45.8k
Get started
SQLite and SQLite alternatives – a comprehensive overview

SQLite and SQLite alternatives – a comprehensive overview

by | May 7, 2024 | Android, Edge Database, IoT, Mobile Database | 2 comments

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)

Digitalization is still on the rise, as is the number of connected devices (from 13 billion connected IoT devices + 15 billion mobile devices operating in 2021 already). Data volumes are growing accordingly ( 3.5 quintillion bytes of data is produced daily in 2023), and centralised (typically cloud-based) computing canbot support all the current needs. This has led to a shift from the cloud to the edge. 

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 with Edge 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). 

We’re expecting vector databases that run locally on a wide variety of devices (aka Edge Vector Databases) to become the next big thing, surpassing even what we have seen happening in the server vector database space. And we wouldn’t be astonished if the synchronizing of vector data is a game changer for Edge AI. Time will tell 😉


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).

database-evolution-towards-edge-vector-databases
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) Public domain embedded on iOS and Android Relational No No No No, but option to use SQLCipher to encrypt SQLite Flutter plugins (ORMs) for SQLite, but nothing from Hwaci No, but various early & unofficial extensions are available < 1 MB Hwaci
Couchbase Mobile / Lite 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 Android / iOS JSON Documents / NoSQL db Yes Yes No Database encryption with SQLCipher (256-bit AES) Unofficial Flutter plugin for Couchbase Lite Community Edition No < 3,5 MB Couchbase
InterBase ToGo / IBLite Embeddable SQL database. Proprietary Android / iOS Relational No No No 256 bit AES strength encryption No No < 1 MB Embarcadero
LevelDB Portable lightweight key-value store, NoSQL, no index support; benchmarks from 2011 have been removed unfortunately New BSD Android / iOS Key-value pairs / NoSQL db No No No No Unofficial client that is very badly rated No < 1 MB LevelDB Team
LiteDB A .Net embedded NoSQL database MIT license Android / iOS (with Xamarin only) NoSQL document store, fully wirtten in .Net No No No Salted AES No No < 1 MB LiteDB team
Realm DB  Embedded object database  Apache 2.0 Android / iOS Object Database deprecated No deprecated Yes Yes No 5 MB+ Acquired by MongoDB in 2019, Data Sync deprecated in 2024; DB still available as open source, not maintained
ObjectBox NoSQL Edge Vector Database with out-of-the-box Data Sync for Mobile and IoT; fully ACID compliant; benchmarks available as open source. Open Core (plus Apache 2.0 bindings) Android / iOS / Linux / Windows / any POSIX Object-oriented NoSQL edge database for high-performance on edge devices in Mobile and IoT Yes WIP Yes transport encryption; additional encryption upon request Yes First local vector database fo on-device Edge AI released May 2024 < 1 MB ObjectBox
Oracle Database Lite Portable with P2P and central sync support as well as support for sync with SQLite Proprietary Android / iOS Relational Yes Yes No 128-bit AES Standard encrytion No No < 1 MB Oracle Corporation
SQL Anywhere Embedded / portable database with central snyc support with a stationary database, pricing now available here Proprietary Android / iOS Relational Yes, tied to using other SAP tech though (we believe) No No AES-FIPS cipher encryption for full database or selected tables No No   SAP (originally Sybase)
UnQLite Portable lightweight embedded db; self-contained C library without dependency. 2-Clause BSD Android / iOS Key-value pairs / JSON store / NoSQL db No No No 128-bit or 256-bit AES standard encryption not yet; might be coming though; there was a 0.0.1 released some time ago No ~ 1.5 MB Symisc systems
extremeDB Embedded relational database Proprietary iOS In-memory relational DB, hybrid persistence No No No AES encryption No No < 1 MB McObject LLC
redis DB High-performance in-memory Key Value store with optional durability Three clause BSD license, RSAL and Proprietary No K/V in-memory store, typically used as cache No No No TLS/SSL-based encryption can be enabled for data in motion. Unofficial redis Dart client available No on-device vector database, but cloud vector support An empty instance uses ~ 3MB of memory redislabs (the original author of redis left in 2020)
Azure SQL Edge  Designed as a SQL database for the IoT edge; however, due to the footprint it is no Edge Database Proprietary No Relational DB for IoT No No No will provide encryption No Not on-device 500 MB+ Microsoft

If you are interested in an indication of the diffusion rate of databases, check out the following database popularity ranking: http://db-engines.com/en/ran. If you are interested to learn more about SQLite, there is a great Podcast interview with Richard Hipp that is worthwhile listening to.

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. 

Make sure to check out the ObjectBox Database & try out ObjectBox Sync. You can get started in minutes and it’s perfect if you are using an object-oriented programming language, as it empowers you to work with your objects within the database. More than 1,000,000 developers already use this Edge Database designed specifically for high performance on small, connected, embedded devices.

Start Using an Edge DB now

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

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

by | May 30, 2023 | Edge Computing, Insights, IoT, Mobile Database

Edge Databases – from trends to use cases

Data is decentralized. Cloud computing is centralized.

Forcing the decentralized world into the centralized cloud topology is not only inefficient, but also economically, ecologically and socially wasteful – and sometimes simply impossible.

To drive digitization and extract value from decentralized data, we need to give the cloud an edge, or more precisely add Edge Computing. Edge computing is a decentralized topology for storing and processing data as close as possible to the data source, i.e., the place where the data is produced, at the edge of the network.

