OLAP vs OLTP: Differences in the Data Warehouse Architecture

What is OLAP?

OLAP is software that enables the rapid multidimensional analysis of large volumes of data from a data warehouse, data mart, or centralized data store.

Enterprises gather and store data from multiple data sources, including applications, websites, smart meters, internal systems, etc. OLAP groups and categorizes this data to deliver valuable insights crucial for strategic decision-making.

OLAP operates on a multidimensional data model representing data in more than two categories or dimensions. An OLAP system consists of the following elements:

• Data warehouse: The centralized repository that stores large volumes of historical and real-time data from various sources, organized in a structured manner to facilitate analysis. For instance, the data warehouse could gather data from a relational database structured in tables with rows and columns.
• ETL tools (Extract, Transform, Load): These tools are responsible for extracting data from diverse sources, transforming it into a format suitable for analysis, and loading it into the data warehouse. Data warehouses use ETL processes to transform and standardize data from diverse sources, ensuring its compatibility with OLAP tools.
• OLAP server: This is the server responsible for managing and processing OLAP queries. It transforms data within relational databases using ETL tools, preparing it for OLAP operations.
• OLAP database: This database is a separate database connected to the data warehouse to avoid burdening it with OLAP analysis. An OLAP database can also simplify the creation of OLAP models.
• OLAP cubes: These are multidimensional structures representing data in the form of a cube, with measures at the intersections of dimensions. These cubes exhibit rigidity since modifying dimensions and the underlying data post-modeling isn't feasible. For instance, adding a warehouse dimension to a cube with customer demographics, product categories, and sales regions will require remodeling the entire cube.
• OLAP analytic tools: These tools provide the user interface for interacting with OLAP systems. They are used for executing operations such as dicing, slicing, and pivoting to gain details within the OLAP cube and extract more insights.

What is the OLAP Cube?

OLAP cube is an array-based multidimensional data structure designed for swift and efficient data processing and analysis. It consists of multiple dimensions to enhance performance. While relational databases struggle with querying and analyzing multidimensional data as they grow, the OLAP Cube's design allows for seamless data exploration across various dimensions.

Their design enables OLAP cubes to store data in an aggregated form. Moreover, the precomputed assortment of values in the cube allows it to possess pre-existing answers to users’ queries. The schematic representation of this system featuring OLAP cubes is depicted in the following illustration.

The OLAP cube supports operations like drill down, roll up, slice, dice, and pivot, providing users with ways to interact with data. Drill down dives into more detailed data by moving down conceptual hierarchies, while roll up summarizes data by moving up hierarchies.

Slicing and dicing create subcubes by selecting dimensions. Pivot allows users to rotate the cube view for versatile, multidimensional data representations. These operations empower users to extract nuanced insights from complex datasets.

Popular OLAP tools

Let’s explore three notable applications known for their versatility and performance in business intelligence.

IBM Cognos

IBM Cognos is one of the leading web-based reporting and multidimensional analytics systems. It is an enterprise software that offers an integrated toolkit for data exploration, analysis, reporting, and scorecarding. With components like Cognos Report Studio for versatile report creation, it efficiently meets diverse organizational information needs. Cognos Analysis Studio enables in-depth analysis of complex data sources using key OLAP features like roll-up and drill-down.

Microsoft SQL Server Analysis Services (SSAS)

Microsoft SSAS is a multidimensional OLAP tool within SQL Server. It empowers IT professionals to dissect large datasets, organizing them into easily navigable cubes. SSAS provides a unified tool for comprehensive analysis and decision-making from dispersed data across various databases, tables, or files.

OBIEE

Oracle's Business Intelligence Enterprise Edition (OBIEE) is a robust tool designed by Oracle Corporation for gathering, storing, and visualizing enterprise data. OBIEE is an open-source platform that provides users with deeper insights. It also facilitates rapid decision-making through interactive dashboards, metadata searches, real-time alerts, and operational reporting.

What is OLTP?

OLTP is a database management approach that handles and records day-to-day transactional activities. It facilitates the immediate execution of numerous database transactions by a large user base, often conducted via the Internet.

A database transaction within OLTP involves altering, inserting, deleting, or querying data. It drives our financial interactions, including online banking, in-store purchases, hotel reservations, airline bookings, and beyond.

OLTP systems often rely on relational database management systems, chosen for their ability to handle numerous queries and updates while maintaining fast response times. This approach proves effective, especially for frontline workers like cashiers and bank tellers, who generate online transactions.

Online transaction processing systems typically adopt a 3-tiered architecture to support these functionalities. This includes:

• Presentation tier: This tier handles the user interface (UI) and interactions, providing a platform for users to input data or initiate transactions. UIs can vary from traditional desktop applications to web-based interfaces for online transactions. They can vary in terms of design aesthetics, navigational structures, or functionality to adapt to various platforms and devices.
• Business logic tier: This tier processes the business logic and manages the data flow between the presentation and the data tier. Transactional processing logic is implemented at this level to ensure the correctness and integrity of the data during updates. OLTP applications often include validation, authorization, and error-checking mechanisms to guarantee accurate and secure transaction processing.
• Data tier: The data tier is the foundation of OLTP, consisting of a database management system (DBMS). DBMS efficiently stores and retrieves data, supporting the high volume of queries and updates required by OLTP applications.

The three-tiered architecture of OLTP ensures a structured and efficient processing model. Each tier operates independently and contributes to transactional data's responsiveness and accuracy.

Differences and similarities between OLAP and OLTP

As discussed earlier, OLTP operates on the frontlines of day-to-day transactions, while OLAP helps generate valuable insights and information.

Looking closely at their key features reveals clear differences and shows how OLTP and OLAP work together to handle different aspects of data processing.

Choosing between OLAP and OLTP: practical applications

The choice between OLAP and OLTP systems depends on the organization's specific needs and the nature of the tasks. OLAP is ideal for scenarios requiring in-depth analysis, trend identification, and strategic decision-making. It excels in applications like business intelligence, where historical data analysis is necessary.

On the other hand, OLTP is ideal for day-to-day operations, like order processing, inventory management, and online banking, where immediate transactional processing with low response times is required. The models are not mutually exclusive. Organizations often implement both to address diverse operational and analytical requirements effectively.