The data management landscape is undergoing a serious transformation. While traditional relational databases have been the “go-to” data storage tool for some time, their limitations in handling complex, interconnected data have become clear. As the volume and complexity of data continues to increase, organizations are looking for more efficient and agile solutions to extract meaningful insights.
This is where graph databases and NoSQL come into play. Unlike relational databases, which work particularly well with structured data, graph databases are designed to model and store data as interconnected nodes and relationships. Graph databases focus on the relationships within the data and, more importantly, can reveal relationships you might not have known existed. NoSQL also works to think outside the traditional “data box” and enables the storage and querying of data outside the traditional structure found in relational databases. Compared to traditional solutions, these fundamentally different approaches offer significant advantages when dealing with complex queries that span multiple data domains.
Chief Data & Analytics Officer at Alteryx.
Solving complex queries
While relational databases work effectively with simple tables of rows and columns, performance can struggle with more complicated questions that involve combining many different data pieces across several tables. For example, if an organization wants to know the answers to seemingly straightforward questions such as, “Who are all our customers in London? How much did they spend in October, and how did their spending change when the weather was warm?”, multiple tables would need to be joined, leading to lengthy query times and exponential costs.
In contrast, graph databases are designed to handle complex, interconnected questions more efficiently. They can traverse the data and relationships between customers, purchase history, location and weather data, delivering faster and more cost-effective results.
The differences between traditional relational databases become even more evident as the number of tables involved in a query increases. Each additional table deepens the complexity of the relational database query, impacting performance. Graph databases, on the other hand, have a more linear relationship between query complexity and performance, increasing and improving speed.
While newer cloud relational database providers have attempted to mitigate query speed issues by scaling resources to handle tasks, this can lead to high costs. Moving to graph database methods for these complex, multi-table queries is a more cost-effective option in many cases, saving time and money over simply scaling the resources.
Organizations looking to reduce costs will need to weigh the price of having new technology in their portfolio versus significantly reduced query times that reduce operational costs and change the user experience. In many cases, penny-wise, organizations will look to reduce costs by not adding the technology and instead will forgo the order of magnitude operational cost savings in reduced compute.
Supporting AI applications
The increasing use of artificial intelligence (AI) among organizations is placing more pressure on data management systems. Large language models (LLMs) and generative AI (genAI) require vast amounts of interconnected information and data to function effectively. Knowledge graphs – which organize data sources into domains and forge relationships between the different entities – are becoming essential for training and powering these AI models.
The use of knowledge graphs stored as graph databases provides the ideal platform for building and maintaining these knowledge graphs as data complexity continues to grow.
The significant performance improvements to AI applications by graph and NoSQL databases in queries involving multiple levels of relationships make them the better option compared to cloud-based relational databases. This is especially true for organizations with complex data structures and large datasets. For example, e-commerce giants such as Amazon and Walmart can use this process to analyze the interactions of customers with technology and social media platforms such as LinkedIn and Meta to quickly extract behavioral insights from this interconnected data. This information is critical for understanding customer experiences and what action to take to improve them. These companies have all realized the potential of graph databases and currently leverage them for highly performant queries.
Unlocking data potential
As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital. By embracing graph databases, organizations can unlock the full potential of their data, reduce costs and time, become more efficient and gain a competitive advantage.
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