Valuable data is increasingly generated in a decentralized manner – outside traditional and centralized data centers and cloud environments. The dominance of centralized cloud computing approaches slows down digitization and the use of this existing decentralized data. Therefore, according to Gartner (2023) “Edge computing is integral to digital transformation”, and we need infrastructure technologies for the edge that enable developers to quickly and reliably work with decentralized edge data.

Edge Database (Foundation for Edge Data Management) is a new type of database that addresses these requirements. Developers need fast local data persistence and decentralized data flows (Data Sync) to implement edge solutions. Edge Databases solve these core edge functionalities out-of-the-box, allowing application developers to quickly implement edge solutions.

Table of Contents

Megatrend to decentralized Edge Computing
Urgently needed: Software infrastructure for edge computing
What is an edge database?
When do you need an edge database?
Edge Database Use Case Example in Manufacturing
The Future of Edge Databases  

Megatrend to decentralized Edge Computing

By 2025, 30+ billion IoT devices will be creating ~4.6 trillion GB of data per day. The growing numbers of devices and data volume, variety, and velocity, as well as bandwidth infrastructure limitations, make it infeasible to store and process all data in a centralized cloud. On top, new use cases come with new requirements, a centralized cloud infrastructure cannot meet. For example, soft and hard response rate requirements, offline-functionality, and security and data protection regulations.

trends-driving-edge-computing

These trends accelerate the shift away from centralized cloud computing to a decentralized edge computing topology. Edge computing refers to decentralized data processing at the “edge” of the network. For example, in a car, on a machine, on a smartphone, or in a building. Hardware specifications do not capture the definition of an “edge device”. The crucial point is rather the decentralized use of data at, or as close as possible to, the data source.

Edge computing itself is not a technology but a topology, and according to McKinsey, one of the top growing trends in tech in 2021. The technologies needed to implement the edge computing topology are still inadequate. More specifically, there is a gap in basic “core” edge technologies, so-called “software infrastructure”. This gap is one of the main reasons for the failure of edge projects.

Needed: Infrastructure Software for Edge Computing

With computing shifting to the edge of the network, the needs of this decentralized topology become clear:

hugh performance db

Need for fast local data storage

→ i.e. a machine on the factory floor collects data on stiffness, friction, pressure points. There is limited space on the device, and typically no connection to the Internet. Even with an Internet connection, high data rates quickly push the available bandwidth, as well as associated networking / cloud costs, to the limit. To be able to use this data, it must be persisted in a structured manner at the edge, e.g. stored locally in a database.

feedback dialogue icon

Need for reliable on-device data flows

→ i.e. the car is an edge device consisting of many control units. Therefore, data must be stored on multiple control units. In order to access and use the data within several of the control units of the car, the data must be selectively synchronized between the devices. A centralized structure and thus a single point of failure is unthinkable.

data sync

Need for edge-to-edge-to-cloud data flows

→ i.e. in a manufacturing hall: Typically, you will find any number of diverse devices from sensors to brownfield to greenfield devices, and no internet connectivity. At the same time, there are diverse employee devices such as tablets or smartphones, as well as central PCs, and a cloud. To extract value from the data, it must be available in raw, aggregated, or summary form, in different places. This means it needs to be synchronized efficiently and selectively, with possible conflicts resolved.

types-of-data-on-edge-flexibility

Need for flexible edge data management

→ e.g. with the rise of IoT, time-series data have become common. However, time series data alone is usually not sufficient, and needs to be combined with other data structures (like objects) to add value. At the same time, a push to standardize data formats in industries (e.g. VSS in automotive or Umati in Industrial IoT) requires that the database supports flexible data structures.

Developing solutions without software infrastructure on an individual level is possible, but has many drawbacks:

Custom in-house implementations are cumbersome, slow, costly, and typically scale poorly. Oftentimes, applications or certain feature sets become unfeasible to deliver because of the lack of core software infrastructure. Legacy code and individual workarounds create problems over the lifetime of a product. Instead of a thriving ecosystem, only a few big players are able to implement edge solutions. Innovation and creativity are limited. An edge database is part of the solution and enables the entire edge ecosystem to build edge applications faster, cheaper and more efficiently.

lack-of-core-tech-for-the-edge

What is an edge database?

An edge database is a new type of database specifically tailored to the unique requirements of the Edge Computing topology. An edge database has specific features that make it easy for application developers to use decentralized data from edge devices when and where needed and focus on value creation. It removes the burden of implementing underlying functionalities for secure storage and the decentralized synchronization of data.

First, an edge database is optimized for resource efficiency (CPU, memory, …) and performance on resource-constrained devices (embedded devices, IoT, mobile). It has a small footprint of a few megabytes. Traditional databases such as MySQL or MongoDB are too large and cumbersome for typical edge devices, and unsuitable for computing at the edge.  

An edge device without data flows to/from other devices is just a data island with very limited utility. Accordingly, an edge database must support the management of decentralized data flows. There is no more efficient way than at the database level. This ideally includes a range of conflict resolution strategies due to the decentralized and multi-directional structure of the Edge.

Data security and protection is an increasingly important issue and can quickly become a showstopper for edge projects.

What is an Edge Database?

When do you need an Edge Database?

Most IoT applications need to store and synchronize data. An edge database is always useful when functions / applications are planned that:

  • should work offline and independent of an internet connection
  • need to guarantee fast response times
  • work with a lot of, possibly high-frequency data
  • need to serve many devices at the same time
  • need historical data

In addition, developers also often decide to use an edge database to save time and nerves, or to be able to react quickly and flexibly to future requirements.

