What is OLAP Analysis?

We live in a data-driven world where it’s impossible to come out on top if you aren’t using data for insights and decision making. You can easily see the data at work in the powerhouse systems driving retail and new service offerings like ride-sharing applications.

In the field of data science, data processing systems are of two types, i.e., OLAP and OLTP. The major difference between these systems is that one offers insights through data and the other is completely operational.

So, this blog will cover the part that uses data to provide insights - OLAP (Online Analytical Processing). But before moving ahead and getting a grip on OLAP, we first have to understand what is the difference between OLTP and OLAP; and why do we need OLAP in the first place.

OLTP vs OLAP

Online Transactional Processing (OLTP) allows the execution of a large volume of database transactions in real-time. Put simply, an OLTP system captures and handles transactional data in a database, performed by a large number of people, majorly over the internet. Each transaction in OLTP involves an individual database record made up of multiple rows and columns.OLTP systems are behind most of our day-to-day transactions like in-store purchases and ATM transactions. The main focus of OLTP is on quick data processing as the databases of OLTP are written, read, and updated frequently.

On the other hand, Online Analytical Processing (OLAP) is a system that is used by organizations for multi-dimensional analysis of large data volumes. Usually, the data that is used for analysis is from data warehouses, data marts, or data stores. The OLAP system is a perfect fit for data mining, complex analytical calculations, and business intelligence. It is also used for business reporting functions such as budgeting, sales forecasting, and financial analytics. The emphasis of OLAP systems is on the response time to perform complex queries, where each query includes one or more columns of aggregated data from multiple rows. For example, OLAP systems can be used for the analysis of marketing lead generation trends and year-over-year financial performance.

Several organizations use OLTP systems to offer data for OLAP. In short, the blend of OLTP and OLAP can be highly beneficial for businesses.

OLAP Analysis Definition

Online Analytical Processing (OLAP) is the technology behind many business intelligence applications. OLAP is a powerful tool for data discovery that provides capabilities for complex analytical calculations, limitless report viewing, and predictive “what if” scenario planning. OLAP is an approach to resolve multidimensional analytical problems in computing.

Most businesses have multiple categories to break down the data for tracking, presentation or analysis. However, data in the warehouse is stored in tables where each of the tables can organize data into two dimensions at a time. OLAP tools allow users to interactively analyze multidimensional data from numerous perspectives to enable fast processing and insightful analysis.

Three basic analytical operations of OLAP are roll-ups, drill-downs, and slicing and dicing, where roll-ups involve data aggregation, drill-downs allow detailed navigation of information, and slicing and dicing enable users to take out specific data sets (slicing) from the OLAP Cube and view these slices from different viewpoints (dicing).

What is an OLAP Cube?

The OLAP cube is the core of OLAP systems. An OLAP cube is a multi-dimensional database that efficiently process and analyze multiple data dimensions more swiftly than a traditional relational database.

A relational or SQL database is structured like a spreadsheet and stores records in a row by column format (two-dimensional). Although the reporting tools for relational databases can query, report and analyze multidimensional data, the performance slows down with the increase in the volume of data.

That is where OLAP Cube comes into view. The OLAP cube extends tables with additional layers that add dimensions to the data. According to theory, a cube can consist of multiple layers. An OLAP cube that represents more than three dimensions is also called a hypercube. In a practical sense, a data analyst will only make the OLAP cube with the layers they need to get optimal performance and analysis.

It’s an OLAP cube that enables the basic multidimensional analysis of data through:

• Roll-ups - aggregates data by moving up in the hierarchy or reducing the dimensions.
• Drill-downs - opposite of roll-ups, converts less detailed data into more detail either by adding new dimensions or moving down in the concept hierarchy.
• Slice and Dice - “slicing” creates a sub-cube by opting for a single dimension from the OLAP cube. Whereas, “dicing” isolates sub cubes by selecting several OLAP cube dimensions to view data from a different point of view.
• Pivot - allows rotation of OLAP cube to display a new data representation and enable dynamic multiple dimensional data views.

Types of OLAP Systems

OLAP systems are categorized into three types, i.e., ROLAP, MOLAP, and HOLAP. So let’s understand what these three systems are and what they can do.

• ROLAP: Relational Online Analytical Processing or ROLAP works directly with relational databases without organizing the data into a cube. The dimension tables and the base data are stored in the relational database as relational tables and new tables are created to hold aggregate data. ROLAP can handle large volumes of data and enable businesses to leverage their existing RDBMS software investment.
• MOLAP: MOLAP or Multidimensional Online Analytical Processing works directly into a multidimensional environment instead of providing the multidimensional view of relational databases as ROLAP does. For most use cases, MOLAP is the most practical and fastest type of OLAP.
• HOLAP: Hybrid Online Analytical Processing or HOLAP attempts to combine the features of both ROLAP (higher scalability) and MOLAP (faster computation) to provide fast access to data at every level of aggregation. Simply put, HOLAP tools can use both pre-calculated cubes of MOLAP and relational data sources from ROLAP.

When to Use OLAP?

OLAP tools like Apache Pinot and Apache Druid can be used in the following scenarios:

• To quickly execute complex analytical and ad hoc queries, without affecting your OLTP system negatively,
• To offer aggregations that allow users to get consistent results fastly, and
• To help business users in generating reports from large data sources.

OLAP is being used for a wide range of organizations for:

• Sales and Marketing: For market research analysis, promotion analysis, sales analysis and forecasting, customer analysis, and market and customer segmentation.
• Finance and Accounting: For financial modeling, financial performance analysis, budgeting, and activity-based costing.
• Production: For production planning and defect analysis.

Adopt OLAP Systems for Your Business

With OLAP companies can maximize the potential of their data as OLAP converts it into the most practical format for multidimensional analysis. Needless to say, it makes it easier for organizations to achieve intelligent insights into their business.

OLAP technology adoption depends on the underlying data sources, software types, and the objective a business wants to achieve. Each business area is specific and should have a bit of customization to create multidimensional “cube” models for data mining and analysis.

Source: OLAP Analysis