DATA ANALYTICS

Data Cube vs. Data Warehouse for Business Intelligence

Julian Alvarado

Sr. Content Marketing Manager, Sigma

Agile business intelligence requires the ability to quickly access and analyze all data relevant to a business question. Over the last couple of decades, technology has developed to empower teams to make data-driven decisions, seizing opportunities and avoiding risks. In the not-too-distant past, on-premises data warehouses and data cubes were the only options for storing data intended for analysis. A debate evolved around the data cube vs. the data warehouse as each has its own pros and cons. In this article, we examine this debate and explore how the cloud has transformed the way today’s organizations are doing business intelligence.

What is a Data Warehouse?

A traditional data warehouse is a database that an organization uses to store information separately from an operational database. The warehouse serves as a single location for data from disparate sources, where BI teams can find cleaned and organized data. The data warehouse first appeared in the 1980s when relational databases using SQL replaced outmoded methods of storage, making databases more valuable. While some organizations continue to use on-premises data warehouses, today’s data-driven companies have moved to cloud data warehouses and platforms (which we’ll dive into later).

Advantages and Disadvantages of Data Warehouses

For a decade, the traditional data warehouse was the only option available for organizations conducting business intelligence. Let’s look at the advantages organizations experience when using a data warehouse and the challenges they face.

Data warehouse pros

  • Consolidated data — The reason data warehouses came into existence was to allow business intelligence teams to access any data set quickly. Rather than going between disparate data sources, data professionals can find the data they need, by topic, in a single location.
  • Input error alerts — A data warehouse requires that data be loaded correctly, so it’s easy to see if there are errors in the data before it goes into the warehouse.

Data warehouse cons

  • Expense — One of the biggest problems with on-premises data warehouses is that they are resource-intensive to build. In addition to the hardware expenses, organizations must also retain the staff necessary to maintain it and to manage the data going into it.
  • Integration complications — On-premises data warehouses can be difficult to integrate with other systems and applications. Technical experts must be kept on staff to ensure all systems and applications are working together as they should.
  • Time-consuming data processing — Due to the fact that on-premises data warehouses are limited in compute power, it’s time-consuming to process data prior to analysis. This drawback is why data cubes were developed.

What is a Data Cube?

A data cube (also called a business intelligence cube or OLAP cube) is a data structure optimized for fast and efficient analysis. It enables consolidating or aggregating relevant data into the cube and then drilling down, slicing and dicing, or pivoting data to view it from different angles. Essentially, a cube is a section of data built from tables in a database that contains calculations. OLAP cubes are typically grouped according to business function, containing data relevant to each function.

Advantages and Disadvantages of Data Cubes

The data cube was an excellent solution to avoid slow processing times for complex data analysis at a time when compute power was constrained. Data cubes have their own advantages and disadvantages.

Data cube pros

  • Faster queries — Because relevant data is aggregated in a cube, it speeds data queries.
  • Simpler UI — Data cubes are considered by many data professionals to have a better user interface than traditional data warehouses, allowing users to more quickly work with data.

Data cube cons

  • New reports require new cubes or modification — A data cube contains only the dimensions needed when the cube was created for a specific analysis. New analyses that include new or different dimensions require that new cubes be created or that the existing cube be modified.
  • Time-consuming modeling — Because a data cube must be created or modified each time a new report is needed, data must be organized in the data warehouse in a way that allows it to be quickly accessed and put into a cube. For this reason, time-consuming data modeling techniques are necessary.
  • Summarized Information — In a data cube, you can’t get to transaction-level detail. Cubes always contain summarized information, so to get to transaction detail, you must leave the cube and go directly against the database. This process is cumbersome, and largely defeats the purpose of using a cube.

Why Data Cube vs. Data Warehouse is an Outdated Paradigm

Traditional data warehouses had limitations, and data cubes helped to address some of them. At the time, data cubes were revolutionary, allowing BI teams to more quickly process data with constrained compute power. But with the advent of the cloud data warehouse and cloud pipeline tools, the data cube vs. data warehouse debate became largely obsolete — the capabilities of cloud platforms and tools solve the challenges involved in using both on-premises warehouses and data cubes.

Near-unlimited storage and compute power

The cloud makes compute power and RAM incredibly affordable. Even small and mid-sized businesses can easily scale resources up or down as needed. With a modern cloud data warehouse like Google Big Query, Amazon Redshift, or the Snowflake Data Cloud, organizations can cost-effectively store massive amounts of raw data.

Columnar database structure

With the development of columnar database structure in the cloud data warehouse, BI teams could begin performing OLAP-type workloads at high performance without needing to extract data and build cubes. Practically, this capability allows organizations to perform tasks directly in the data warehouse, without having to create or maintain cubes.

SaaS pipeline tools

Modern SaaS data pipeline solutions such as Fivetran and Matillion offer out-of-the-box connectivity to popular data sources and can also normalize and transform disparate sources of data and move it around without requiring users to write code. These tools have greatly accelerated data processing.

Cloud-native analysis tools

Today’s data analysis tools (like Sigma) connect directly to the cloud data warehouse, taking advantage of the CDW’s power and scalability to access massive amounts of fresh data whenever needed. The best solutions allow decision-makers to directly query live data, down to row-level detail, while maintaining strict data governance. Additionally, users of these tools can traverse data via multiple hierarchies based on the user’s choice, rather than on a rigid pre-modeled exploration experience as cubes provide.

Move Beyond the OLAP Cube vs. Data Warehouse Debate

Using modern cloud-based platforms and applications will accelerate your business intelligence. Today, you can take advantage of the power and scalability of the cloud to quickly and easily store, process, and query relevant data in a secure, governed manner.

Ready to visualize your data for actionable insights?