency, higher processing costs |
| Unified Platform | Single system with hybrid capabilities | Simplified management, reduced complexity | May compromise on specialisation |
| CDC-Driven Replication | Change Data Capture for real-time synchronisation | Low latency, efficient | More complex to implement and monitor |
Implementing CDC for Efficient OLTP-OLAP Synchronisation
Instead of running full ETL processes that reprocess all data, Change Data Capture (CDC) offers a more efficient approach to keeping OLTP and OLAP systems in sync. This is especially valuable for organisations that need near real-time analytics without impacting transaction performance.
CDC works by:
- Monitoring the transaction logs of your OLTP database
- Capturing only the changes (inserts, updates, deletes)
- Applying those changes to your OLAP system incrementally
This approach dramatically reduces processing overhead while ensuring your analytical systems have current data. With Matatika’s performance-based pricing, this efficiency translates directly into cost savings.
For example:
- Traditional ETL might process 100GB of data daily to keep systems in sync
- CDC might process only 2GB of actual changes
- Result: 98% reduction in processing volume and corresponding cost savings
This setup has multiple benefits:
- Performance: Transactional systems remain fast and responsive
- Freshness: Analytical systems receive near real-time updates
- Efficiency: Processing overhead is minimised, reducing costs
- Simplicity: Less complex than building custom real-time solutions
And since Matatika provides built-in CDC capabilities, you don’t need to implement and maintain complex change tracking mechanisms yourself.
Supporting Insight: The Real Cost of Suboptimal Data Architecture
The financial impact of inefficient data architecture extends beyond just technical issues. Recent industry research reveals:
- Organisations with optimised OLTP-OLAP architectures achieve 72% faster time-to-insight compared to those using compromise architectures
- The average cost of poor data architecture decisions was estimated at £42,000 in wasted compute resources annually for mid-sized enterprises
- Teams leveraging purpose-built systems for each workload type reported 3.2x higher user satisfaction with both operational and analytical systems
These statistics highlight why proper data architecture is not optional, it’s essential for both efficiency and effectiveness.
One data leader put it plainly: “We spent two years trying to make our OLTP system handle analytics workloads. We ended up with a system that was mediocre at both jobs and excellent at neither.”
Key Takeaways
- A strategic approach to OLTP and OLAP systems is essential for balanced performance across transaction processing and analytics
- Use separate, purpose-built systems for operational and analytical workloads to maximise efficiency and minimise costs
- Implement intelligent data synchronisation between systems to ensure data consistency without excessive processing overhead
- Consider CDC approaches for near real-time analytics without compromising transactional performance
- Choose pricing models that reward efficiency rather than penalising data movement between systems
Frequently Asked Questions
How does Matatika’s approach to data architecture differ from traditional ETL tools?
Traditional ETL tools typically charge based on data volume, creating a financial disincentive to maintain proper OLTP and OLAP systems. Matatika’s workspace architecture and performance-based pricing provide clean environment separation with costs that reflect actual usage, not arbitrary row counts. This makes architectural best practices both technically simpler and financially feasible.
Do I need to duplicate all my data between OLTP and OLAP systems?
No. With Matatika, you can selectively replicate only the data necessary for analytics while maintaining transactional efficiency. Our platform enables you to define which entities and attributes need to be available for analysis versus those that should remain exclusively in the transactional system, saving both storage and processing resources.
How do I handle real-time analytics needs?
Matatika supports multiple approaches to near real-time analytics, including Change Data Capture (CDC) and streaming architectures. These options allow you to feed analytical systems with fresh data without impacting transaction processing performance. For most business needs, CDC provides the optimal balance between freshness, performance, and cost-efficiency.
Will separating OLTP and OLAP systems increase my infrastructure costs?
With traditional row-based pricing models, maintaining separate systems often did increase costs significantly. However, Matatika’s performance-based pricing means you only pay for the actual computational resources used, not duplicate data processing. Most organisations find that the efficiency gains from purpose-built systems more than offset any additional infrastructure requirements, resulting in net cost savings.
How long does it take to implement a proper OLTP-OLAP architecture?
With Matatika’s Mirror Mode, you can implement an optimised architecture in parallel with your existing systems, validating performance before making any cutover. The typical implementation timeline is 4-8 weeks, with no disruption to ongoing operations and complete safety throughout the process.
From Compromise to Optimisation
The shift from compromise architectures to properly optimised OLTP and OLAP systems doesn’t have to be complex or expensive. With Matatika, you can:
- Create purpose-built environments for both transaction processing and analytics
- Synchronise data efficiently between systems without inflating costs
- Pay only for the resources you actually use
- Implement changes safely with zero disruption to business operations
Ready to optimise your OLTP and OLAP architecture without the migration risks?
Most teams avoid architectural improvements because they lack a safe way to test new approaches alongside existing systems. The ETL Escape Plan changes that by providing tools to assess your current architecture, validate new approaches using Mirror Mode, and implement changes without operational disruption.
Download the ETL Escape Plan
A practical guide to switching ETL platforms without the risk, drama, or delay, including strategic frameworks for implementing optimised OLTP-OLAP architectures.
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