Database & Data Sync Blog

Evolution of search: traditional vs vector search

Evolution of search: traditional vs vector search

Traditional search vs. vector search – what are the key differences? Why is vector search crucial in today’s data-driven world? This article delves into the evolution of search technology, comparing traditional keyword methods with vector search, and highlights the benefits of AI-powered semantic understanding for improved search accuracy and relevance.

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Edge AI: The era of on-device AI

Edge AI: The era of on-device AI

The Era of Edge AI: Unleashing Localized Intelligence

Edge AI represents a transformative shift in artificial intelligence, moving data processing from centralized cloud servers directly to the local devices we use every day. This evolution allows for real-time AI applications in environments where connectivity is limited or non-existent—from rural health monitors to autonomous vehicles. By processing data on the device itself, Edge AI offers enhanced privacy, reduced latency, and improved reliability, making smart technology truly ubiquitous.

To power these advanced applications, a critical component is necessary: the vector database. Unlike traditional databases that manage data in structured formats, vector databases handle and interpret vector embeddings—complex data formats crucial for AI tasks. These databases enable the rapid processing and analysis of large datasets right where the data is generated, supporting everything from instant language translation on smartphones to critical decision-making in driver assistance systems.

As we delve deeper into the capabilities and requirements of on-device AI, it becomes clear that vector databases are not just supporting infrastructure—they are foundational to the advancement and expansion of Edge AI. Their ability to manage and search through massive sets of high-dimensional data efficiently allows Edge AI systems to perform sophisticated tasks such as pattern recognition, semantic search, and dynamic learning, all in real time.

With the rapid development of Edge AI technology, we are on the brink of a new era where artificial intelligence is not just a distant server’s capability but a localized, accessible, and integral part of our daily devices. This shift promises to make AI more personal, immediate, and effective, thereby shaping the future of how technology interacts seamlessly with our everyday lives.

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In-Memory Database Use Cases

In-Memory Database Use Cases

Discover the versatility of ObjectBox with its new in-memory database feature. Ideal for caching, temporary data, and enhancing app speed, this update caters to various use cases, including diskless devices and testing environments. Seamlessly switch between disk-based and in-memory storage options, with future updates promising enhanced performance and persistence functionalities. Available now for Java, Android, C, C++, with support for Dart, Go, Python, and Swift coming soon.

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Vector types (aka arrays) added with ObjectBox Java 3.6 release

Multi-dimensional vectors / vector embeddings are a central building block for AI applications. And accordingly, the ability to store vectors to add long-term memory to your AI applications (e.g. via vector databases) is gaining importance. Sounds fancy, but for the basic use cases, this simply boils down to “arrays of floats” for developers. And this is exactly what ObjectBox database now supports natively. If you want to use vectors on the edge, e.g. in a mobile app or on an embedded device, when offline, independent from an Internet connection, removing the unknown latency, try it and let us know what you think.

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Vector databases – a look at the AI database market with a comprehensive comparison matrix

Vector databases – what are vector databases? Why do you need them for AI applications? This article provides an overview on the topic of vector databases, its use for AI and takes a look at the market including a comprehensive comparison matrix on popular vector database choices like Weaviate, Marqo AI, Chroma, Pinecone and many more

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What is an Edge Database, and why do you need one?

What is an Edge Database, and why do you need one?

Edge Databases are a new type of database rising to the needs of the decentralized Edge Computing topology. They deliver specific feature sets making it easy for application developers to implement edge solutions quickly and successfully without being detered or even stopped by fundamental functionalities like fast local data persistence and decentralized data flows (Data Sync).

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Why do we need Edge Computing for a sustainable future?

Centralized data centers consume a lot of energy, produce a lot of carbon emissions and cause significant electronic waste. While data centers are seeing a positive trend towards using green energy, an even more sustainable approach (alongside so-called “green data centers” [1]) is to cut unnecessary cloud traffic, central computation and storage as much as possible by shifting computation to the edge. Ideally, Edge Computing strategies use efficient technologies like ObjectBox to harness the power of already deployed available devices (like e.g. smartphones, machines, desktops, gateways), making the solution even more sustainable.

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Green Coding: Developing Sustainable Software for a Greener Future

Every line of code – scaled up to hundreds, thousands, or even millions of devices (desktops, smartphones, tablets…) worldwide – has the potential to significantly reduce energy consumption and CO₂ emissions.

Digitization helps to save CO₂ – many experts agree on that. But things are not that simple, because the creation of software and its use contribute to greenhouse gas emissions too.
All code creates a carbon footprint. Software development and use affect the environment from the energy consumed while running to the associated electronic device waste. Choosing a sustainable architecture matters, but developers also can make a difference with green coding, creating envrionmentally sustainable software. And those that build developer tools can help even more so.

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