Edge Database Use Case Example in Manufacturing

Today, you can find everything from low-frequency brownfield devices to high-frequency greenfield devices on a factory floor. As a rule, the machine controllers in use are not designed to store or transmit data. They usually lack not only the functionality, but also the resources to support this. Therefore, additional edge devices are often needed to collect, analyze and interpret the huge amounts of data that each machine produces on site. For such an edge device, rapid data persistence and ingestion, and efficient data flow from edge-to-edge and edge-to-cloud are at the heart of value creation. The clear separation of machine control and edge data processing unit ensures that there is no risk of unintentional interference with the machine controller. An edge device with a powerful edge database can support multiple use cases on the shop floor today:

manufacturing-edge-computing-use-case

1. Operational efficiency

Process optimization along the line to increase quality and reduce damage. When the first machine in a production line uses a new batch of material, i.e. in sheet metal processing, one of the first steps is to cut a sheet to the required size. At this stage, the machine can already detect the differences in the metal compared to a previous batch (deviations are allowed within the DIN standard). With an Edge device this data can be evaluated, and the relevant information passed on to the next machine. With this data machines further down the line can avoid damage / breakpoints of the material.

2. Condition monitoring

Continuous machine condition monitoring reduces downtime and increases maintenance efficiency. A constant stream of high-frequency machine data is compared against the fingerprint of the machine. Any slight deviation is immediately detected and reported. Catching deviations early reduces down-times and costly repairs.

3. Historical Data

Historical data is stored for learning and training to optimize the production line. With an edge database, the data is persisted and thus available in the event of faulty behavior. In case of an error, the data preceding the incident can be analyzed and used to find the causes and predict, or even avoid, such an error in the future. Chances are that “fuzzy expert knowledge” already available at the production site can be translated into deterministic rules when tested with these data sets.

The future of Edge Databases 

Edge computing provides numerous benefits and enables many applications and functionalities that are only possible with edge computing. However, only a few (usually large) players have been able to create value in edge computing projects, gaining competitive advantages. One reason is a lack of basic edge software. A thriving edge ecosystem necessitates edge software infrastructure that addresses the fundamental recurring needs of edge projects. Edge databases are a critical component in the development of such an ecosystem. Looking further into the future, AI applications’ biggest challenge at this moment is resource-intensity. Databases can give AI apps a long term memory and boost AI app performance while lowering resource use. Distributing this to edge devices will be a game changer for everyday use of AI apps. 

Check out the ObjectBox Edge Database
Why do we need Edge Computing for a sustainable future?

Why do we need Edge Computing for a sustainable future?

by | May 25, 2023 | Edge Computing, Insights, IoT, Sync

Centralized data centers use a lot of energy and water, emit a lot of CO2, and generate a lot of electronic waste. In fact, cloud data centers are already responsible for around 300 Mt CO2-eq of greenhouse gas emissions [1]. And the energy consumption of data centers is increasing at an exponential rate [2].

While more data centers are switching to green energy [3], this approach is not nearly enough to solve the problem. A more sustainable approach is to reduce unnecessary cloud traffic, central computation, and storage as much as possible by shifting computation to the edge. In our experience, just reducing data overhead and unnecessary data traversals can easily cut 60-90% of data traffic and thus significantly impact the CO2 footprint of an application, as well as costs.

Edge Computing stores and uses data on or near the device on which it was created. This reduces the amount of traffic sent to the cloud and, on a large scale, has a significant impact on energy consumption and carbon emissions.

Why do Digitization projects need to think about sustainability now?

Given the gravity of the climate crisis, every industry needs to assess its potential environmental impact and find ways to reduce its carbon footprint. The digital world, and its most valuable commodity, data, should not be any different. The digital transformation is ongoing and with it electronic devices and IT usage numbers are exploding. Thus, new apps must consider their carbon footprint throughout their lifecycle, especially resource use in operation and at scale [4]. 

Also, think about this: The share of global electricity used by data centers is already estimated to be around 1-3% [1] and data centers generate 2% of worldwide CO2 emissions (on par with the aviation industry) [5]. 54% of all emissions due to cloud data centers are caused by the big hyperscalers (Google, Amazon, Microsoft, Alibaba Cloud) [6]. On top of this, providing and maintaining cloud infrastructure (manufacturing, shipping of hardware, buildings and lines) also consumes a huge amount of greenhouse gasses [7] and produces a lot of abnormal waste (e.g. toxic coolants) at the end of life [8].

sustainable edge computing

Bearing that in mind, the growth rate for data center demand is concerning. The steady increase in data processing, storage, and traffic in the future, comes with a forecasted electricity consumption by data centers to grow by 10% a year [9]. In fact, estimations expect the communications industry to use 20% of all the world’s electricity by 2025 [10].

sustainable edge computing

Shifting to green energy is a good step. However, a more effective and ultimately longer term solution requires looking at the current model of data storage, filtering, processing and transferal. By implementing Edge Computing, we can reduce the amount of useless and wasteful data traversing to and from the cloud as much as possible, thus reducing overall energy requirements in the long term. Of course, everyone can make a difference with their daily behavior and for developers that is especially true: Applying green coding principles helps producing applications that produce lower CO2 emissions over the whole app lifetime. 

What is Edge Computing?

Until recently 90% of enterprise data was sent to the cloud, but this is changing rapidly. In fact, this number is dropping to only 25% by 2025, according to Gartner. By then, most of the data will be stored and used locally, on the device it was created on, e.g. on smartphones, cars, trains, machines, watches. This is Edge Computing, and it is an inherently decentralized computing paradigm (as opposed to the centralized cloud computing approach). Accordingly, every edge device needs the same technology stack (just in a much smaller format) as a cloud server. This means: An operating system, a data storage / persistence layer (database), a networking layer, security functionalities etc. that run efficiently on restricted hardware.

