As founders, CTOs, and engineering leaders of data-intensive applications, you may be familiar with the challenge of balancing fast transactional processing and heavy analytical queries. Most applications start with an OLTP (Online Transactional Processing) backend that works well for managing real-time transactions. However, as your application's data requirements grow—whether for real-time analytics, machine learning models, or other data-heavy features—you’ll eventually need to move to an OLAP (Online Analytical Processing) backend to handle these workloads.

This guide walks you through four common stages of maturity for backends for data-intensive applications, providing best practices and technical recommendations for tools and technologies at each stage.

1. Initial Architecture: OLTP-based Backend
2. Mirrored OLTP Backend
3. Dedicated OLAP Backend
4. Fully Mature: Cloud-native, Real-time OLAP Backend

These stages are just a typical life-cycle: not every product or team will architect every stage. But generally it makes sense to start out with smaller investments in new products, and scale up your time and resources as the product gains traction and scale.

At the end of the article though, we’ll suggest that by leveraging data engineering frameworks like Moose, you can actually jump straight to a robust analytics architecture that will scale with your product as it grows (while still shipping quickly, with minimal upfront investment).

OLTP vs. OLAP: What’s the Difference?

Before diving into the architecture, it’s essential to understand the distinction between OLTP and OLAP:

• OLTP (Online Transactional Processing) systems are designed for managing transactional workloads in real-time, such as CRUD operations (Create, Read, Update, Delete) used by applications like ledgers, CRMs, etc. They handle a large number of short, atomic transactions and prioritize fast write operations. OLTP databases typically follow a row-based storage model and are optimized for transactional consistency and low latency. Examples: PostgreSQL, MySQL, Microsoft SQL Server.
• OLAP (Online Analytical Processing) systems, on the other hand, are optimized for read-heavy workloads and complex analytical queries over large datasets, like data visualization, leaderboards, data exploration, etc.. They support aggregation and multidimensional analysis, which are essential for business intelligence, reporting, and data mining. OLAP systems often use a columnar storage model to handle large data sets more efficiently and provide faster querying. Examples: Amazon Redshift, Google BigQuery, Snowflake.

Stage 1: Initial Architecture - OLTP-based Backend

This initial architecture stage is typified by a “fastest available to me” mindset—what backend architecture will most quickly unblock building my cool new idea for a data-intensive feature or product. At this stage, you're likely building analytics features on top of a transactional system that you already know and already have in place—typically a relational database such as PostgreSQL, MySQL, or even a managed solution like Neon, Supabase, or Amazon RDS. This can get you to market with your new feature very quickly, but puts your existing transactional workloads at risk with your new analytical queries potentially slowing down your core database.

Tools & Technologies used at this stage:

• Database: PostgreSQL or MySQL for relational storage, or NoSQL databases like MongoDB. And/or managed solutions like Neon or Supabase can abstract away a lot of the complexity of the OLTP stack to increase your dev speed and reduce your management overhead
• Caching: Redis or Memcached to reduce database load for frequently accessed data.

Best Practices:

• Schema Design: Leverage the existing (likely normalized) schemas in your transaction database. You may want to do some denormalization to make the data easier to work with for analytics, but be careful not to disrupt your existing workloads.
• Indexing: Use appropriate indexes to optimize query performance, but be mindful of the balance between read and write performance.
• Monitoring: Implement basic monitoring with Prometheus and Grafana to keep track of database performance and identify slow queries.

Pros:

• Fast time-to-market.
• Familiar technology stack.
• Sufficient for small datasets and moderate transactional workloads.

Cons:

• Performance degradation as analytics queries potentially interfere with transactional workloads
• Scalability issues when managing growing datasets and concurrent workloads

Stage 2: Mirrored OLTP Backend

This stage represents the first clashes with scale, with analytic workloads putting pressure on transactional workloads, and the fastest response being duplicate OLTP databases: one for transactional workloads and one for analytical, and a sync between them. This strategy buys you time, but it’s still not ideal for scaling analytics—the underlying infrastructure just wasn’t built for analytics. It also introduces the complexity of duplicate systems without giving you the benefit of matching the type of database to the requirements of the workloads.

Tools & Technologies:

• Replication: Use PostgreSQL Streaming Replication or MySQL Replication to replicate your OLTP database to a secondary instance.
• Read Replicas: Managed services like Amazon RDS Read Replicas or Google Cloud SQL can help you offload some read-heavy queries.

Best Practices:

• Asynchronous Replication: Configure asynchronous replication to reduce the load on your primary database and avoid bottlenecks.
• Analytics Tuning: Consider using materialized views to precompute analytics results and reduce the load on your transactional database.

Pros:

• Easy to set up with minimal changes to the existing stack.
• Can offload analytical queries to a secondary system without affecting transactional performance.

