Vector Database: A New Kind of Database For The AI Era - BigStep Technologies
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Vector Database: A New Kind of Database For The AI Era

Vector Database


Complex data is growing at break-neck speed. These are unstructured forms of data that include documents, images, videos, and plain text on the web. Many organizations would benefit from storing and analyzing complex data, but complex data can be difficult for traditional databases built with structured data in mind. Classifying complex data with keywords and metadata alone may be insufficient to fully represent all of its various characteristics.

Fortunately, Machine Learning (ML) techniques can offer a far more helpful representation of complex data by transforming it into vector embeddings. Vector embeddings describe complex data objects as numeric values in hundreds or thousands of different dimensions.

Many technologies exist for building vectors, ranging from vector representations of words or sentences, to cross-media text, images, audio, and video. There are several existing public models that are high-performance and easy to use as-is. These models can be fine-tuned for specific applications and you can also train a new model from scratch, although that is less common.

What is a Vector Database

Vector Database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.

It offers a stunning new level of ability to search especially unstructured data, but can also handle semi-structured and even structured data. The implication of this reality is that AI can meaningfully sort and process the deluge of data – not just for tech giants, but for average businesses and even SMEs.

Well-designed AI-based applications sift through extremely large datasets extremely quickly to generate new insights and ultimately drive new revenue streams, creating real value for businesses. But none of the data growth is truly operationalized and democratized without the new kid on the block: vector databases. These represent a new category of database management and a paradigm shift for the use of exponential volumes of unstructured data that are unused in object storage.

Why use a Vector Database

The vector database compares the similarity of these objects to find the closest matches, providing accurate results while eliminating irrelevant results that traditional search technology might have returned. Let’s look at some common use cases for vector search:

1. Semantic search

2. Similarity search for images, audio, video, JSON, and other forms of unstructured data

3. Ranking and recommendation engines

4. Deduplication and record matching

5. Anomaly detection

Required Capabilities of a Vector Database

1. Vector Indexes for Search and Retrieval

2. Single-Stage Filtering

3. Data Sharding

4. Replication

5. Hybrid Storage

Dive Into the Vectors and Search

Unstructured data such as images, video, audio, and user behavior – generally does not fit the relational database model and cannot be easily sorted into row-column relationships. The horribly tedious ways of managing unstructured data often boil down to manually tagging the data (think tags and keywords on video platforms).

Quality Data – and Insights

A well-trained neural network model will output embeddings that are consistent with specific content and can be used to search for semantic similarity. The tool for storing, indexing, and searching these embeds is a vector database – purpose-built to manage embeds and their distinct structures.

Key to the market is that developers anywhere can now add a vector database to AI applications with its production-ready capabilities and lightning-fast unstructured data searches. These are powerful applications that can help a company meet its business goals.

A Vector Database Strategy Starts With Use Cases That Make Sense For Your Business

It’s increasingly common for a company’s end-to-end data strategy to include artificial intelligence, but it’s important to consider which business units and use cases will benefit the most. Artificial intelligence applications built on vector databases can analyze voluminous unstructured data for marketing, sales, research and security purposes. Recommended systems—including user-generated content recommendations, personalized e-commerce search, video and image analytics, targeted advertising, anti-virus cybersecurity, language-enhanced chatbots, drug detection, protein detection, and bank fraud detection—are among the first major use cases. well managed vector databases with speed and accuracy.

Choose among Purpose-Built and Open Source Vector Databases

Enterprises benefit from purpose-built, open-source vector databases that have matured to the point where they offer greater search performance in vector data at scale at a lower cost than other options.

Such purpose-built vector databases should be designed to easily include new indexes for emerging application scenarios and support flexible multi-node scaling to accommodate ever-increasing data volumes.

Overcoming the Challenges Ahead

Big new paradigm-shifting technologies inevitably bring several challenges – technical and organizational. Vector databases can search billions of inserts, and their indexing is technically different from relational database indexing. Not surprisingly, developing vector indices requires specialized knowledge. Vector databases are also computationally intensive, given their AI and machine learning genesis. Solving their large-scale computational problems is an area of ​​constant development.


Combined with machine learning transformer models, vector databases offer a more intuitive way to find similar objects, answer complex questions, and understand the hidden context of complex data.

So how should you get started?

With BigStep Technologies, you can do this in just a few minutes. Our AI experts make it easy for you to add AI technologies to production applications. The team at BigStep are capable of developing, managing, monitoring and enhancing your vector database and help you achieve your goal. Get in touch with our experts today by contacting us at and let us take care of the rest.

karishma Verma

Content writer with a passion to write for a variety of niches to broaden my horizon as a professional writer.

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