As you can only use the devices’ resources, which can be pretty limited, inefficient applications can push a device to its limits, leading to slow response rates, crashes, and battery drain.

edge device architecture

EDGE DEVICE ARCHITECTURE

Edge Computing is much more than some simple data pre-processing, which takes advantage of only a small portion of the computing that is possible on the edge. An Edge Database is a prerequisite for meaningful Edge Computing. With an Edge Database, data can be stored and processed on the devices directly (the so-called edge). Only useful data is sent to the server and saved there, reducing the networking traffic and computing power used in data centers tremendously, while also making use of the computing resources of devices which are already in use. This greatly reduces bandwidth and energy required by data centers. On top, Edge Computing also provides the flexibility to operate independently from an Internet connection, enables fast real time response rates, and cuts cloud costs.

Why is Edge Computing sustainable?

Edge Computing reduces network traffic and data center usage

With Edge Computing the amount of data traversing the network can be reduced greatly, freeing up bandwidth. Bandwidth is a measure of the quantity / size of data a network can transfer in a given time frame. Bandwidth is shared among users. Accordingly, the more data is supposed to be sent via the network at a given moment, the slower the network speed. Data on the edge is also much more likely to be useful and indeed used on the edge, in context of its environment. Instead of constantly sending data strems to the cloud, it therefore makes sense to work with the data on the edge and only send that data to the cloud that really is of use there (e.g. results, aggregated data etc.).

Edge computing is optimized for efficiency

Edge “data centers” are typically more efficient than cloud data centers. As described above, resources on edge devices are restricted. Therefore, and as opposed to cloud infrastructure, edge devices do not scale horizontally. That is one reason why every piece of the edge tech stack is – typically and ideally – highly optimized for resource efficiency. Any computing done more efficiently helps reduce energy consumption. Taking into account the huge number of devices already deployed , the worldwide impact of reducing resource use for the same operations is significant.

Edge Computing uses available hardware

There is a realm of edge devices already deployed that is currently underused. Many existing devices are capable of data persistence, and some even for fairly complex computing. When these devices – instead – send all of their data to the cloud, an opportunity is lost. Edge Computing enables companies to use existing hardware and infrastructure (retrofitting),  taking advantage of the available computing power. If these devices continue to be underused, we will need to build bigger and bigger central data centers, simultaneously burdening existing network infrastructure and reducing bandwidth for senselessly sending everything to the cloud.

Cloud versus Edge: an Example

Today, many projects are built based on cloud computing. Especially in first prototypes or pilots, cloud computing offers an easy and fast start. However, with scale, cloud computing often becomes too slow, expensive, and unreliable. In a typical cloud setup, data is gathered on edge devices and forwarded to the cloud for computation and storage. Often a computed result is sent back. In this design, the edge devices are dumb devices that are dependent upon a working internet connection and a working cloud server; they do not have any intelligence or logic of their own. In a smart home cloud example, data would be sent from devices in the home, e.g. a thermostat, the door, the TV etc. to the cloud, where it is saved and used.

Cloud vs Edge

If the user would want to make changes via a cloud-based mobile app when in the house, the changes would be sent to the cloud, changed there and then from there be sent to the devices. When the Internet connection is down or the server is not working, the application will not work.

With Edge Computing, data stays where it is produced, used and where it belongs – without traversing the network unnecessarily. This way, cloud infrastructure needs are reduced in three ways: Firstly, less network traffic, secondly, less central storage and thirdly less computational power. Rather, edge computing makes use of all the capable hardware already deployed in the world. E.g. in a smart home, all the data could stay within the house and be used on site. Only the small part of the data truly needed accessible from anywhere would be synchronized to the cloud.

Cloud vs Edge

Take for example a thermostat in such a home setting: it might produce 1000s of temperature data points per minute. However, minimal changes typically do not matter and data updates aren’t necessary every millisecond. On top, you really do not need all this data in the cloud and accessible from anywhere.

With Edge Computing, this data can stay on the edge and be used within the smart home as needed. Edge Computing enables the smart home to work fast, efficiently, and autonomous from a working internet connection. In addition, the smart home owner can keep the private data to him/herself and is less vulnerable to hacker attacks. 

How does ObjectBox make Edge Computing even more sustainable?

ObjectBox improves the sustainability of Edge Computing with high performance and efficiency: our 10X speed advantage translates into less use of CPU and battery / electricity. With ObjectBox, devices compute 10 times as much data with equivalent power. Due to the small size and efficiency, ObjectBox runs on restricted devices allowing application developers to utilize existing hardware longer and/or to do more instead of existing infrastructure / hardware.

Alongside the performance and size advantages, ObjectBox’ Sync solution takes care of making data available where needed when needed. It allows synchronization in an offline setting and / or to the cloud. Based on efficient syncing principles, ObjectBox Sync aims to reduce unnecessary data traffic as much as possible and is therefore perfectly suited for efficient, useful, and sustainable Edge Computing. Even when syncing the same amount of data, ObjectBox Sync reduces the bandwidth needed and thus cloud networking usage, which incidentally reduces cloud costs.

ObjectBox’ Time Series feature, provides users an intuitive dashboard to see patterns behind the data, further helping users to track thousands of data points/second in real-time.