Cons:

• Still performing analytics on a transactional system, which isn’t optimized for large-scale queries.
• The solution is temporary, and the secondary OLTP database will eventually hit similar performance bottlenecks.

Stage 3: Create a Dedicated OLAP Backend

Once your data volume and analytics complexity reach a critical mass, it’s time to introduce a dedicated OLAP stack designed specifically for handling large-scale analytical workloads. At this stage, you move from a transactional database to a columnar or distributed database optimized for analytics. This is likely a significant rearchitecture introducing a whole new stack of technologies–this can bring major step changes in performance and scalability, but can come at a cost: requiring major time and resources to execute the rearchitecture, as well as potentially requiring more specialized data engineering skills to set up and maintain the new system.

Tools & Technologies:

• OLAP Databases: Amazon Redshift, Google BigQuery, Snowflake, or Clickhouse or Apache Druid for real-time analytics.
• ETL Tools: Use tools like Airflow, Fivetran, or dbt to extract, transform, and load data from your OLTP system into your OLAP database.
• Data Warehousing: Implement a data warehouse or data lake using Amazon S3 with Athena for querying or Google BigQuery for scalable querying.

Best Practices:

• Schema Design for OLAP: Opt for a star or snowflake schema to support fast and efficient querying across large datasets.
• Data Partitioning: Use partitioning and clustering to improve query performance on large datasets.
• Data Retention Policies: Set up policies to archive or delete old data to optimize storage and query performance.

Pros:

• Designed to scale for large analytical workloads.
• Can handle complex queries over large datasets with minimal performance impact on the application.

Cons:

• Time-consuming and costly to set up and maintain.
• Requires specialized data engineering skills for optimal configuration.
• Potential data synchronization issues between OLTP and OLAP systems.

Stage 4: Fully mature OLAP backend - cloud-native and real-time

As your application’s scale and complexity grow, you may find that your first OLAP architecture wasn’t optimized for your long-term needs. Maybe you’re hitting peak load that is 10x what you originally scoped for, or maybe it turns out you do need real-time streaming updates to create that stellar user experience, or maybe you want to migrate to the cloud to optimize your costs. This often leads to further re-architectures, where you redesign your OLAP stack to meet your current and future scalability requirements.

Tools & Technologies:

• Data Lakehouse: Solutions like Databricks Lakehouse or Apache Iceberg to unify batch and real-time analytics with machine learning capabilities.
• Streaming Analytics: Incorporate streaming technologies like Apache Kafka, Redpanda, or AWS Kinesis to support real-time data ingestion and analysis.
• Cloud-Native OLAP: Consider Snowflake or Google BigQuery for cloud-native, serverless OLAP solutions that automatically scale with your data and workloads.

Best Practices:

• Data Pipeline Optimization: Streamline ETL/ELT processes with real-time data ingestion tools like Kafka or Debezium to ensure continuous synchronization between your OLTP and OLAP systems.
• Data Governance: Implement strong data governance policies, using tools like Apache Ranger or AWS Lake Formation, to ensure compliance and data security.
• Cost Management: Use cost-optimization tools like AWS Cost Explorer or Google Cloud Cost Management to avoid runaway cloud costs from large-scale data processing.

Pros:

• Highly scalable and tailored to your exact data requirements.
• Supports both real-time and batch processing at scale.
• Eliminates many of the pain points of earlier architectures.

Cons:

• Requires significant upfront investment in time and resources.
• Complex to maintain, often requiring dedicated data engineering and infrastructure teams.

A Framework-Based Approach: Skip the Complexity

We’ve presented the above as if they are linear steps in a process, but really, they represent a series of trade-offs that are made at different stages of an application’s life-cycle: usually trading off long-term performance needs against speed of delivery and level of investment. But what if you could architect a robust and mature analytics backend from the start, while still maintaining rapid delivery and low overhead? Then there would be no reason to settle for the earlier “steps” in this process. That’s where framework-based approaches fit in—like the Moose framework built by Fiveonefour.

Data engineering frameworks abstract away the complexity of OLAP infrastructure and the specialized data engineering required to build it. This enables any developer who can work with a regular OLTP stack (eg. via SQL queries or APIs) to scale their application with OLAP from the start. By automating data pipelines, OLAP integration, and real-time processing, data engineering frameworks allow for rapid development of data-intensive features without re-architecture hurdles.

Conclusion

Migrating from OLTP to OLAP is a common path in the maturity of any data-intensive application. By utilizing the right stage-appropriate technology and best practices, you can ensure a smooth transition while optimizing for performance and scalability. However, a framework-based approach like Moose can help you leapfrog the typical challenges, enabling you to get to market quickly without sacrificing long-term growth and flexibility.