Now more than ever, businesses are focused on investing time and capital into their data platform. There is so much work that goes into processing data to generate insights, yet in my experience, there seems to be some confusion surrounding this core concept. When developing and deploying a data system, there are different ways to process one’s data and each of them are suited for different use cases. Sometimes, one needs to store and process real-time transactional data, and other times, one must store and process data for complex reporting requirements. The names of the systems that are best suited for each of these use cases are Online Transaction Processing (OLTP), and Online Analytical Processing (OLAP), respectively.

Each of these systems serve a distinct purpose within the data landscape, and each of these systems has properties that make them well suited for a specific workload. Properties such as purpose, optimization and structure are critical to each system’s operations. By ensuring you select the correct storage system, you also ensure that your processes will be operating as efficiently as possible.

In this article, I will walkthrough the differences between OLTP and OLAP systems. Each system has its own strengths and weaknesses as well as use cases. After introducing each system and its uniqueness, I will delve into how each of these systems interacts with front-end business intelligence (BI) tools. By the end of this article, you will have a holistic understanding of OLTP and OLAP systems, how they fit into your data platform and how they will help you maximize your business’s investment in your data platform.

Online Transaction Processing (OLTP)

Definition

Online Transaction Processing (OLTP) refers to a data processing system designed to manage and execute real-time transactional data. Considering the system is designed for real-time processing, this means it needs to be optimized for quick, efficient and frequent queries. These queries are typically executed with high concurrency for a high volume of small transactions. Some examples of situations where OLTP systems are most suitable include processing orders, handling payments and/or updating inventory levels.

Use Cases

OLTP systems are great for ensuring you have high availability, low latency and frequent updates to your data. These systems work best with front-end applications where users are directly interacting with the system and transactions in real time. Some example scenarios of use cases for OLTP systems include:

• Processing retail transactions (e.g., point-of-sale systems)
• Handling online banking transactions (e.g., transferring money between accounts)
• Managing reservation systems (e.g., airline or hotel bookings)
• Running e-commerce platforms (e.g., placing orders, updating inventories)

Examples of OLTP Databases

Some of the most commonly utilized OLTP systems used in the modern data landscape includePostgreSQL, SQL Server and Oracle. These systems are optimized for the quick, efficient and frequent queries that were discussed earlier:

• PostgreSQL: An open-source relational database known for its flexibility, making it a popular choice for a wide range of applications.
• SQL Server: A powerful OLTP system favored by many businesses for its performance, security and integration with the Microsoft ecosystem.
• Oracle: An enterprise-grade solution recognized for its scalability, reliability and robust feature set, making it ideal for transactional applications.

Limitations

While OLTP systems are great for a high volume of small transactions, they are not ideal for analytical tasks that require processing large quantities of data. In other words, when we need to write complex queries, aggregate metrics or analyze patterns across large datasets, an OLTP system is not the ideal solution. Some example scenarios where OLTP systems should not be used include:

• Performing business intelligence and reporting tasks.
• Running large-scale data mining operations.
• Conducting deep analysis of historical data for trend identification.

There are several reasons why OLTP systems are not well suited for the use cases listed above, and some of those reasons include:

• Scalability for Analytical Queries: OLTP databases are not built for handling complex queries that require scanning large datasets, which can lead to performance bottlenecks.
• Data Duplication: Maintaining the same data in multiple OLTP systems (e.g., across different regions) can introduce complexity in ensuring consistency and avoiding duplication.
• Limited Data History: OLTP systems are generally focused on current, operational data and often purge historical data to maintain performance, making them unsuitable for long-term trend analysis.
• System Overhead: Managing high transaction volumes can lead to increased overhead for maintaining data integrity, ensuring isolation of transactions and handling concurrent access.

Online Analytical Processing (OLAP)

Now that we have discussed the pros and cons of OLTP systems it is time to flip to the other side of the coin and walkthrough designing an managing data platforms for complex analysis. This style of system is known as an Online Analytical Processing (OLAP) system and it differs from the approach of an OLTP system in multiple ways. Without further ado, let us dive into the specifics of an OLAP system.

Definition

Online Analytical Processing (OLAP) refers to a data processing system designed to query and analyze large volumes of data. Unlike OLTP systems that handle large amounts of small transaction, an OLAP system is designed for analyzing high volumes of data through processes such as aggregation across multiple dimensions by which an end-user can slice and dice the data. In other words, OLAP systems enable end-users to identify patterns within their datasets that in turn help decision makers drive growth within their business.

