Online Analytical Processing | Definition, Types, Example, Tools, Advantages & Disadvantages

What is Online Analytical Processing ?

Online Analytical Processing (OLAP) is a technique used in data analytics to enable users to interactively analyze multidimensional data from multiple perspectives. Unlike Online Transaction Processing (OLTP), which focuses on managing and processing transactions in real-time, OLAP focuses on complex queries and analysis of data stored in a multidimensional database, often referred to as a data cube. These databases are optimized for querying and reporting rather than transactional processing.

OLAP allows users to perform tasks such as data mining, trend analysis, and forecasting by providing a flexible and intuitive interface for exploring data. One of the key features of OLAP systems is their ability to handle large volumes of data and provide quick responses to ad-hoc queries. This is achieved through techniques like pre-aggregation and indexing, which optimize the performance of queries by pre-calculating and storing aggregated values.

There are two main types of OLAP systems: MOLAP (Multidimensional OLAP) and ROLAP (Relational OLAP). MOLAP systems store data in a multidimensional format, typically using a proprietary storage engine optimized for OLAP queries. ROLAP systems, on the other hand, store data in a relational database and use SQL queries to perform OLAP operations. Additionally, there is a hybrid approach called HOLAP (Hybrid OLAP), which combines elements of both MOLAP and ROLAP.

Online analytical processing plays a crucial role in business intelligence and decision-making by providing analysts and decision-makers with powerful tools for exploring and analyzing large volumes of data from different perspectives. Its ability to handle complex queries and provide rapid responses makes it an essential component of modern data analysis workflows.

Online Analytical Processing Definition

Here's how various authors and sources define OLAP:

1) Microsoft:

Microsoft defines OLAP as "a technology that enables analysts to extract and view business data from different points of view." It emphasizes OLAP's role in providing multidimensional views of data for analysis.

2) Kimball Group:

The Kimball Group, a prominent authority on data warehousing and business intelligence, defines OLAP as "the dynamic synthesis, analysis, and consolidation of large volumes of multidimensional data." They stress OLAP's role in dynamic analysis and consolidation of multidimensional data.

3) Oracle:

Oracle defines OLAP as "a category of software technology that enables analysts, managers, and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user." Oracle's definition highlights OLAP's role in providing fast, consistent, and interactive access to various views of transformed data.

4) Wikipedia:

Wikipedia defines OLAP as "an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing, and data mining."

5) IBM:

IBM defines OLAP as "a category of software tools that provide analysis of data stored in a database. OLAP tools enable users to analyze different dimensions of multidimensional data." IBM's definition underscores OLAP's role in analyzing data stored in databases and its focus on multidimensional analysis.

Types of Online Analytical Processing

There are mainly three types of OLAP systems:

1) Multidimensional OLAP (MOLAP):

MOLAP systems store data in a multidimensional array storage engine. These systems pre-calculate and store aggregations to improve query performance. MOLAP databases are optimized for fast querying and analysis of multidimensional data. They provide fast response times for complex analytical queries but may require substantial storage space for pre-calculated aggregations.

2) Relational OLAP (ROLAP):

ROLAP systems store data in relational databases and use relational database management system (RDBMS) engines to perform OLAP operations. They do not require pre-aggregated data and instead rely on SQL queries to access and manipulate data directly from the relational database. ROLAP systems are more flexible and scalable than MOLAP systems but may suffer from slower query performance, especially for complex analytical queries.

3) Hybrid OLAP (HOLAP):

HOLAP systems combine elements of both MOLAP and ROLAP approaches. They store some data in a multidimensional format for fast querying and analysis (similar to MOLAP), while storing other data in a relational format for flexibility and scalability (similar to ROLAP). HOLAP systems aim to leverage the strengths of both MOLAP and ROLAP while minimizing their weaknesses. They offer a balance between query performance and flexibility, making them suitable for a wide range of OLAP applications.

Online Analytical Processing Example

Let's consider a retail company as an example of OLAP usage.

Imagine a retail company that operates stores in multiple locations and sells various products. The company collects data on sales transactions, including information such as the date of sale, store location, product sold, quantity sold, and revenue generated. This data is stored in a data warehouse for analysis.

Now, using OLAP, the company can perform various analyses to gain insights into their business operations:

• Sales Performance Analysis: A retail company uses OLAP to analyze sales data, identifying top-selling products and trends over time.
• Inventory Management: OLAP helps optimize inventory management by analyzing stock levels and turnover rates across different store locations.
• Customer Segmentation: The company segments customers based on purchasing behavior using OLAP, enabling targeted marketing campaigns and personalized offers.
• Market Basket Analysis: Market basket analysis with OLAP reveals associations between products, guiding product placement and cross-selling strategies in stores.

Overall, OLAP enables the retail company to perform in-depth analysis of sales data, inventory levels, customer behavior, and market trends to make informed business decisions and drive growth and profitability.

Online Analytical Processing Tools

Several software tools and platforms are available for implementing OLAP solutions. Here are some popular examples:

1) Microsoft SQL Server Analysis Services (SSAS):

SSAS is a multidimensional and data mining analysis tool in the Microsoft SQL Server suite. It allows users to create OLAP cubes and perform multidimensional analysis using MDX (Multidimensional Expressions) queries.

