Online Analytical Processing, commonly known as OLAP, is a computing approach that allows users to easily and selectively extract and view data from different points of view. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining.
The primary objective of OLAP is to allow users to generate ad hoc reports quickly, containing information that has been summarized into multidimensional views and hierarchies. OLAP tools enable users to analyze different dimensions of multidimensional data. For example, it provides time series and trend analysis views.
Definition of OLAP
Online Analytical Processing (OLAP) is a category of software tools that analyze data stored in a database. It is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. OLAP is used in a wide range of applications including business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with each area having its own dedicated application.
OLAP tools enable users to analyze database information from multiple database systems at one time. The order and manner in which information is viewed can be manipulated by users, allowing for a wider perspective on the information. Users can also drill down into the data to view more detailed information.
Types of OLAP
There are three main types of OLAP systems: Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP), and Hybrid OLAP (HOLAP). MOLAP is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database.
ROLAP differs significantly in that it does not require the pre-computation and storage of information. Instead, ROLAP tools access the data in a relational database and generate SQL queries to calculate information at the appropriate level of detail when responding to a user's request.
HOLAP incorporates elements of both MOLAP and ROLAP. HOLAP tools can utilize both pre-calculated cubes and relational data sources.
History of OLAP
The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing). Edgar F. Codd, who is known as the inventor of the relational model, introduced the term in a white paper that he wrote while he was working at IBM. He also outlined twelve rules to which a fully functional OLAP solution should adhere.
The concept of OLAP has been around since the late 1970s, but it didn’t gain the name OLAP until the early 1990s. The growth of the Internet and the ability to distribute data across geographies led to a growth in the popularity of OLAP databases, which are good at managing multidimensional data sets.
Evolution of OLAP
The evolution of OLAP has been influenced by the increasing need for sophisticated data analysis tools. The first generation of OLAP tools were based on the concept of a cube, also known as a multidimensional database. These tools were able to provide fast response times to complex analytical queries because they pre-calculated all possible aggregations of the data and stored them in the cube.
However, these first-generation tools had limitations, particularly in terms of the size of the data they could handle. This led to the development of ROLAP tools, which could handle larger volumes of data because they stored the data in a relational database and only calculated the required aggregations on the fly.
The latest generation of OLAP tools, often referred to as HOLAP or Hybrid OLAP, combine the advantages of MOLAP and ROLAP. They can handle large volumes of data like ROLAP tools, but they also have the fast response times of MOLAP tools because they store some pre-calculated aggregations.
Use Cases of OLAP
OLAP systems are used in a wide range of applications and industries. They are particularly popular in business reporting environments. For example, a typical OLAP query might be to calculate the total sales of a particular product over a certain period of time, broken down by region and compared to the same period in the previous year.
OLAP systems are also used in data mining, where they can be used to identify patterns in large datasets. For example, an OLAP system might be used to identify customers who are likely to churn based on their behavior patterns.
OLAP in Business Intelligence
OLAP is a key component of Business Intelligence (BI) solutions. BI tools use OLAP to provide users with a multidimensional view of their data, allowing them to analyze data from different perspectives and at various levels of detail. This can help businesses to make more informed decisions and to identify opportunities and threats more quickly.
For example, a retail business might use an OLAP system to analyze sales data by product, by region, by time period, and by other factors. This could help the business to identify which products are selling well in which regions, and at what times of year, enabling them to optimize their stock levels and marketing efforts accordingly.
OLAP in Data Warehousing
OLAP is also commonly used in conjunction with data warehousing. In a data warehouse environment, data is extracted from a variety of sources, transformed into a consistent format, and loaded into a database. The data can then be analyzed using an OLAP tool.
The combination of a data warehouse and an OLAP tool provides a powerful platform for data analysis. The data warehouse provides a large, consistent data set, while the OLAP tool provides the ability to analyze the data from multiple perspectives and at various levels of detail.
OLAP and Cloud Computing
With the advent of cloud computing, OLAP has found a new platform to showcase its capabilities. Cloud-based OLAP solutions offer all the benefits of traditional OLAP systems, but with the added advantages of scalability, flexibility, and cost-effectiveness that come with cloud computing.
Cloud-based OLAP solutions can scale to handle large volumes of data, and they can be accessed from anywhere, at any time. They also offer a pay-as-you-go pricing model, which can be more cost-effective than traditional on-premises solutions, particularly for small and medium-sized businesses.
Benefits of Cloud-Based OLAP
One of the main benefits of cloud-based OLAP is scalability. Traditional on-premises OLAP solutions can be expensive and time-consuming to scale, as they require additional hardware and software. In contrast, cloud-based OLAP solutions can be scaled up or down quickly and easily, to meet changing business needs.
Another benefit of cloud-based OLAP is flexibility. Users can access the system from anywhere, at any time, using any device with an internet connection. This makes it easier for users to analyze data and make decisions, regardless of their location.
Cost-effectiveness is another key benefit of cloud-based OLAP. With a pay-as-you-go pricing model, businesses only pay for the resources they use. This can be more cost-effective than traditional on-premises solutions, particularly for small and medium-sized businesses.
Challenges of Cloud-Based OLAP
Despite the many benefits, there are also challenges associated with cloud-based OLAP. One of the main challenges is data security. As with any cloud-based solution, there is a risk that sensitive data could be accessed by unauthorized individuals. Therefore, businesses need to ensure that they have robust security measures in place to protect their data.
Another challenge is data integration. Businesses often have data stored in multiple locations, in different formats. Integrating this data into a cloud-based OLAP system can be complex and time-consuming.
Finally, there is the challenge of data ownership. When data is stored in the cloud, there can be uncertainty about who owns the data and what can be done with it. Businesses need to ensure that they understand and are comfortable with the terms and conditions of their cloud service provider.
Conclusion
Online Analytical Processing (OLAP) is a powerful tool for data analysis, providing users with the ability to analyze data from multiple perspectives and at various levels of detail. The advent of cloud computing has opened up new possibilities for OLAP, offering scalability, flexibility, and cost-effectiveness.
However, as with any technology, there are challenges to be overcome. Businesses need to carefully consider issues such as data security, data integration, and data ownership when implementing a cloud-based OLAP solution. With careful planning and implementation, however, OLAP can provide valuable insights that help businesses to make informed decisions and to succeed in today's data-driven world.