Use Cases

OLAP is typically used when data analysis, trend identification and complex reporting are core requirements of the system being implemented. This style of system enables end-users to explore data across a range of hierarchies and levels of detail that are intended to give various perspectives on the business’s performance. Some example scenarios of use cases for OLAP systems include:

• Creating dashboards and reports that aggregate sales data by region, product line or time period.
• Analyzing customer behavior patterns and preferences for targeted marketing campaigns.
• Conducting financial analysis, such as budgeting and forecasting.
• Performing risk assessment and fraud detection in banking or insurance sectors.

Examples of OLAP Databases

When designing an OLAP system some popular options for storing and handling your data include Snowflake, BigQuery and Redshift. These data platforms are designed to support data warehousing and analytical workloads:

• Snowflake: Known for its multi-cluster architecture, Snowflake is best at handling large datasets and allowing businesses to scale their data processes easily.
• Redshift: Amazon Redshift is a fully managed data warehouse service that enables efficient querying across large datasets, making it ideal for businesses needing to perform complex analytics.
• BigQuery: Offers serverless, highly scalable data warehousing with built-in machine learning capabilities, making it a go-to solution for organizations looking to analyze vast amounts of data quickly.

Limitations

An OLAP system is similar to an OLTP system in that it has its drawbacks as well. As you may have guessed OLAP systems are not ideal for handling a large volume or small transactions quickly and efficiently. Some example scenarios where an OLAP system is not the correct choice include:

• High Resource Requirements: OLAP systems typically require a large amount of compute resources to process and store the historical data that is being queried.
• Latency: OLAP systems are typically designed for processing large batches of data, so they are not ideal for real-time decision-making.
• Data Integration Complexity: OLAP systems pull data from multiple sources, so data consistency and integration can be difficult to manage.
• Cost: OLAP systems typically can be costly for organizations that may not have the budget today to setup this style of data system.

Comparison: OLTP vs. OLAP Systems

While both OLTP and OLAP systems are critical to designing a holistic data platform, they do serve different purposes that are ideal for different use cases. This section will provide more context and a direct comparison between these two systems particularly in regards to performance, speed and data structure.

Performance and Speed

When it comes to OLTP systems, they are designed for handling high volumes of small transactions in the most efficient manner possible. They deliver results with low-latency for individual queries, such as retrieving customer reservation or processing an insurance claim. This speed is achieved through:

• Optimized indexing: OLTP databases typically have indexes that allow for rapid lookups and efficient handling of simple queries.
• Concurrency management: OLTP systems efficiently handle multiple users performing transactions simultaneously, maintaining data integrity and speed.
• Small transactions: Since OLTP data points are typically smaller this allows for more efficient processing of the desired record.

On the other hand, OLAP systems are optimized for complex, large-scale queries that aggregate data across multiple dimensions. While they may not provide the quick responses that an OLTP system does, OLAP systems are built to process large datasets efficiently when it comes to analytical tasks. Performance optimizations in OLAP include:

• Pre-aggregated data: OLAP systems will often include pre-aggregated data that enables end-users to more quickly query and analyze datasets.
• Batch processing: OLAP typically processes batches of data in order to generate reports and conduct data analysis. This style of processing will take longer to execute but will provide more accessible insights for end-users.

The key distinction in performance and speed lies in the nature of the queries:

• OLTP excels at high-speed processing of numerous small transactions that require immediate responses.
• OLAP systems are optimized for analyzing large datasets and providing answers to complex, multidimensional queries, though this process generally takes more time compared to OLTP’s real-time operations.

Data Structure and Design

The data structure and design of both OLTP and OLAP systems serve a distinct purpose that lead to differences in their configuration:

• OLTP Systems: OLTP systems are typically normalized, meaning that there is not much redundancy within the data. This is critical for data consistency and storage optimization. Normalization in an OLTP system is also helpful in managing datasets and identifying oddities.
• OLAP Systems: On the other hand, OLAP systems typically use a denormalized structure in order to optimize query performance. A couple of schema designs seen most frequently include star and snowflake schemas, where fact tables are surrounded by dimension tables that can be joined together on common keys. This flavor of design reduces the need for complex computations during analytical workloads.