How Edge Computing enables new use cases that help make the world more sustainable

As mentioned above, there are a variety of IoT applications that help reduce waste of all kinds. These applications can have a huge impact on creating a more sustainable world, assuming the applications themselves are sustainable. Three powerful examples to demonstrate the huge impact IoT applications can have on the world:

food-icon

Reducing Food Waste

From farm to kitchen, IoT applications can help to reduce food waste across the food chain. Sensors used to monitor the cold chain, from field to supermarket, can ensure that food maintains a certain temperature, thus guaranteeing that products remain food safe and fresh longer, reducing food waste. In addition, local storage can be used to power apps that fight household waste (you can learn how to build a food sharing app yourself in Flutter with this tutorial).

light bulb

Smart City Lighting

Smart City Lighting: Chicago has implemented a system which allows them to save approx. 10 million USD / year and London estimates it can save up to 70% of current electricity use and costs as well as maintenance costs through smart public lighting systems [10].

water-drop

Reducing Water Waste

Many homes and commercial building landscapes are still watered manually or on a set schedule. This is an inexact method of watering, which does not take into account weather, soil moistness, or the water levels needed by the plant. Using smart IoT water management solutions, landscape irrigation can be reduced, saving water and improving landscape health.

These positive effects are all the more powerful when the applications themselves are sustainable.

Sustainable digitization needs an edge

The benefits of cloud computing are broad and powerful, however there are costs to this technology. A combination of green data centers and Edge Computing helps to resolve these often unseen costs. With Edge Computing we can reduce the unnecessary use of bandwidth and server capacity (which comes down to infrastructure, electricity and physical space) while simultaneously taking advantage of underused device resources. Also with AI growing in popularity, Edge Computing will become very relevant for sustainable AI applications. AI applications are very resource intensive and Edge AI will help to distribute workloads in a resourceful manner, lowering the resource-use. One example of this is an efficient local vector database. ObjectBox amplifies these benefits, with high performance on small devices and efficient data synchronization – making edge computing an even more sustainable solution.

Try ObjectBox: the Edge Database
  1. https://www.iea.org/reports/data-centres-and-data-transmission-networks
  2. https://8billiontrees.com/carbon-offsets-credits/carbon-ecological-footprint-calculators/carbon-footprint-of-data-centers/
  3. https://www.datacenterdynamics.com/en/opinions/future-data-centers-green/
  4. 13 https://www.datacamp.com/blog/environmental-impact-data-digital-technology
  5. https://www.computerworld.com/article/3431148/why-data-centres-are-the-new-frontier-in-the-fight-against-climate-change.html
  6. https://medium.com/@jaychapel/aws-vs-azure-vs-google-cloud-market-share-2019-what-the-latest-data-shows-dc21f137ff1c
  7. https://thereader.mitpress.mit.edu/the-staggering-ecological-impacts-of-computation-and-the-cloud/
  8. https://link.springer.com/article/10.1007/s12053-019-09833-8#ref-CR2
  9. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/investing-in-the-rising-data-center-economy
  10. https://www.smartcitylab.com/blog/urban-environment/lighting-the-road-to-smart-cities-and-sustainability/

 

Cross platform Data Sync: a simple example

Cross platform Data Sync: a simple example

by | Mar 16, 2022 | Edge Computing, IoT, Mobile Database, Sync, Tutorial

Cross platform data sync can be simple: In this tutorial we will show you how you can easily sync data across devices.

Built for fast and effortless data access on and across embedded devices from Mobile to IoT, ObjectBox keeps data in sync between devices for you. The Database and Data Snyc works across platforms (iOS, Android, Linux, Rasbian, Windows, MacOS) and supports a variety of languages with easy native APIs (Swift, Java, Kotlin, C / C++, Flutter / Dart, Golang).

For example, you can sync between an Industrial IoT sensor app in Go and a C++ monitoring application – and a mobile Android app written in Kotlin or Java – and of course an iOS app written in Swift – and… you get the drift 😉

ObjectBox is a high-performance embedded database for Edge Computing with integrated Data Sync. The ObjectBox database is quick to set up and free and easy to use. Our powerful and intuitive APIs are a great match for multiplatform development environments.

Syncing data across devices – a task-list app example

In this tutorial, we are going to sync data across three instances of an example task-list app (written in C++, Go and Java).

With the task-list app, users can create simple text-based tasks and mark them as done. It stores tasks together with their creation dates. There is also a parameter to store the date when the task was completed. It acts as a filter for users to only see unfinished tasks. 

This app is a standard cross platform ObjectBox example that is available for all language bindings. Here are the repositories of each example app that we will be looking at today:

Overview of the example code 

In this section, we’ll quickly review how the the task-list example app uses ObjectBox Sync. For a more detailed description, check out the Sync docs. If you want to see how each of these steps were incorporated into the example code, go to the next section.

Note: The basic use of the database and its sync features is the same for all programming languages. If you haven’t used the ObjectBox DB yet, please refer to the corresponding documentation: C/C++ Docs, Java/Kotlin/Dart Docs, Go Docs, Swift Docs.

For sync to work in any app, we generally only need four things:

  1. The sync-enabled library — this is not the same as the general ObjectBox library and has to be downloaded separately.
  2. Database objects enabled for sync — for this we need include the sync annotation in the ObjectBox schema file.
  3. ObjectBox Sync Server — please apply for a free Sync Trial here to get your own copy of the Sync Server (available for Linux and Docker). Note that this will only have to be started once and in this tutorial we’ll show you how to run the server on Linux. If you are using Docker, follow the steps outlined here.
  4. Start a Sync Client in the app — as one can see from the Sync Client docs, creating and starting a sync client is just a matter of a couple of lines of code.

Important: When syncing between different apps, please make sure that the UIDs in the model JSON file (e.g. objectbox-default.json) are the same everywhere.

    How to run the examples

    Here you’ll find requirements and step-by-step guides for running the task-list example app in each of the three languages.

