The explosion of data available to companies and corporations over the past 5 years has left data scientists with an excess of information at their disposal. While that may sound like a good problem for data scientists to have, they face a steep obstacle in organizing, cataloging and drawing context out of all available data assets in an efficient and time-sensitive manner.
Data experts are now working to solve this problem of having too much information by utilizing parallel Graph Online Analytical Processing (GOLAP) data warehouses. This approach utilizes a variety of automated tools that speed up important data processing functions, including modeling, transformation, and query-building.
However, one nagging caveat to building a GOLAP data warehouse does exist – data science practitioners must ensure that their data exists in an Online Transaction Processing (OLTP) graph database to begin with, which can create a moment of pause for those who haven’t already invested in OLTP.
Given that most structured data in today’s world is currently in relational databases, it can be a complex and time-consuming process to port that data over to a graph database. Nevertheless, to avoid unintended losses or needless complexities, data scientists should ensure their data is contained in a graph database, as it’s simply more natural to port over data from graph database to graph database.
The GOLAP approach also relies on parallel database technology, which “subdivides what is usually considered a single database operation such as index creation, database loading, or SQL queries into multiple parts,” according to IBM. By incorporating parallel processing into a graph database – which stores linked data with a better architecture – GOLAP can be used to build and execute superfast data warehouse analytics, graph algorithms at large scales.
A GOLAP data warehouse also provides a demonstrable value to companies in virtually any industry looking to solve their most demanding business and customer challenges, such as customer relationship management, supply chains and logistics, pricing models, and risk assessments, among other use cases.
GOLAP data warehouses, such as Cambridge Semantics’ AnzoGraph, also hold the key to prepping and storing the data that companies want to use to train a new generation of machine learning models. These intelligent, powerful databases allow data scientists to break down rich models of harmonized data into their basic functions and relationships, regardless of how many disparate sources the data comes from.
Curious how you can speed up your analytics capabilities and data discovery with a graph OLAP database? Learn more about the power of AnzoGraph and try the free beta here for 60 days.
