Edge AI refers to decentralized artificial intelligence systems that process data locally on in-store devices, e.g. POS terminals, smart shelves, Raspberry Pis, mobile phones, or cameras, rather than relying on distant cloud servers. This architecture works independently from distant cloud servers or internet connectivity, and therefore offline with minimized latency. Both, offline-capability and speed, are critical for applications like fraud detection and checkout automation. Accordingly, IDC emphasizes that 45% of retailers now prioritize “near-the-network” edge deployments. There, AI models run locally on in-store servers or IoT devices, balancing cost and performance.
Key Components of Edge AI Systems
For Edge AI to deliver real-time, offline-capable intelligence, its architecture must integrate on-device databases, local processing, and efficient data synchronization. These three pillars ensure seamless AI-powered retail operations without dependence on the cloud, minimizing latency, costs, and privacy concerns.
Edge AI system architecture in retail, integrating local processing, real-time data sync, and various applications like POS or signage
Retail generates vast real-time data from IoT sensors, POS transactions, smart cameras, and RFID tags. To ensure instant processing and uninterrupted availability you need:
On-device data storage: All kinds of devices from IoT sensors to cameras capture data. Depending on the device capabilities, with small on-device databases, data can be stored and used directly on the devices.
Local central server: A centralized on-premise device (e.g. a PC or Raspberry Pi, or more capable hw) ensures operations continue even if individual devices are resource-limited or offline.
Bi-directional on-premise data sync: Local syncing between devices and with a central on-site server ensures better decisions and fail-safe operations. It keeps all devices up-to-date without internet dependence.
2. Local Data Processing & Real-Time AI Decision-Making
Processing data where it is generated is critical for speed, privacy, and resilience:
On-device AI models: Small, quantized AI models (SLMs) like Microsoft’s Phi-3-mini (3.8B parameters, <2GB memory footprint) can run directly on many devices (e.g. tablets, and POS systems), enabling real-time fraud detection, checkout automation, and personalized recommendations.
Local on-premise AI models: Larger SLMs or LLMs run on the more capable in-store hardware for security, demand forecasting, or store optimization.
On-device & on-premise vector databases: AI models leverage on-device vector databases to structure and index data for real-time AI-driven insights (e.g., fraud detection, smart inventory management), fast similarity searches, and real-time decision-making.
Selective Cloud Sync: Bi-directional cloud data sync extends the on-premise data sync. Select data, such as aggregated insights (e.g., sales trends, shrinkage patterns), payment processing, and select learnings are synced with the cloud to enable Enterprise-wide analytics & compliance, Remote monitoring & additional backup, and Optimized centralized decision-making.
Cloud Database & Backend Infrastructure: A cloud-based database acts as the global repository. It integrates data from multiple locations to store aggregated insights & long-term trends for AI model refinement and enterprise reporting, facilitating cross-location comparisons.
Centralized cloud AI model: A centralized cloud AI model is optional for larger setups. It can be used to continuously learn from local insights, refining AI recommendations and operational efficiencies across all connected stores.
Use Cases of Edge AI for Retailers
Edge AI is unlocking new efficiencies for retailers by enabling real-time, offline-capable intelligence across customer engagement, marketing, in-store operations, and supply chains.
Key applications of Edge AI in retail, driving personalization, operational efficiency, and smarter decision-making.
Enhancing Customer Experiences in Retail Stores with Edge AI – Examples
Edge AI transforms the shopping experience, enabling retailers to offer more streamlined and more personalized services based on real-time data, thereby boosting customer satisfaction and sales. Key benefits include:
Contactless Checkout: AI-driven self-checkouts allow customers to select products captured by cameras. Thus, bypassing the need for scanning product codes, which streamlines both standard and automated checkout processes. For example, Amazon’s Just Walk Out technology allows customers to pick up items and leave the store without traditional checkout, enhancing convenience and reducing wait times.
Retail operational excellence and cost optimization with Edge AI – Examples
Edge AI also significantly enhances operational efficiency, especially operational in-store efficiency, reduces losses, and helps lower costs (while at the same time enhancing sustainability):
Loss Prevention: Retail shrink, exacerbated by inflation-driven shoplifting and self-checkout vulnerabilities, costs the industry over $100 billion annually. Advanced sensors and intelligent cameras combined with Edge AI help detect early signs of theft or fraud. Thus, allowing security measures to intervene promptly, and independently from an internet connection.