    C++ example app

    Requirements

    New to C++? Check out our beginner C++ ObjectBox installation tutorial.

    • WSL Ubuntu
    • CMake
    • Git
    • C++
    • Clang

      Step-by-step guide

      1.Start by creating a CMakelists.txt file:

      Now configure and build the project via CMake: Configure (Clang), CMake: Build.

      2. Sync-enabled objects: note the first line in tasklist.fbs.

      3. [if not running a server already] Start the ObjectBox Sync Server on Linux by running ./sync-server --model build/_deps/objectbox-src/examples/cpp-gen/objectbox-model.json --unsecured-no-authentication

      where sync-server is the path to your sync server executable. You can find more information about the server in the Sync Server docs.

      4. Sync Client: launch [objectbox-c-examples-cpp-gen-sync], and the Sync Client will start automatically. You can see how it was implemented in main.cpp.

      As this is just an example, we opted for no authentication to make things simple. This is not what you would use in production. We currently offer two authentication methods: shared secret and Google Sign-In. Here is the relevant Sync docs section on authentication options that explains how to use these.

      5. Let’s add a first task, called “task-cpp” (new task-cpp-1), to check if our C++ app syncs correctly. The output should look like this:

      Output of the C++ tasklist example app, showing a newly added task

      6. You can finally open the Admin UI to check if the task appears there. This is most easily done by opening http://127.0.0.1:9980/ in any web browser. For a more detailed description of what this can do, check out the Admin UI docs.

      Go example app

      Requirements

      • WSL Ubuntu
      • Go (see how to configure it for VS Code here)
      • Git

      Step-by-step guide

      1. First, clone the objectbox-go repository to your VS Code project. Make sure the current directory is objectbox-go.

      2. Sync-enabled objects. There are two versions of the task-list example: with and without sync. To run the one with sync, we need to enable our Task object for syncing. To do this, simply put the sync annotation on a new line in examples/tasks/internal/model/task.go:

      Then run the generator: go generate examples/tasks/internal/model/task.go to update the schema.

      3. [if not running a server already] Now start the ObjectBox Sync Server: ./sync-server --model=examples/tasks/internal/model/objectbox-model.json --unsecured-no-authentication,

      where sync-server is the path to your sync server file. You can find more information about the server in the Sync Server docs.

      4. Run go run examples/tasks/main.go. The Sync Client will start within the app; check main.go to see how this was implemented.

      As this is just an example, we opted for no authentication to make things simple. This is not what you would use in production. We currently offer two authentication methods: shared secret and Google Sign-In. Here is the relevant Sync docs section on authentication options that explains how to use these.

      5. Now we can add our first task (new task-go) – if it synced correctly, you should already see that from the output of the app. In particular, there will be a message from the change listener (“received 1 changes”):

      Output of the Go task-list example app after adding a first task

      6. Lastly, open the Admin UI to check if the task appears there. This is most easily done by opening http://127.0.0.1:9980/ in any web browser. For a more detailed description of what this can do, check out the Admin UI docs.

      Admin UI showing a task created with the Go example app

      Java (Android) example app

      Requirements

      • Java
      • Android Studio

      Step-by-step guide

        1. First of all, open Android Studio and clone the objectbox-examples repository via File → New → Project from Version Control. Use this URL: https://github.com/objectbox/objectbox-examples.git
        2. Sync-enabled objects: check out Task.java to see how this was done (note the @Sync annotation).
        3. [if not running a server already] Start the ObjectBox Sync Server: 

      ./sync-server --model android-app-sync/objectbox-models/default.json --unsecured-no-authentication,

      where sync-server is the path to your sync server file. You can find more information about the server in the Sync Server docs.

      1. Now you can run “android-app-sync” on a device of your choice. The Sync Client will start in the app. 

      As this is just an example, we opted for no authentication to make things simple. This is not what you would use in production. We currently offer two authentication methods: shared secret and Google Sign-In (only for Java, Kotlin, Dart, C & Go). Here is the relevant Sync docs section on authentication options that explains how to use these.

      5. Add a new task called “task-java”.

      6. Finally, open the Admin UI to check if the task appears there. This is most easily done by opening http://127.0.0.1:9980/ in any web browser. For a more detailed description of what this can do, check out the Admin UI docs.

      Next Steps

      How easy was that? cool Now that you’ve run your first ObjectBox Sync example, why not build something yourself? Use any combination of the supported languages to build your own cross platform app.

      We’re eager to see your use case examples! Don’t hesitate to share your results with us by posting on Social Media and tagging @objectbox_io, or simply sending us an email on contact[at]objectbox.io. 

       

      If you want to learn more about how ObjectBox can be used in IoT, here is an overview of different use cases. 

      What is Data Synchronization + How to Keep Data in Sync

      What is Data Synchronization + How to Keep Data in Sync

      by | Dec 29, 2021 | Edge Computing, Insights, IoT, Sync

      What is Data Sync / Data Synchronization in app development?

      Data Synchronization (Sync) is the process of establishing consistency and consolidation of data between different devices. It is fundamental to most IT solutions, especially in IoT and Mobile. Data Sync entails the continuous harmonization of data over time and typically is a complex, non-trivial process. Even corporates struggle with its implementation and had to roll back Data Sync solutions due to technical challenges. 

      The question Data Sync answers is

      phone-data-sync-with-machine-payment-automatic-data

      How do you keep data sets from two (or more) data stores / databases – separated by space and time – mirrored with one another as closely as possible, in the most efficient way?

      Data Sync challenges include asynchrony, conflicts, slow bandwidth, flaky networks, third-party applications, and file systems that have different semantics.

