Understanding OLAP: Online Analytical Processing in Data Engineering

Businesses store information in databases. They use this data for analysis and business intelligence. 

A relational database is sufficient to record transactional data and basic information about business users. This type of database is called an Online Transaction Processing (OLTP) database.

While relational databases have value, they cannot analyze large volumes of data from different perspectives - which is crucial for data analytics. This is where OLAP or Online Analytical Processing comes in.

An OLAP database is multidimensional and enables data teams to run complex analytical queries efficiently.

In this article, we will explain what OLAP is, how it works, and the types of OLAP systems. We will also delve into how OLAP systems benefit data engineers and analysts, their analytical capabilities, factors that can boost their performance, and how organizations can install effective OLAP solutions.

What is OLAP?

Online Analytical Processing (OLAP) is a technology used to represent and analyze large data volumes using dimensions and hierarchies. 

OLAP allows users to explore relationships between different data elements and interactively analyze data with speed and flexibility.

OLAP software serves many analytical and business intelligence use cases, including financial reporting, trend forecasting, inventory management, etc.

Organizations integrate data from multiple data sources into a central storage system like a cloud data warehouse. A data warehouse is a relational database. It stores data in a collection of tables.

OLAP systems add layers to the single tables in relational databases to create a multidimensional database known as the OLAP cube.

The OLAP Cube

An OLAP cube is a database that allows users to represent data in multiple dimensions, such as time, geography, product, and customer.

Each cube is deployed on the OLAP server, and data engineers can use the dedicated client interface to visualize data and perform analytical operations.

OLAP cubes enable data teams to run complicated queries across several data dimensions and generate reports that provide in-depth business intelligence insights. This would not be possible using a transactional database.

An OLAP cube representing more than three dimensions is known as a hypercube. Theoretically, an OLAP data cube can have infinite layers.

OLAP Concepts and Terminology

Before we go any further, let’s look at the key concepts used in usual online analytical processing:

• Dimensions: Dimensions are categories of information stored in an OLAP cube. They are used to segment and filter data. Common examples of dimensions are Time, Stores, Products, and Location.
• Measures: Measures refer to the data within the dimensions of an OLAP cube. They are used to define and analyze business performance. Margin, unit sales, budget, and cost of goods, are examples of a measure.
• Hierarchies: An OLAP hierarchy is a logical structure that organizes data into a hierarchical order for data analysis. For example, a time dimension might have hierarchies for year, quarter, month, and day. Hierarchies can be nested, meaning that they have levels of increasing detail. 
• Members: Members are the individual items within a dimension used to analyze and aggregate data in an OLAP cube. For example, in a product dimension, the members can be individual products, such as shoes, shirts, and pants.

Benefits of OLAP

Online analytical processing provides five key benefits for data engineers and data analysts:

• Improved Decision-Making: OLAP enables multidimensional analysis, such as time, geography, and product lines. This provides deeper insights into the relationships between different factors and can lead to better-informed decisions.

• Enhanced Data Visualization: Online analytical processing applications enable advanced data visualization. This gives data teams a better understanding of current and historical data.

• Flexibility: An OLAP database uses a semantic layer to accommodate changes to the data model or analysis requirements without overhauling the underlying data structure.

• Save time: OLAP software can help data engineers and business analysts save time and effort by automating processes within data analysis and reporting.

• Competitive advantage: OLAP can help organizations gain a competitive advantage in their industry by providing faster, more flexible analysis and reporting capabilities.

Types of OLAP Systems

There are three types of online analytical processing (OLAP) systems:

• ROLAP (Relational OLAP): In this system, relational databases, like a data warehouse, store data, and SQL queries are used to retrieve data for analysis. It facilitates multidimensional data analysis and offers high data efficiency.

ROLAP systems are highly scalable and can handle large amounts of data. However, they may have slower query response times and may not support complex calculations.

• MOLAP (Multidimensional OLAP): This type of OLAP stores data in a multidimensional cube format, where each dimension represents a different attribute of the data (such as time, geography, or product).

MOLAP is the fastest way to perform multidimensional analysis. It supports complex calculations and aggregations and is easier to use.

However, MOLAP is not as scalable as ROLAP and can handle limited data. It also requires extensive data preprocessing.