Energy Savings: Smart sensors and Edge AI can be used to optimize the use of energy for lighting, heating, ventilation, water use, etc. For example, 45 Broadway, a 32-story office building in Manhattan, implemented an AI system that analyzes real-time data. That included temperature, humidity, sun angle, and occupancy patterns – to proactively adjust HVAC settings. This integration led to a 15.8% reduction in HVAC-related energy consumption. Plus, saving over $42,000 annually and reducing carbon emissions by 37 metric tons in just 11 months.
Conclusion: Edge AI as Retail’s Strategic Imperative
Yet, Edge AI isn’t just about running AI models locally. It’s about creating an autonomous, resilient system where on-device vector databases, local processing, and hybrid data sync work together. This combination enables real-time retail intelligence while keeping costs low, data private, and operations uninterrupted. To stay ahead, businesses should invest in edge-ready infrastructure with on-device vector databases and data sync that works on-premise at their core. Those who hesitate risk losing ground to nimble competitors who have already tapped into real-time, in-store intelligence.
Hybrid systems, combining lightning-fast offline-first edge response times with the power of the cloud, are becoming the norm. IDC projects that 78% of retailers will adopt these setups by 2026, saving an average of $3.6 million per store annually. In an inflation-driven market, Edge AI isn’t just a perk – it’s a critical strategy for thriving in the future of retail. By leveraging Edge AI-powered on-device databases, retailers gain the speed, efficiency, and reliability needed to stay competitive in an AI-driven retail landscape.
The healthcare industry is experiencing an unprecedented surge in data generation, responsible for approximately 30% of the world’s total data volume. This vast and fast-growing amount of health data is the primary force behind the digital transformation of healthcare. Only through the adoption of advanced technologies can healthcare providers manage, analyze, and secure this information. While COVID-19 accelerated this shift, contributing to the explosion of health data, the ongoing demand for real-time patient insights, personalized treatment, and improved operational efficiency continues to drive the sector toward digitalization and AI. Simultaneously, growing data privacy concerns, increasing costs, and heavier regulatory requirements are challenging the use of cloud computing to manage this data. A megashift to Edge Computing and Edge AI is addressing these challenges, enabling a faster, safer, and more reliable digital healthcare infrastructure.
The digital healthcare market 2024 and beyond, a high-speed revolution
Prior to COVID, growth in digital health adoption stalled. However, digitalization in the healthcare industry has sky-rocketed since the start of the pandemic. Reflecting this market turnaround, followed by the rise of advanced digital tools like AI, recent years have been record-breaking for investments in healthcare companies. A trend that will continue in the next years, as analysts predict rapid growth across digital healthcare market sectors:
Drivers of growth and change in digital healthcare
Digital Healthcare Growth Driver 1: Growing Medical IoT Device Adoption
There will be a projected 40 billion IoT devices by 2030.IoMT devices already accounted for 30% of the entire IoT device market in 2020. Internet of Medical Things (IoMT) are hardware devices designed to process, collect, and/or transmit health–related data via a network. According to Gartner, 79% of healthcare providers are already using IoT in their processes, i.e. remote health monitoring via wearables, ingestible sensors, disinfection robots, or closed-loop insulin delivery systems.IoMT devices increase safety and efficiency in healthcare, and future technical applications, like smart ambulances or augmented reality glasses that assist during surgery, are limitless.
IoMT devices accounted for 30% of the IoT device market
Digital Healthcare Growth Driver 2: The Explosion of Health Data
Growing IoMT adoption is subsequently driving a rapid increase in the amount of collected health data. According to the RBC study, the healthcare industry is now responsible for approximately 30% of the world’s total data volume. By 2025, healthcare data is expected to continue growing at a 36% CAGR, outpacing data volumes from sectors like manufacturing, financial services, and media. Big health data sets are being used to revolutionize healthcare, bringing new insights into fields like oncology, and improving patient experience, care, and diagnosis. According to the Journal of Big Data: “taken together, big data will facilitate healthcare by introducing prediction of epidemics (in relation to population health), providing early warnings of disease conditions, and helping in the discovery of novel biomarkers and intelligent therapeutic intervention strategies for an improved quality of life.” In fact, the healthcare analytics market is projected to reach $129.7 billion by 2028, growing at a 23.5% CAGR. This growth is driven by the need for real-time data processing, personalized medicine, and predictive analytics to manage chronic conditions and optimize hospital operations.