      Data Sync versus Data Replication in Databases

      sync-data-better-than-replication

      Data replication is the process of storing the same data in several locations to prevent data loss and improve data availability and accessibility. Typically, data replication means that all data is fully mirrored / backed up / replicated on another instance (device/server). This way, all data is stored at least twice. Replication typically works in one direction only (unidirectional); there is no additional logic to it and no possibility of conflicts.

      In contrast, Data Sync typically relates to a subset of the data (selection) and works in two directions (bi-directional). This adds a layer of complexity, because now conflicts can arise. Of course, if you select all data for synchronisation into one direction, it will yield the same result as replication. However, replication cannot replace synchronization.

      Why do you need to keep data in sync?

      Think about it – if clocks were not in sync, everyone would live on a different time. While I can see an upside to this, it would result in many inefficiencies as you could not rely on schedules. When business data is not in sync (up-to-date everywhere), it harms the efficiency of the organization due to:

      • Isolated data silos
      • Conflicting data / information states
      • Duplicate data / double effort
      • Outdated information states / incorrect data

      In the end, the members of such an organization would not be able to communicate and collaborate efficiently with each other. They would instead be spending a lot of time on unnecessary work and “conflict resolution”. On top, management would miss an accurate overview and data-driven insights to prioritize and steer the company. The underlying mechanism that keeps data up-to-date across devices is a technical process called data synchronization (Sync). And while we expect these processes to “just work”, someone needs to implement and maintain them, which is a non-trivial task.

      Growing data masses and shifts in data privacy requirements call for sensible usage of network bandwidth and the cloud. Edge computing with selective data synchronization is an effective way to manage which data is sent to the cloud, and which data stays on the device. Keeping data on the edge and synchronizing selective data sets effectively, reduces the data volume that is transferred via the network and stored in the cloud. Accordingly, this means lower mobile networking and cloud costs. On top, it also enables higher data security and data privacy, because it makes it easy to store personal and private data with the user. When data stays with the user, data ownership is clear too.

      Unidirectional Data Replication

      replication-data-sync-database

      Bidirectional Data Synchronization

      how-to-sync-data-what-is-data-sync

      Out-of-the-box Sync magic: Syncing is hard

      Almost every Mobile or IoT application needs to sync data, so every developer is aware of the basic concept and challenges. This is why many experienced developers appreciate out-of-the-box solutions. While JSON / REST offers a great concept to transfer data, there is more to Data Sync than what it looks like at a glance. Of course, the complexity of Sync varies widely depending on the use case. For example, the amount of data, data changes, synchronous / asynchronous sync, and number of devices (connections), and what kind of client-server or peer-to-peer setup is needed, all affect the complexity.

      iceburg-building-data-synchronization

      What looks easy in practice hides a complex bit of coding and opens a can of worms for testing. For an application to work seamlessly across devices – independent of the network, which can be offline, flaky, or only occasionally connected – an app developer must anticipate and handle a host of local and network failures to ensure data consistency. Moreover, for devices with restricted memory, battery and/or CPU resources (i.e. Mobile and IoT devices), resource sensitivity is also essential. Data storage and synchronization solutions must be both effective / efficient, and sustainable.

      How to Keep Data in Sync Without the Headache?

      Thankfully, there are out-of-the-box data synchronization solutions available on the market, which solve data syncing for developers. They fall broadly into two categories: cloud-dependent data synchronization, and independent, “edge” data synchronization. Cloud-based solutions, like Firebase, require a connection to the internet to function. Data is sent to and requested from the cloud constantly. Edge solutions, like ObjectBox, also offer “Offline Sync”: Data is stored in an efficient on-device database, synchronization on and between edge devices can be done continually without an Internet connection, and Dat Sync with a cloud or a backend that is not located on premise occurs once the device(s) goes online. Below, we summarize the most popular market offerings for data synchronization (offline and cloud based):

      mongo-realm-logo

      Couchbase

      Couchbase is a Cloud DB, Edge DB and Sync offering that requires the use of Couchbase servers.

      firebase-logo

      Firebase

      Firebase is a Backend as a Service (BaaS) offering from Google (acquired). Google offers it as a cloud hosted solution for mobile developers.

      mongo-realm-logo

      Mongo Realm

      Realm was acquired by MongoDB in 2019; the Mongo Realm Sync solution is now in Alpha and available hosted with MongoDB.

      mongo-realm-logo

      ObjectBox

      ObjectBox is a DB for any device, from restricted edge devices to servers, and offers an out-of-the-box Sync solution. ObjectBox enables self-hosting on-premise / in the cloud, as well as Offline Sync.

      pasre-logo-comparison

      Parse

      Parse is a BaaS offering that Facebook acquired and shut down. Facebook open sourced the code. The GitHub repository is not officially maintained. You can host Parse yourself or use a Parse hosting service.

      Data Sync, Edge Computing, and the Future of Data

      There is a megashift happening in computing from centralized cloud computing to Edge Computing. Edge computing is a decentralized topology entailing storing and using data as close to the source of the data as possible, i.e. directly on edge devices. Accordingly, the market is growing rapidly with projections estimating continuing growth with a 34% CAGR for the next five years. The move from the cloud to the edge is strongly driven by new use cases and growing data masses.  Edge data persistence and Data Sync (managing decentralized data flows), especially “Offline Sync”, are the key technologies needed for Edge Computing. Using edge data persistence, data can be stored and processed on the edge. This means application always work, independent from a network connection, offline. Faster response times can be guaranteed. With Offline Sync, data can be synchronized between several edge devices in any location independant from an Internet connection. Once a connection becomes available, selected data can be synchronized with  a central server. By exchanging less data with the cloud or a central instance, data synchronization reduces the burden on the network. This brings down mobile network and cloud costs, and reduces the amount of energy used: a win-win-win solution. It also enables data privacy by design.