• HOLAP (Hybrid OLAP): Hybrid OLAP combines the strengths of the MOLAP and ROLAP systems. It allows users to store summary data in multidimensional cubes and detailed business data in a relational database.

HOLAP systems can query data at high speeds, provide fast data access, handle high volumes of data, and improve data relevance.

Overall, the choice of OLAP system depends on the specific analytical needs of the organization, including the amount and complexity of data, the required query response times, and the types of analysis and reporting required.

OLAP Operations

OLAP systems use a specific SQL language called MDX or Multidimensional Expressions. Most databases also support standard SQL queries to perform OLAP analysis.

Some standard multidimensional OLAP operations are:

• Slice and Dice: Users can slice and dice data in an OLAP cube by selecting a subset of data based on specific criteria or filters. 

Slicing is the process of dividing one dimension within the cube into a separate table, enabling low-level and isolated analysis of a data set. Dicing is the process of dividing two or more dimensions within a cube to generate a separate cube.

• Drill down and Roll-up: In OLAP, users can drill down from high-level data to view lower-level information. They can also roll up or move from detailed data to less detailed data or summarized information.
• Pivot: OLAP systems allow users to pivot or rotate data from rows to columns or from columns to rows, enabling multidimensional analysis from different perspectives. They can also compare data across dimensions.
• Drill-through: Drill-through is an OLAP function where users can create data points to access detailed information faster. When users click on a data point in a summary, they are shown the underlying data that make up the summary
• Drill-across: Drill-across uses a common dimension shared by different data sources to enable data analysis across multiple unrelated sources or cubes. This function allows analysts to perform analysis on data from multiple sources without integrating them into a single cube.
• Aggregations and Calculations: OLAP databases support many calculations and aggregations, such as sum, average, count, minimum, maximum, and variance. Users can perform these operations across one or more dimensions.

OLAP Data Models

Three schemas are commonly used for OLAP data modeling:

Star Schema

A star schema is used in ROLAP systems. This multidimensional data model organizes data into a central fact table surrounded by dimension tables. Each dimension table is joined to the fact table through a primary key-foreign key relationship.

The fact table contains the measures or quantitative data, like sales revenue, quantity sold, or profit margin, that is being analyzed. The dimension tables have descriptive data, such as time, geography, or product information, that provide context for the measures.

The star schema is a popular OLAP data model because it is easy for business analysts and end users to understand. They can easily navigate through different levels of data.

Snowflake Schema

Another ROLAP data model used in OLAP data warehousing is the Snowflake schema. It organizes data similarly to the star schema but with normalized dimension tables.

There is still a central fact table, but dimension tables are normalized by breaking them into multiple related tables. This reduces data redundancy and improves data consistency.

This results in a schema with more tables and relationships, which can slow query performance, add complexity to queries, and be harder to understand. But it can improve query performance and reduce storage requirements by eliminating redundant data.

Fact Constellation Schema (Galaxy Schema)

The galaxy schema or fact constellation schema contains multiple fact tables, each with its own set of dimensions. The fact tables are linked together through shared dimensions.

In this schema, each fact table represents a different business process or measure, like sales or customer satisfaction. Each fact table is linked to one or more dimension tables containing descriptive data.

Shared dimensions link the fact tables, allowing for more complex queries and analyses.

This schema allows for more flexibility in querying and analyzing data, as users can analyze multiple business processes or metrics at the same time. However, this schema is harder to use than the star or snowflake schema.

OLAP Performance Optimization

You can improve the performance of your OLAP systems by:

• Pre-aggregating data: Aggregation involves pre-calculating and storing summary data, like totals or averages, in OLAP cubes. 

Aggregation combines data at different levels of granularity to provide a high-level overview. This enables OLAP systems to retrieve and display summary information faster, reducing query times and improving performance.

• Caching: Caching involves storing frequently accessed data, query results, and objects in memory so that OLAP systems can quickly retrieve them during data analysis.

Caching is particularly effective for OLAP when there is a high volume of similar or repetitive queries, like reports or dashboards.

• Indexing: Indexes are data structures created on specific columns or dimensions in the data warehouse.

When a query is executed, the OLAP system uses the index to quickly locate the rows with the required data rather than scanning the entire storage system.

