OLAP cubes, outdated BI technology?

As businesses demand that more employees have access to the benefits of expansive, real-time data analysis, it seems that the latency and complexity associated with OLAP cubes will soon see them rendered to the pages of technological history by a new breed of operational Business Intelligence (BI) tools. – Tools that cater for pervasive and virtually instantaneous data analysis and reporting through in-memory analytics. But they’ll survive; for now.

What do OLAP cubes offer?

The ability of OLAP cubes to facilitate multifaceted data analysis in response to complex business queries, will see them maintain some degree of usefulness, as businesses accumulate increasingly large data volumes of increasing complexity.

Because OLAP cubes can be made up of more than three dimensions (hypercube), in-depth analysis is enabled, allowing users to gain comprehensive and valuable business insights.

Virtually unlimited numbers of dimensions can be added to the data structure (OLAP cube), allowing for detailed data analysis. Analysts can view data sets from different angels or pivots; a process if involving large data volumes, would take hours on a relational database.

According to Surajit Chaudhuri and Umeshwar Dayal’s report, “An overview of data warehousing and OLAP technology”, OLAP cubes can respond to complex queries in 0.1% of the time of an OLTP relational database.

OLAP cubes can also perform data analysis without internet connectivity. But, this point here, once a considerable advantage, highlights a changing of the guard. In a world of constant connectivity, this capability is of almost no value.

How are OLAP cubes becoming outdated?

OLAP cubes are also becoming outdated in other ways. Businesses across all sectors are demanding more from their reporting and analytics infrastructure within shorter business timeframes. OLAP cubes can’t deliver real-time analysis and reporting – something high performing businesses now expect. Nor can they deliver widespread multi-user access to data analytics with the effectiveness and efficiency of in-memory analytics.

Why are in-memory databases better?

Modern companies are striving to spread fact-based decision-making throughout their organizations. In-memory analytics enables faster analysis, rapid insights and minimal IT involvement.

In-memory analytics eliminates the need to store pre-calculated data in the form of OLAP cubes or aggregate tables. It offers business-users faster analysis, and access to analysis of large data sets, with minimal data management requirements.

In-memory databases use main memory for data storage, and can perform analysis faster than standard database systems that use disk storage, as they don’t have to perform disk I/O to update or query data.

OLAP cubes have to be updated in batches. For large organizations with large data volumes of many dimensionalities, this results in unacceptable data latency, preventing business users from accessing current data and up-to-the-minute data analysis.

In-memory analytics

In-memory analytics delivers business insights to enable steadfast decision-making with the agility that businesses demand. Business users have access to self-service analysis and IT departments can spend less time on query analysis, cube building, aggregate table design, and other time-consuming performance-tuning tasks.

In-memory analytics remove the need for ETL and data warehouses, leading to substantially faster query performance as compared to database-resident storage.

Features of in-memory analysis:

• High performance: Faster reporting with organization-wide access
• Sharing: All users can access the same in-memory data through a server side deployment
• Seamless navigation: Drill down, through and across your data quickly and easily

Benefits of in-memory analysis:

• Easy in-memory analysis development: Easy to make a database from an existing metadata layer
• Minimal training: Database development leverages metadata layer builder
• Reduced workload: Don’t have to keep delving into your transactional databases or data warehouse
• User transparency: Users can navigate their entire analytical workflow faster using a single integrated interface
• Real-time reporting: The in-memory database can be loaded incrementally in near real-time intervals

The times, they are a changin’: Yellowfin is helping the transition

Companies are demanding operational BI tools that support data visualization and personalized dashboards, and empower non-technical business people across their organizations with the insights of real-time business analytics to support better decision-making, and streamline operational effectiveness and efficiencies. And that’s exactly what Yellowfin’s in-memory analytics capabilities provide.

But, unlike other traditional and in-memory BI vendors, Yellowfin takes the best of both worlds approach. Yellowfin provides choice regarding your BI deployment method. With Yellowfin, you’re not restricted to using a single mechanism or data source. In-memory, relational or OLAP cube – the choice is yours. Yellowfin is making the transition between OLAP cubes and in-memory analytics easy.

Frequently Asked Questions About OLAP Cubes and Modern Business Intelligence

What is an OLAP cube in Business Intelligence?

An OLAP cube is a multidimensional data structure used in Business Intelligence (BI) systems to analyze data from multiple perspectives. It organizes information across dimensions such as time, location, and product categories, allowing analysts to perform complex queries and gain deeper insights from large datasets.

Why were OLAP cubes widely used in traditional BI systems?

OLAP cubes became popular because they allow businesses to pre-calculate and store aggregated data, making complex analytical queries much faster than querying raw transactional databases. This approach helped organizations generate reports and perform multidimensional analysis more efficiently.

What are the main limitations of OLAP cubes today?

One of the main drawbacks of OLAP cubes is that they require batch updates and pre-built data structures. This can introduce delays in reporting and make it difficult to access real-time insights, which modern businesses increasingly expect from their analytics platforms.

How do OLAP cubes enable multidimensional data analysis?

OLAP cubes allow users to explore data across several dimensions simultaneously. Analysts can perform actions such as drilling down into detailed data, pivoting views, or slicing datasets, helping them uncover patterns and relationships that may not be visible in traditional reports.

What is in-memory analytics in Business Intelligence?

In-memory analytics is a BI technology that stores data directly in a system’s main memory (RAM) instead of on disk-based storage. This allows BI tools to process large datasets much faster and deliver insights almost instantly.

Why are in-memory databases faster than disk-based databases?

In-memory databases eliminate the need for disk input/output operations when retrieving or updating data. Because the data is already stored in memory, queries can be processed much faster, resulting in quicker analytics and reporting.

How does in-memory analytics support real-time reporting?

In-memory analytics can process and update datasets quickly, allowing BI platforms to refresh dashboards and reports with near real-time information. This helps organizations monitor performance metrics and respond to changes more rapidly.

Are OLAP cubes still relevant in modern data analytics?

Although newer technologies are emerging, OLAP cubes can still be useful for structured reporting and complex multidimensional analysis, especially in organizations with established data warehousing systems.

What is the difference between OLAP cubes and in-memory analytics?

OLAP cubes rely on pre-aggregated data stored in multidimensional structures, while in-memory analytics processes data dynamically using system memory. This allows in-memory systems to provide faster query performance and more flexible analysis.

How are modern BI platforms combining OLAP and in-memory analytics?

Many modern BI platforms allow organizations to use multiple data processing methods, including OLAP cubes, relational databases, and in-memory analytics. This flexibility enables businesses to choose the best approach based on their performance needs, data size, and reporting requirements.