Healthcare data occupies ~30% of the world’s total data volume
Digital Healthcare Growth Driver 3: Artificial Intelligence
The increase in healthcare data opens up new opportunities and challenges to apply advanced technologies like big data analytics and artificial intelligence (AI) to improve healthcare delivery, patient outcomes, and operational efficiency. For instance, AI is being used to analyze medical imaging data, identify patterns in electronic health records, and predict patient outcomes, contributing to improved patient care. By 2026, AI is projected to save the global healthcare industry over $150 billion annually, by answering “20 percent of unmet clinical demand.”
Digital Healthcare Growth Driver 4: Artificial Intelligence
With the rise of IoMT and the boost in healthcare data,Edge Computing is becoming a key driver of healthcare digitalization. The majority of IoMT devices (55.3 %) currently operate on-premise rather than in the cloud, ensuring faster, more secure real-time data processing. This shift to Edge Computing enhances data privacy and reduces latency, which is critical in life-critical medical applications. Additionally, the development ofSmall Language Models (SLMs) for on-device AI (Edge AI) allows healthcare providers to deploy AI-powered solutions directly on medical devices. This helps with tasks like remote monitoring and diagnostics without the need for cloud connectivity, which is particularly beneficial in environments with limited internet access.
As IoMT continues to evolve, Edge Computing will play an essential role in supporting healthcare’s increasing demand for real-time data processing. By 2025, it is projected that 75% of the healthcare data will be generated at the Edge, further driving the adoption of these technologies across the industry.
75% of the healthcare data will be generated at the Edge in 2025
Digital Healthcare Growth Driver 5: Underlying Social Megatrends
The global population is growing; global life expectancy is rising. Accordingly, by 2030 the world needs more energy, more food, and more water. Explosive population growth in some areas versus declines in others contributes to shifts in economic power, resource allocation, societal habits, and norms. Many Western populations are aging rapidly. E.g. in America, the number of people 65+ is expected to nearly double to 72.1 million by 2034. Because the population is shrinking at the same time, elder care is a growing challenge and researchers are looking to robots to solve it.
Health megatrends focus not only on the prevention of disease, but also on the perception of wellness, and new forms of living and working. Over this decade more resources will be spent on health and longevity, leading to artificially and technologically enhanced human capabilities. More lifestyle-related disorders and diseases are expected to emerge in the future.
A focus on health and longevity will lead to artificial & tech-enhanced human capabilities
The Challenges of Healthtech
Along with more data, more devices, and more opportunities also comes more responsibility and more costs for healthcare providers.
Data Volume and Availability With the growing number of digital healthcare and medical devices, a dazzling volume of health data is created and collected across many different channels. It will be vital for the healthcare industry to reliably synchronize and combine data across devices and channels. Due to the sheer volume, reliable collection and analysis of this data is a major challenge. After it’s been processed, data needs to be available on demand, i.e. in emergency situations that require reliable, fast, available data.
IT Costs Medical devices contribute a large portion to healthcare budgets. However as data volumes grow, data costs will also become a relevant cost point. Sending all health data to the cloud to be stored and processed is not only slow and insecure, it is also extremely costly. To curb mobile network and cloud costs, much health data can be stored and processed at the edge, on local devices, with only necessary data being synced to a cloud or central server. By building resilient data architecture now, healthcare providers (e.g. hospitals, clinics, research centers) can avoid future costs and headaches.
Edge Computing is Integral to Data-driven Healthcare Ecosystems
With big data volumes, industries like healthcare need to seek out resilient information architectures to accommodate growing numbers of data and devices. To build resilient and secure digital infrastructure, healthcare providers will need to utilize both cloud computing and edge computing models, exploiting the strengths of both systems.
Cloud & Edge: What’s the Difference?
Cloud Computing information is sent to a centralized data center, to be stored, processed and sent back to the edge. This causes latency and a higher risk of data breaches. Centralized data is useful for large-scale data analysis and the distribution of data between i.e. hospitals and doctors’ offices.
Edge Computing Data is stored and processed on or near the device it was created on. Edge Computing works without an internet connection, and thus is reliable and robust in any scenario. It is ideal for time-sensitive data (real-time), and improved data privacy and security.