• Partitioning: Data engineers can divide large tables or cubes into smaller, more manageable parts to optimize OLAP performance.

Partitioning a database into smaller units based on a partitioning key reduces the amount of data that needs to be scanned for each query.

• Parallel processing: In parallel processing, a query is divided into parts, known as tasks, and distributed across multiple processors or cores.

Each processor is assigned tasks simultaneously, allowing the query to be executed much faster than if it were processed sequentially on a single processor.

• Materialized views: Materialized views are pre-calculated views that store the results of complex queries as physical tables in the data warehouse.

When a complex query that includes the materialized view is executed, the OLAP system can retrieve the pre-calculated results from the materialized view table rather than performing the query again.

• Hardware and infrastructure considerations: High-performance hardware, like fast processors, large memory, and high-speed storage, is required to enable OLAP functionality, like data discovery, unlimited report viewing, and complex analytical calculations.

Infrastructure for OLAP systems must be highly scalable to handle large data volumes and be.

Many organizations choose cloud-based vendors for data analysis since these feature-rich platforms simplify integration while being reliable, easy to scale, and more affordable than on-premise data infrastructures.

Implementing OLAP Solutions

Installing effective OLAP systems within an organization can be challenging. We’ve explained the four crucial steps to help you get started:

#1. Selecting the right OLAP technology

An OLAP system is built using a combination of tools. Many factors influence your choice of OLAP tools, but you should focus on:

• Business goals and analytical needs
• Scalability
• Performance
• Flexibility
• Ease of use
• Integration with existing systems and data workflows

Using these factors as the foundation can help build a cohesive OLAP solution for data engineering.

Modern data teams rely on cloud-native OLAP solutions. Cloud-based OLAP services are easier to install and modify. They are also more affordable and offer additional capabilities.

For example, using cloud data warehouses enables massively parallel processing (MPP), which allows organizations to perform sophisticated analytics on vast amounts of data at rapid speeds.

Databases that use columnar storage, like a cloud data warehouse, can be adapted for OLAP. Amazon Redshift is an example of a columnar cloud database that also has analytical capabilities.

Once the solution is deployed to the production environment, it must be monitored and maintained. This involves tracking performance, ensuring data quality, and implementing updates as needed.

#2. Data preparation and ETL/ELT processes

For successful online analytical processing, there are two main considerations - data collection and data modeling.

Data collection refers to how you gather, store, and clean data. This is typically done via data integration. More specifically, data engineers use ETL (extract, transform, and load) or ELT (extract, load, and transform) data integration methods.

ELT is ideal for the modern data stack. In this method, source data is extracted and loaded into a central repository, like a data warehouse or data lake. This is better for the current data ecosystem since it allows for easier data movement, more flexibility, and faster data access.

Once data has been loaded into a cloud data warehouse, engineers and analysts use data models to prepare data for analysis. A data model visualizes the technical, business, and performance requirements for efficient analysis.

At the analytical level, data modeling is used to create denormalized or multidimensional data models that enable efficient querying and aggregation.

#3. Designing and building OLAP cubes

The OLAP cube is an integral part of the OLAP architecture. Data engineers design and build OLAP data cubes to represent multidimensional data. 

Typically, there is a cube built for each specific query. Engineers must custom-build every cube since the data stored within it cannot be updated after being modeled.

Each OLAP cube is deployed on the OLAP server, and analysts can use client interfaces to perform analytical operations.

#4. Security and access control considerations

Online analytical processing systems often contain sensitive data, so it’s important to implement appropriate security and access control measures and use a trusted, secure data movement solution to prevent unauthorized access.

Your data movement platform, OLAP solution, and business intelligence applications must maintain data integrity while protecting data via encryption, role-based access controls, integration monitoring, sync logs, and compliance with the latest standards, like GDPR, SOC 2 Type II, and ISO 27001.

Conclusion

OLAP is a crucial technology for large-scale, in-depth data analysis. OLAP solutions can significantly improve the value of your data insights by using contextual data across multiple dimensions to produce data-backed conclusions.

Cloud-based OLAP software allows data teams to integrate data and perform complex analytical operations at a rapid pace. You can use the best practices from above to implement an effective OLAP solution at your organization.

You can optimize query response times within your OLAP systems using pre-aggregation, partitioning, and cloud-based platforms.