Database management is an integral part of our digital world, as it impacts everything – from modern business operations to cutting-edge scientific research. Even our everyday online searches are aided by robust databases. And to put things into perspective, a ResearchAndMarkets report on global cloud databases forecasts their market size will reach $39.67 billion by 2029. It’s essential then to study the nuances of different database systems, especially when it’s time to explore advanced formats like vector databases.
A vector database refers to a high-performing data structure consisting of mathematical vectors. Each piece of data is represented by a vector that is then projected into a multi-dimensional space. When a search is made, a vector database compares the query vector with the vectors in the database and locates the nearest ones based on their geometric distances.
The search results then display contextual relationships rather than just linear matches. For instance, a vector search for “technology trends” would yield both exact keyword match results plus closely related topics like “AI developments” or “VR tech innovations”.
Vector databases hold a promise of greater accuracy and higher performance. VentureBeat’s opinion piece even points out how vector databases may improve generative AI applications. But businesses should also be aware of the implications that come along with their use. Let’s delve into some of them.
Challenges in Vector Databases
As is the case with any new technology, dealing with vector databases involves a few challenges.
- Complexity: Compared to regular databases, vector databases use a considerably more complex system – a multi-dimensional geometric space. The vectors in this space can be challenging to handle, and it can take time and effort to become proficient in the implementation and management of these databases.
- High Computation Load: The search algorithms employed by vector databases can be highly CPU-intensive. Locating the nearest neighbors in a multi-dimensional spatial database requires a lot of mathematical computation, which might tax the system infrastructure heavily, especially under high-volume loads.
- Compatibility and Integration: Traditional relational databases have been around for quite some time and enjoy widespread support from various software systems. Vector databases are still a green field and may not require custom tailoring.
Considerations For Vector Databases
Entering the realm of vector databases requires careful planning. Here are a few aspects to think about:
Lower Latency and Complexity
Adding a vector database to your existing tech stack brings a layer of complexity and latency to your system. You’d want a provider that can keep things simple and ensure that the addition does not negatively impact your system’s overall performance. The vector databases at MongoDB provide solutions to this by allowing vector embeddings to be stored in the same system as the rest of the data. This reduces the complexity of adding a separate vector database.
Track Record
Companies should consider the track record of the provider and determine their reputation in the tech industry. Newly emerging vector database providers might offer good deals and promises, but their products might not have undergone enough testing in the real world. According to Retool’s State of AI report, Pinecone and MongoDB have the highest Net Promoter Score (NPS) when it comes to vector search.
Efficient Data Removal and Updates
Vector values will change over time. As new data comes in or existing data updates, you need to be able to efficiently remove obsolete vector representations from your database. This is especially crucial in industries such as e-commerce, where inventory and customer preferences constantly change.
Vector search expert Jonathan Ellis mentions that during such updates the ideal vector database should be able to provide a healthy balance between reads and writes by increasing the storage units. This would ensure a smooth search experience without impacting the system’s speed.
Final Thoughts
Vector databases, although relatively new entrants to the data management arena, are indeed showing promising potential in various applications. As they continue to evolve and improve, businesses need to stay informed about the challenges and considerations associated with vector databases in order to make the right choices.
If you like this article, visit our Tech category to find more news and updates on generative AI and databases.
Database management is an integral part of our digital world, as it impacts everything – from modern business operations to cutting-edge scientific research. Even our everyday online searches are aided by robust databases. And to put things into perspective, a ResearchAndMarkets report on global cloud databases forecasts their market size will reach $39.67 billion by 2029. It’s essential then to study the nuances of different database systems, especially when it’s time to explore advanced formats like vector databases.
A vector database refers to a high-performing data structure consisting of mathematical vectors. Each piece of data is represented by a vector that is then projected into a multi-dimensional space. When a search is made, a vector database compares the query vector with the vectors in the database and locates the nearest ones based on their geometric distances.
The search results then display contextual relationships rather than just linear matches. For instance, a vector search for “technology trends” would yield both exact keyword match results plus closely related topics like “AI developments” or “VR tech innovations”.
Vector databases hold a promise of greater accuracy and higher performance. VentureBeat’s opinion piece even points out how vector databases may improve generative AI applications. But businesses should also be aware of the implications that come along with their use. Let’s delve into some of them.
Challenges in Vector Databases
As is the case with any new technology, dealing with vector databases involves a few challenges.
Complexity: Compared to regular databases, vector databases use a considerably more complex system – a multi-dimensional geometric space. The vectors in this space can be challenging to handle, and it can take time and effort to become proficient in the implementation and management of these databases.
High Computation Load: The search algorithms employed by vector databases can be highly CPU-intensive. Locating the nearest neighbors in a multi-dimensional spatial database requires a lot of mathematical computation, which might tax the system infrastructure heavily, especially under high-volume loads.
Compatibility and Integration: Traditional relational databases have been around for quite some time and enjoy widespread support from various software systems. Vector databases are still a green field and may not require custom tailoring.
Considerations For Vector Databases
Entering the realm of vector databases requires careful planning. Here are a few aspects to think about:
Lower Latency and Complexity
Adding a vector database to your existing tech stack brings a layer of complexity and latency to your system. You’d want a provider that can keep things simple and ensure that the addition does not negatively impact your system’s overall performance. The vector databases at MongoDB provide solutions to this by allowing vector embeddings to be stored in the same system as the rest of the data. This reduces the complexity of adding a separate vector database.
Track Record
Companies should consider the track record of the provider and determine their reputation in the tech industry. Newly emerging vector database providers might offer good deals and promises, but their products might not have undergone enough testing in the real world. According to Retool’s State of AI report, Pinecone and MongoDB have the highest Net Promoter Score (NPS) when it comes to vector search.
Efficient Data Removal and Updates
Vector values will change over time. As new data comes in or existing data updates, you need to be able to efficiently remove obsolete vector representations from your database. This is especially crucial in industries such as e-commerce, where inventory and customer preferences constantly change.
Vector search expert Jonathan Ellis mentions that during such updates the ideal vector database should be able to provide a healthy balance between reads and writes by increasing the storage units. This would ensure a smooth search experience without impacting the system’s speed.
Final Thoughts
Vector databases, although relatively new entrants to the data management arena, are indeed showing promising potential in various applications. As they continue to evolve and improve, businesses need to stay informed about the challenges and considerations associated with vector databases in order to make the right choices.
If you like this article, visit our Tech category to find more news and updates on generative AI and databases.