Edge Computing contributes to resilient and secure healthcare data systems
Transforming Healthcare with Edge Computing
Use Case: Secure and Up to Date Digital Record Keeping in Doctors Offices
For private doctors’ offices, embracing digitalization comes with different hurdles than larger healthcare providers. Often, offices do not keep a dedicated IT professional on staff, and must find digital solutions that serve their needs, while allowing them to comply with ever-increasing data regulations. As an industry used to legislative challenges, GPs know that sensitive patient data must be handled with care.
Solution providers serving private doctors’ offices are using edge databases to help keep patient data secure. An edge database allows private GPs to collect and store digital data locally. In newer practice setups, doctors use tablets, like iPads, throughout their practice to collect and track patient data, take notes and improve flexibility. This patient data should not be sent or stored in a central cloud server as this increases the risk of data breaches and opens up regulatory challenges. In a cloud-centered setup, the doctor also always needs to rely on a constant internet connection being available, making this also a matter of data availability
Accordingly, the patient data is stored locally, on the iPads, accessible only by the doctor treating the patient. Some of the data is synchronized to a local, in-office computer at the front desk for billing and administration. Other data is only synchronized for backup purposes and encrypted. Such a setup also allows synchronizing data between iPads, enabling doctors to share data in an instant.
Use Case: Connected Ambulances – Real-Time Edge Data from Home to Hospital
Between an incidence location and the hospital, a lot can happen. What if everything that happened in the ambulance was reliably and securely tracked and shared with the hospital, seamlessly? There are already trials using 5G technology to stream real-time data to hospitals, allowing ambulance medics to access patient data while in transit. Looking to the future, Edge Computing will enable digital healthcare applications to function in real-time and reliably anywhere and anytime, e.g. a moving ambulance, in the tunnel, or a remote area, enabling ambulance teams and doctors to give the best treatment instantly / on-site, while using available bandwidth and networks when available to seamlessly synchronize the relevant information to the relevant healthcare units, e.g. the next hospital. This will decrease friction, enhance operational processes, and improve time to treatment.
Digital Healthcare: Key Take-Aways
Digital healthcare is a fast-growing industry; more data and devices alongside new tech are empowering rapid advances. Finding ways to utilize growing healthcare data, while ensuring data privacy, security and availability are key challenges ahead for healthcare providers. The healthcare industry must find the right mix of technologies to manage this data, utilizing cloud for global data exchange and big data analytics, while embracing Edge Computing for it’s speed, security, and resilience.
It’s all about data availability. Either in emergency situations, or simply to provide a smooth patient experience, data needs to be fast, reliable, and available: when you need it where you need it.
Edge computing alongside other developing technologies like 5G or Artificial Intelligence will empower a new and powerful digital healthcare ecosystem.
ObjectBox provides edge data software, to empower scalable and resilient digital innovation on the edge in healthcare, automotive, and manufacturing. ObjectBox’ edge database and data synchronization solution is 10x faster than any alternative, and empowers applications that respond in real-time (low-latency), work offline without a connection to the cloud, reduce energy needs, keep data secure, and lower mobile network and cloud costs.
What is Edge AI?Edge AI (also: “on-device AI”, “local AI”) brings artificial intelligence to applications at the network’s edge, such as mobile devices, IoT, and other embedded systems like, e.g., interactive kiosks. Edge AI combines AI with Edge Computing, a decentralized paradigm designed to bring computing as close as possible to where data is generated and utilized.
What is Cloud AI? As opposed to this, cloud AI refers to an architecture where applications rely on data and AI models hosted on distant cloud infrastructure. The cloud offers extensive storage and processing power.
An Edge for Edge AI: The Cloud
Example: Edge-Cloud AI setup with a secure, two-way Data Sync architecture
Today, there is a broad spectrum of application architectures combining Edge Computing and Cloud Computing, and the same applies to AI. For example, “Apple Intelligence” performs many AI tasks directly on the phone (on-device AI) while sending more complex requests to a private, secure cloud. This approach combines the best of both worlds – with the cloud giving an edge to the local AI rather than the other way around. Let’s have a look at the advantages on-device AI brings to the table.
Faster Response Rates. Processing data locally cuts down travel time for data, speeding up responses.
Increased Availability. On-device processing makes apps fully offline-capable. Operations can continue smoothly during internet or data center disruptions.
Sustainability/costs. Keeping data where it is produced and used minimizes data transfers, cutting networking costs and reducing energy consumption—and with it, CO2 emissions.
Challenges of Local AI on the Edge
Data Storage and Processing: Local AI requires an on-device database that runs on a wide variety of edge devices (Mobile,IoT, Embedded) and performs complex tasks such as vector search locally on the device with minimal resource consumption.
