The Yin and Yang of Data Processing: OLAP vs OLTP Systems Demystified

Why is it important that Data Engineers(DE) have good knowledge of OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) systems when it comes to modelling their data structure. Does it make a difference and in what situation do we adopt the right modelling strategy.

Understanding which data management system to build—OLAP (Online Analytical Processing) or OLTP (Online Transaction Processing)—is crucial because each system serves different purposes and is optimized for different types of operations. Knowing the distinction and choosing the right one ensures that your data infrastructure aligns with your business needs and provides the appropriate performance and functionality.

Online Analytical Processing (OLAP)

Understanding the business needs and what is the requirements that is needed, ensures a system is built that enables a framework which allows the business to maximus the way they use their data.

Some of the typical cases you may face as a requirement from businesses are:

• Handling real-time transaction processing

• Complex data analysis and decision support

• Managing updates regularly

• Forecasting future trends

• Identifying cross-selling and up-selling opportunities.

It's really important that understanding what the business needs are. In order to this, you need to extract as much information as possible from the business use case and this will enable you to design a system that will be optimal for the customers needs.

So if the customer ultimate purpose is to:

• Analyze data

• Create dashboards and reports

• Fast queries as a priority

• Identify trends and opportunities

Then the aim is to design an OLAP system. OLAP systems are designed for complex queries and analysis rather than transactional operations. They are used to analyze multidimensional data from multiple perspectives, typically for business intelligence (BI) purposes.

OLAP systems and denormalized data structures complement each other to provide high-performance, efficient, and user-friendly data analysis solutions. By leveraging denormalized data, OLAP systems can deliver faster query responses and simplified data retrieval, making them essential for business intelligence and analytical tasks.

Features of a denormalized data structure:

• Reduces the need for complex joins, leading to faster query execution.

• Increase in redundant data

• Optimized for read-heavy workloads typical in analytical queries.

• Provides a single, comprehensive view of data, making it easier for reporting and analysis

Online Transaction Processing (OLTP)

What about in the case if the client wants to:

• Handles a large number of short online transactions (insert, update, delete)

• Strong emphasis on data integrity and accuracy

• Capable of scaling vertically (adding more power to existing servers) and horizontally (adding more servers)

• Optimized for fast query response times for simple, frequent queries

Then the aim would be to design an OLTP system. By incorporating these features, OLTP systems ensure efficient, reliable, and secure transaction processing essential for day-to-day operations in businesses.

OLTP systems and normalized data are closely related as both prioritize data integrity, ACID properties, transaction management, redundancy reduction, and efficient query processing. Normalization principles guide the design of OLTP systems to ensure reliable and consistent transaction processing.

Features of a denormalized data structure are:

• Minimization of redundant data

• Each piece of information is stored in its most atomic form, ensuring that data is not duplicated or spread across multiple locations

• Data integrity and consistency is achieved

Incorporating OLAP and OLTP systems

It seems like both these systems have their benefits and provide key valuable attributes. So is their a way perhaps we can use both of these. Yes we can.

Integrating OLTP and OLAP systems within an ETL (Extract, Transform, Load) pipeline can be advantageous for organizations seeking to leverage both real-time transactional data and historical analytics. Here's how they work together in the ETL process:

1. Extract: OLTP systems are the primary data sources for the ETL pipeline, providing real-time transactional data from operational databases. Data extraction from OLTP systems involves capturing new transactions as they occur and extracting relevant data fields required for analysis.

2. Transform: During the transformation phase, data from OLTP systems undergoes various transformations to prepare it for analytics in the OLAP system. Transformations may include data cleansing, normalization, aggregation, and enrichment to ensure data consistency and quality.

3. Load: Transformed data is loaded into the OLAP system, where it is stored in a structured format optimized for analytical queries. OLAP systems typically use denormalized data structures for efficient querying and analysis, which may involve further data modeling and optimization during the loading process.

Summary

OLAP systems are designed for complex data analysis and decision support, using denormalized structures for fast query performance and historical data analysis. They enable advanced reporting and business intelligence. OLTP systems manage daily transactional data with normalized structures for high data integrity and fast processing, supporting real-time updates and concurrency. Together, they ensure efficient operations and strategic insights, enhancing both daily functions and long-term planning for businesses.

What systems do you incorporate to manage your data effectively?