Now that we have compared OLTP and OLAP systems in terms of performance, speed and data structure we can identify the system that fits our use case based on a few core requirements. While these requirements are prevalent in data architecture in our next section we will also see how a back-end data processing system can be dictated by a front-end BI software.

BI Interaction: Leveraging OLTP and OLAP Systems With Front-End BI Tools

Overview of Front-End BI Tools

Front-end Business Intelligence (BI) tools are an essential component to the success of an organization’s data platform. In today’s marketplace there is no shortage of technologies that can meet a company’s BI needs by offering the ability to create data visualizations and dashboard that enable users to analyze datasets through real-time interaction. Some popular BI tools that we like to use at InterWorks include:

• Snowflake Cortex AI: A new product offering from Snowflake, this BI tool leverages AI to generate ad hoc reports with ease. Users can ask a question of the service and it will analyze your dataset for you to generate the ideal visualization.
• Tableau: Known for its simple and sleek design, Tableau is a titan in the BI marketplace. It is used widely to build out data visualizations and dashboard for a wide variety of use cases.
• Power BI (PBI): Optimized for integration with Microsoft systems PBI is similar to Tableau in that it has a drag-and-drop design as well as a user-friendly experience. It is also used to developing workbooks containing visualizations and dashboards.
• ThoughtSpot: Unique in its own right, ThoughtSpot is optimized to generate reports based on star schema data models. Similar to Cortex, ThoughtSpot leverage AI to create reports, thus making it incredibly useful in ad hoc reporting.

Each of the tools above are designed to handle large datasets that are typically sourced from OLAP systems.

OLAP’s Role in Enhancing User Experience

OLAP systems are the foundation on which front-end BI tools rest. This style of system enables BI tools to operate smoothly and efficiently so end-users to gain perspective on how their business is performing. There are plenty of ways the OLAP systems enhance the operation of BI tools and some of those include:

• Pre-Aggregated Data for Faster Queries: The design of OLAP systems is catered to pre-aggregating data across multiple dimensions, thus allowing the BI tool to query data quickly without having to calculate metrics.
• Multidimensional Data Modeling: OLAP systems are ideal for multidimensional models that enable users to slice and dice their data in a manner that offers additional perspective based on qualitative data points.
• Advanced Analytics: OLAP systems are also great for machine learning and predictive analytics. These models can be visualized through BI tools to forecast performance and identify patterns.

These enhancements in user experience become more apparent when we tie this back to real-world examples for the toolset listed out above. These examples include:

• Snowflake Cortex AI with OLAP: Allows data practitioners to analyze pre-aggregated data using AI. This enables users to efficiently utilize machine learning models on their data, thus unlocking the power of predictive analytics.
• Tableau with OLAP: Allows users to build interactive workbooks and dashboards that enable a user to drill data aggregations using a multitude of dimensions. For example, a retail company could visualize t-shirt sales by region for a specific calendar year. 
• Power BI with OLAP: Provides real-time analytics and reporting through interactive workbooks and dashboarding. For example, Power BI can connect to a financial data source to visualize risk analysis and performance metrics across an entire organization.
• ThoughtSpot with OLAP: Enables users to search across star schema models to create reports on an ad hoc basis. For example, imagine a marketing manager wanted to see total spend on an advertising platform for the month of August 2024. They could ask a question like, “How much did the company spend on the generic platform in the month of August 2024? How does that compare to the previous months in 2024?” and the user will immediately receive a visualization.

While OLAP systems are phenomenal for interacting with front-end BI tools it is important to acknowledge that OLTP systems also serve an important purpose as well. Each flavor of design has its place in the data landscape and is optimized to serve a specific use case.

Conclusion

The more I think about OLTP versus OLAP systems the more I realize that both are important components to a holistic data platform. There are situations where technologies such as PostgreSQL, SQL Server and Oracle are necessary for processing a high-volume of small transactions for near instantaneous data interaction. There are also situations where technologies such as Snowflake, BigQuery and Redshift are necessary for processing and storing aggregations of data that enable users to execute drill down analysis to drive decision making. These systems are OLTP and OLAP, respectively, and both have their own place within your data ecosystem.

Ultimately, a well-rounded data architecture includes both OLTP and OLAP systems so long as your organization has data sources that include transactional and analytical data. If you are part of an organization that could benefit from discussing and identifying methods to improve your data architecture, then please feel free to reach out to us at InterWorks here.