Data Sync: It’s vital to keep data consistent across devices, necessitating robust bi-directional Data Sync solutions. Implementing such a solution oneself requires specialized tech talent, is non-trivial and time-consuming, and will be an ongoing maintenance factor.
Small Language Models:Small Language Models (SLMs) like Phi-2 (Microsoft Research), TinyStories (HuggingFace), and Mini-Giants (arXiv) are efficient and resource-friendly but often need enhancement with local vector databases for better response accuracy. An on-device vector database allows on-device semantic search with private, contextual information, reducing latency while enabling faster and more relevant outputs. For complex queries requiring larger models, a database that works both on-device and in the cloud (or a large on-premise server) is perfect for scalability and flexibility in on-device AI applications.
On-device AI Use Cases
On-device AI is revolutionizing numerous sectors by enabling real-time data processing wherever and whenever it’s needed. It enhances security systems, improves customer experiences in retail, supports predictive maintenance in industrial environments, and facilitates immediate medical diagnostics. On-device AI is essential for personalizing in-car experiences, delivering reliable remote medical care, and powering personal AI assistants on mobile devices—always keeping user privacy intact.
Personalized In-Car Experience: Features like climate control, lighting, and entertainment can be adjusted dynamically in vehicles based on real-time inputs and user habits, improving comfort and satisfaction. Recent studies, such as one by MHP, emphasize the increasing consumer demand for these AI-enabled features. This demand is driven by a desire for smarter, more responsive vehicle technology.
Remote Care: In healthcare, on-device AI enables on-device data processing that’s crucial for swift diagnostics and treatment. This secure, offline-capable technology aligns with health regulations like HIPAA and boosts emergency response speeds and patient care quality.
Personal AI Assistants: Today’s personal AI assistants often depend on the cloud, raising privacy issues. However, some companies, including Apple, are shifting towards on-device processing for basic tasks and secure, anonymized cloud processing for more complex functions, enhancing user privacy.
ObjectBox for On-Device AI – an edge for everyone
The continuum from Edge to Cloud
ObjectBox supports AI applications from Edge to cloud. It stands out as the first on-device vector database, enabling powerful Edge AI on mobile, IoT, and other embedded devices with minimal hardware needs. It works offline and supports efficient, private AI applications with a seamless bi-directional Data Sync solution, completely on-premise, and optional integration with MongoDB for enhanced backend features and cloud AI.
Interested in extending your AI to the edge? Let’s connect to explore how we can transform your applications.
As artificial intelligence (AI) continues to evolve, companies, researchers, and developers are recognizing that bigger isn’t always better. Therefore, the era of ever-expanding model sizes is giving way to more efficient, compact models, so-called Small Language Models (SLMs). SLMs offer several key advantages that address both the growing complexity of AI and the practical challenges of deploying large-scale models. In this article, we’ll explore why the race for larger models is slowing down and how SLMs are emerging as the sustainable solution for the future of AI.
From Bigger to Better: The End of the Large Model Race
Up until 2023, the focus was on expanding models to unprecedented scales. But the era of creating ever-larger models appears to be coming to an end. Many newer models like Grok or Llama 3 are smaller in size yet maintain or even improve performance compared to models from just a year ago. The drive now is to reduce model size, optimize resources, and maintain power.
The Plateau of Large Language Models (LLMs)
Why Bigger No Longer Equals Better
As models become larger, developers are realizing that the performance improvements aren’t always worth the additional computational cost. Breakthroughs in knowledge distillation and fine-tuning enable smaller models to compete with and even outperform their larger predecessors in specific tasks. For example, medium-sized models like Llama with 70B parameters and Gemma-2 with 27B parameters are among the top 30 models in the chatbot arena, outperforming even much larger models like GPT-3.5 with 175B parameters.
The Shift Towards Small Language Models (SLMs)
In parallel with the optimization of LLMs, the rise of SLMs presents a new trend (see Figure). These models require fewer computational resources, offer faster inference times, and have the potential to run directly on devices. In combination with an on-device database, this enables powerful local GenAI and on-device RAG apps on all kinds of embedded devices, like on mobile phones, Raspberry Pis, commodity laptops, IoT, and robotics.