2) IBM Cognos Analytics:

IBM Cognos Analytics is a comprehensive business intelligence platform that includes OLAP capabilities. It enables users to create interactive dashboards, reports, and multidimensional analysis to gain insights from their data.

3) Oracle OLAP:

Oracle OLAP is a component of the Oracle Database that provides native support for OLAP functionality. It allows users to build and deploy multidimensional cubes for analysis using Oracle's SQL-based OLAP query language.

4) SAP BusinessObjects BI Platform:

SAP BusinessObjects BI Platform offers OLAP capabilities through tools like SAP BusinessObjects Analysis for Office and SAP BusinessObjects Analysis for OLAP. These tools enable users to perform multidimensional analysis and create interactive reports and dashboards.

5) MicroStrategy:

MicroStrategy is a business intelligence platform that includes OLAP functionality for analyzing large datasets. It offers tools for building OLAP cubes, creating interactive visualizations, and sharing insights with stakeholders.

6) Tableau:

Tableau is a popular data visualization and analytics platform that supports OLAP analysis. It allows users to connect to multidimensional data sources, create interactive visualizations, and perform ad-hoc analysis using drag-and-drop tools.

7) Pentaho Mondrian:

Pentaho Mondrian is an open-source OLAP server that provides support for OLAP analysis within the Pentaho BI Suite. It allows users to create and deploy OLAP cubes for multidimensional analysis using a web-based interface.

These are just a few examples of OLAP tools available in the market, each offering various features and capabilities for analyzing multidimensional data and gaining insights into business operations. Organizations can choose the tool that best fits their requirements in terms of functionality, scalability, and compatibility with existing IT infrastructure.

Advantages of Online Analytical Processing

1. Facilitates complex analysis: OLAP enables users to perform multidimensional analysis and gain insights from large volumes of data, supporting complex queries and reporting.
2. Faster decision-making: By providing fast query response times, OLAP systems empower organizations to make timely decisions based on real-time data analysis.
3. Enhanced data visualization: Online analytical processing tools offer interactive visualizations and dashboards, allowing users to explore data dynamically and gain deeper insights into business performance.
4. Improved planning and forecasting: OLAP enables organizations to conduct trend analysis and forecasting, helping them anticipate market trends and plan future strategies effectively.
5. Supports data-driven decision-making: Online analytical processing provides a comprehensive view of business data, empowering decision-makers to make informed choices based on accurate and up-to-date information.

Disadvantages of Online Analytical Processing

1. Complexity of implementation: Setting up OLAP systems can be complex and time-consuming, requiring expertise in data modeling, database design, and query optimization.
2. Costly infrastructure requirements: Online analytical processing systems may require significant investment in hardware, software, and ongoing maintenance, making them costly to implement and operate.
3. Data latency: OLAP systems may suffer from data latency issues, especially when dealing with large volumes of real-time data, leading to delays in decision-making.
4. Limited support for transactional processing: Online analytical processing is optimized for analytical processing and may not be well-suited for transactional processing tasks, such as data entry and updates.
5. Scalability challenges: As data volumes grow, OLAP systems may face scalability challenges, leading to performance bottlenecks and increased response times.

How Do Online Analytical Processing Systems Improve Performance ?

Online Analytical Processing (OLAP) systems improve performance through several mechanisms:

1. Pre-Aggregation: Online Analytical Processing (OLAP) systems enhance performance by pre-calculating and storing aggregated values, reducing computational overhead during query execution.
2. Indexing: These systems utilize indexing techniques to optimize query performance, allowing for quick retrieval of relevant data based on query predicates and aggregations.
3. Data Cubes: OLAP organizes data into multidimensional structures, known as data cubes, facilitating fast querying and analysis by pre-structuring data along multiple dimensions.
4. In-Memory Processing: Some OLAP systems leverage in-memory processing, storing data in RAM rather than on disk, to accelerate query execution by reducing disk I/O overhead.
5. Parallel Processing: This techniques are employed to distribute query workload across multiple processors or nodes, enabling faster query response times and improved scalability.
6. Compression: These techniques may be used to reduce storage space and improve query performance by storing more data in memory.
7. Query Optimization: This strategies such as join reordering and predicate pushdown are applied to minimize resource utilization and maximize query performance.

OLAP vs OLTP

Here are the differences between OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing):

Aspect	OLAP	OLTP
Purpose	Analytical - Supports complex queries for data analysis and reporting.	Transactional - Supports day-to-day transaction processing and data manipulation.
Data Usage	Historical and aggregated data.	Current and detailed transactional data.
Data Structure	Multidimensional (e.g., cubes)	Relational (tables with rows and columns)
Query Complexity	Complex queries involving aggregation and data summarization.	Simple, individual record retrieval and modification.
Performance	Designed for read-heavy workloads with less emphasis on real-time data updates.	Optimized for write-heavy workloads with frequent data updates.
Indexing	Typically pre-aggregated and indexed for faster query response.	Often relies on traditional indexing methods for efficient data retrieval.
Data Granularity	Aggregated data at various levels of detail (e.g., daily, monthly).	Detailed, transaction-level data.
Users	Typically analysts, managers, and decision-makers for data analysis.	Front-end users, applications, and backend systems for transaction processing.