Why the rise of vector databases is a revolution for data analysis
Machine learning and data science is taking center stage in enterprises and organizations of all sizes. Yet, making data science operational within an organization is no mean feat. Traditional databases are unable to handle the load and complexity of data science tasks. Instead, organizations need a new kind of database. Vector databases are proving to be just the solution. In this post, we look at what vector databases are, what needs they solve, and some unique characteristics of a great vector database.
Large companies like Amazon and Google are built on the maturity of their database services. They can query large volumes of data and glean meaning out of what would otherwise be chaos to a traditional relational database.
Today, the data wars are being fought by companies that have large volumes of structured data but limited access to real-time data. Despite the fact that companies are increasingly buying and deploying data analytics platforms like BigQuery, Hadoop, and Spark, there are still businesses, especially those in financial services, media and telecommunications sectors that cannot get access to and process the amount of data they need. This means these businesses are unable to stay ahead of the game and stay competitive.
Data science teams in large companies are slow and often short-staffed. The organization’s data needs are sky high, and the data science teams are always playing catch up. Even if they build something to meet the organization’s needs, it gets outdated the day it is released as the organization’s needs are always changing. The team cannot maintain and update the project to keep it aligned with the organization’s goals. Large organizations need something nimble and agile.
Startups and SMBs do not have the budget or resources to support an in-house data science team. They prefer to offload tasks to tools rather than people, as the mantra is to keep a small workforce without any bloat.
There is a clear need for modern data services that can level the playing field between the startup and the enterprise, and cater equally to both types of organizations. Enter, vector databases.
A vector database views data as a set of interconnected vectors. You can think of these vectors as a map or a representation of the data. What makes vectors so powerful is that they are multi-dimensional and can add numerous layers on top of each data point resulting in a rich dataset.
Big Data is typically high volume, very complex, and unstructured, which makes it difficult to analyze. It becomes even harder when you throw in the aspect of real-time. This is where vector databases excel as they can analyze large-scale data analysis and do it in real-time.
While traditional databases have a strict structure and logical way of storing data, storing data in vector data types can be a little less structured. In a traditional database, we have one row per table. The only difference is the column – a single row is grouped with others and contains information about only that one data point.
When a customer buys a bike, the bike might be mentioned in two columns, one for the model and the other for the color. In a traditional database, we would have to write a model that would process only one dataset and ignore the other. As a result, when the user looks up bike information for a specific color, the model would return results only if the color matches the query exactly.