Advantages of SLMs
Despite the growing complexity of AI systems, SLMs offer several key advantages that make them essential in today’s AI landscape:
Accessibility As SLMs are less resource-hungry (less hardware requirements, less CPU, memory, power needs), they are more accessible for companies and developers with smaller budgets. Because the model and data can be used locally, on-device / on-premise, there is no need for cloud infatstructure and they are also usable for use cases with high privacy requirements. All in all, SLMs democratize AI development and empower smaller teams and individual developers to deploy advanced models on more affordable hardware.
Cost Reduction and Sustainability Training and deploying large models require immense computational and financial resources, and comes with high operational costs. SLMs drastically reduce the cost of training, deployment, and operation as well as the carbon footprint, making AI more financially and environmentally sustainable.
On-Device AI for Privacy and Security SLMs are becoming compact enough for deployment on edge devices like smartphones, IoT sensors, and wearable tech. This reduces the need for sensitive data to be sent to external servers, ensuring that user data stays local. With the rise of on-device vector databases, SLMs can now handle use-case-specific, personal, and private data directly on the device. This allows more advanced AI apps, like those using RAG, to interact with personal documents and perform tasks without sending data to the cloud. With a local, on-device vector database users get personalized, secure AI experiences while keeping their data private.
The Future: Fit-for-Purpose Models: From Tiny to Small to Large Language models
The future of AI will likely see the rise of models that are neither massive nor minimal but fit-for-purpose. This “right-sizing” reflects a broader shift toward models that balance scale with practicality. SLMs are becoming the go-to choice for environments where specialization is key and resources are limited. Medium-sized models (20-70 billion parameters) are becoming the standard choice for balancing computational efficiency and performance on general AI tasks. At the same time, SLMs are proving their worth in areas that require low latency and high privacy.
Innovations in model compression, parameter-efficient fine-tuning, and new architecture designs are enabling these smaller models to match or even outperform their predecessors. The focus on optimization rather than expansion will continue to be the driving force behind AI development in the coming years.
Conclusion: Scaling Smart is the New Paradigm
As the field of AI moves beyond the era of “bigger is better,” SLMs and medium-sized models are becoming more important than ever. These models represent the future of scalable and efficient AI. They serve as the workhorses of an industry that is looking to balance performance with sustainability and efficiency. The focus on smaller, more optimized models demonstrates that innovation in AI isn’t just about scaling up; it’s about scaling smart.
SQLite and SQLite alternatives - databases for the Mobile and IoT edge
Overview of SQLite and SQLite alternatives as part of the mobile / edge database market with a comprehensive comparison matrix (last updated autumn 2024)
Therefore, there is a renewed need for on-device databases like SQLite and SQLite alternatives to persist and manage data on edge devices. On top, due to the distributed nature of the edge, there is a need to manage data flows to / from and between edge devices. This can be done withEdge Databases that provide a Data Sync functionality (SQLite alternatives only, as SQLite doesn’t support this). Below, we’ll take a close look at SQLite and its alternatives with consideration of today’s needs.
Databases for the Edge
While being quite an established market with many players, the database market is still growing consistently and significantly. The reason is that databases are at the core of almost any digital solution, and directly impact business value and therefore never going out of fashion. With the rapid evolvements in the tech industry, however, databases evolve too. This, in turn, yields new database types and categories. We have seen the rise of NoSQL databases in the last 20 years, and more recently some novel database technologies, like graph databases and time-series databases, and vector databases.
With AI and accordingly vector databases being all the hype since 2022/2023, the database market is indeed experiencing fresh attention. Due to the speed with which AI is evolving, we’re however already leaving the “mainframe era of AI” and entering the distributed Edge AI space. With SQLite not supporting vector search and related vector database functions, this adds a new dimension to this ever-present topic. There is a need for local, on-device vector databases to support on-device AI that’s independent of an Internet connection, reliably fast, and keeps data on the device (100% private).
Both, the shift back from a centralised towards a decentralised paradigm, and the growing number of restricted devices call for a “new type” of an established database paradigm. SQLite has been around for more than 20 years and for good reason, but the current market shift back to decentralized computing happens in a new environment with new requirements. Hence, the need for a “new” database type, based on a well-established database type: “Edge databases”. Accordingly, a need for SQLite alternatives that consider the need for decentralized data flows and AI functionalities (depending on the use case of course; after all SQLite is a great database).
What is an Edge Database?
Edge databases are a type of databases that are optimised for local data storage on restricted devices, like embedded devices, Mobile, and IoT. Because they run on-device, they need to be especially resource-efficient (e.g. with regards to battery use, CPU consumption, memory, and footprint). The term “edge database” is becoming more widely-used every year, especially in the IoT industry. In IoT, the difference between cloud-based databases and ones that run locally (and therefore support Edge Computing) is crucial.
What is a Mobile Database?
We look at mobile databases as a subset of edge databases that run on mobile devices. The difference between the two terms lies mainly in the supported operating systems / types of devices. Unless Android and iOS are supported, an edge database is not really suited for the mobile device / smartphone market. In this article, we will use the term “mobile database” only as “database that runs locally on a mobile (edge) device and stores data on the device”. Therefore, we also refer to it as an “on-device” database.
What are the advantages and disadvantages of working with SQLite?
SQLite is a relational database that is clearly the most established database suitable to run on edge devices. Moreover, it is probably the only “established” mobile database. It was designed in 2000 by Richard Hipp and has been embedded with iOS and Android since the beginning. Now let’s have a quick look at its main advantages and disadvantages:
Advantages
Disadvantages
20+ years old (should be stable ;))
Toolchain, e.g. DB browser
No dependencies, is included with Android and iOS
Developers can define exactly the data schema they want
Full control, e.g. handwritten SQL queries
SQL is a powerful and established query language, and SQLite supports most of it
Debuggable data: developers can grab the database file and analyse it
20+ years old ( less state-of-the-art tech)
Using SQLite means a lot of boilerplate code and thus inefficiencies ( maintaining long running apps can be quite painful)
No compile time checks (e.g. SQL queries)
SQL is another language to master, and can impact your app’s efficiency / performance significantly…
The performance of SQLite is unreliable
SQL queries can get long and complicated
Testability (how to mock a database?)
Especially when database views are involved, maintainability may suffer with SQLite
What are the SQLite alternatives?
There are a bunch of options for making your life easier, if you want to use SQLite. You can use an object abstraction on top of it, an object-Relational-Mapper (ORM), for instance greenDAO, to avoid writing lots of SQL. However, you will typically still need to learn SQL and SQLite at some point. So what you really want is a full blown database alternative, like any of these: Couchbase Lite, Interbase, LevelDB, ObjectBox, Oracle Berkeley DB, Mongo Realm, SnappyDB, SQL Anywhere, or UnQLite.
While SQLite really is designed for small devices, people do run it on the server / cloud too. Actually, any database that runs efficiently locally, will be highly efficient on big servers too, making them a sustainable lightweight choice for some scenarios. However, for server / cloud databases, there are a lot of alternatives you can use as a replacement like e.g. MySQL, MongoDB, or Cloud Firestore.
Bear in mind that, if you are looking to host your database in the cloud with apps running on small distributed devices (e.g. mobile apps, IoT apps, any apps on embedded devices etc.), there are some difficulties. Firstly, this will result in higher latency, i.e. slow response-rates. Secondly, the offline capabilities will be highly limited or absent. As a result, you might have to deal with increased networking costs, which is not only reflected in dollars, but also CO2 emissions. On top, it means all the data from all the different app users is stored in one central place. This means that any kind of data breach will affect all your and your users’ data. Most importantly, you will likely be giving your cloud / database provider rights to that data. (Consider reading the general terms diligently). If you care about privacy and data ownership, you might therefore want to consider a local database option, as in an Edge Database. This way you can decide, possibly limit, what data you sync to a central instance (like the cloud or an on-premise server).
SQLite alternatives Comparison Matrix
To give you an overview, we have compiled a comparison table including SQLite and SQLite alternatives. In this matrix we look at databases that we believe are apt to run on edge devices. Our rule of thumb is the databases’ ability to run on Raspberry Pi type size devices. If you’re reading this on mobile, click here to view the full matrix.
Edge Database
Short description
License / business model
Android / iOS*
Type of data stored
Central Data Sync
P2P Data Sync
Offline Sync (Edge)
Data level encryption
Flutter / Dart support
Vector Database (AI support)
Minimum Footprint size
Company
SQLite
C programming library; probably still 90% market share in the small devices space (personal assumption)
Embedded / portable database with P2P and central synchronization (sync) support; pricing upon request; some restrictions apply for the free version. Secure SSL.
Partly proprietary, partly open-source, Couchbase Lite is BSL 1.1
Is there anything we’ve missed? What do you agree and disagree with? Please share your thoughts with us via Twitter or email us on contact[at]objectbox.io.
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