      Try the fastest Edge Sync solution today
      IoT, Edge Computing, and Digitalization in Healthcare

      IoT, Edge Computing, and Digitalization in Healthcare

      by | Jun 28, 2021 | Case Study, Edge Computing, Insights, IoT

      COVID-19 accelerated the digitization of healthcare, contributing to growing IoT adoption and exploding health data volumes. This digital transformation helps to improve efficiency and reduce costs, while opening new avenues for enhanced patient experience and well-being. 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 is addressing these challenges enabling a faster, safer and more reliable digital healthcare infrastructure.

      The digital healthcare market 2020 and beyond, a high speed revolution

      Prior to COVID, growth in digital health adoption stalled. [1] However, digitalization in the healthcare industry has sky-rocketed since the start of the pandemic. Reflecting this market turnaround, the third quarter of 2020 was a record year for investments in healthcare companies. [2] A trend that will continue in the next years, as analysts predict rapid growth across digital healthcare market sectors:

      healthcare-edge-iot-trends

      Drivers of growth and change in digital healthcare

      Digital Healthcare Growth Driver 1: COVID

      The COVID pandemic accelerated the digitization of healthcare, pushing doctors, patients – and their data – to the virtual world. [8] The year 2020 marks the tipping point for digital healthcare offerings. With healthcare providers and patients forced to use digital means, adoption barriers have been removed for good. Indeed, a recent study from Forrester indicates that 36% of adults found that the care they received virtually was just as effective as what they would have received in person, and over 30% of adults will seek virtual care again in the future. [9]

      health-care-edge-computing

      Over 30% of adults will seek
      virtual care again in the future

      Digital Healthcare Growth Driver 2: Growing Medical IoT Device Adoption

      There will be a projected 55 billion IoT devices by 2025. [10] Internet of Medical Things (IoMT) are hardware devices designed to process, collect, and/or transmit health related data via a network. IoMT devices are projected to make up 30% of the entire IoT device market by 2025. [11] According to Gartner, 79% of healthcare providers are already using IoT in their processes, [12] i.e. remote health monitoring via wearables, ingestible sensors, [13] disinfection robots, [14] or closed-loop insulin delivery systems.15 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 are projected to make up
      30% of the IoT device market by 2025

      health-care-edge-computing

      Digital Healthcare Growth Driver 3: The Explosion of Health Data

      Growing IoMT adoption is subsequently driving a rapid increase in the amount of collected health data. According to an IDC study, healthcare data is growing exponentially projected 36% CAGR through 2025; health data is expected to eclipse data volumes from sectors like manufacturing, financial services, and media. [16] The increase in healthcare data opens up new opportunities to apply technology to improve healthcare like e.g. big data analysis, AI and ML. In fact, the healthcare analytics market is expected to reach $84.2 billion by 2027 with a 26% CAGR. [17]

      health-care-edge-computing

      Healthcare data will experience a
      36% CAGR through 2025

      Digital Healthcare Growth Driver 4: Technological innovations: Edge Computing, AI, and VR

      Big health data sets are being used to revolutionize healthcare, bringing new insights into fields like oncology,18 and improving patient experience, care, and diagnosis: “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.” [19] In a November 2020 survey from Intel, 84% of healthcare providers shared that Artificial Intelligence deployments had occurred or were planned in their clinical workflow, an increase from 37% in 2018. This is unsurprising, as AI technologies are predicted to save the healthcare industry up to $150 billion per year, by answering “20 percent of
      unmet clinical demand.” [20]

      Augmented and Virtual Reality are also finding a place in healthcare settings. VR tools have been shown to reduce pain, [21] and are being used in therapy as a means to help patients overcome painful and traumatic experiences. Experts expect a realm of future AR applications in the operating room, assisting doctors during surgical procedures.

      Current or planned AI deployments are at
      84% in 2020, up from 37% in 2018

      health-care-edge-computing

      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, 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. [22]

      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 the coming 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. [22]

      health-care-edge-computing

      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 opportunity also comes more
      responsibility and more costs for healthcare providers.

      health-care-edge-computing

      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. [23] 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.

      health-care-edge-computing

      Reliability, Privacy, and Data Security are extremely important in health
      technology; 70% of healthcare consumers are concerned about data privacy. [24] Data use is often governed by increasingly strict national regulation, i.e. HIPAA (USA) and / or GDPR (Europe). [25] With the number of cyber-attacks in the healthcare industry on the rise, [26] healthcare professionals must be even more diligent about the storage and processing of data. In addition, healthtech must be extremely well vetted; failures can cost lives – typical “banana products”, which ripen with the customers, are a no-go.

      health-care-edge-computing

      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 headache.

      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 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.

      health-care-edge-computing

      Edge Computing contributes to resilient and secure healthcare data systems

      health-care-edge-computing

      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 set up, the doctor also always needs to rely on a constant internet connection being available, making this also a matter of data availability

      health-care-edge-computing

      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

      health-care-edge-computing

      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? Already there are trials underway using 5G technology to stream real time data to hospitals, [27] and allowing ambulance medics to access patient data while in transit. [28] Looking to the future, Edge Computing will enable digital healthcare applications to function in realtime 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.

      health-care-edge-computing

      Underutilized data plays a major role in health-tech innovation, [29] data is the lifeline of future healthcare offerings; however, there is still much work to be done to improve the collection, management and analysis of this data.

      health-care-edge-computing

      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.

      health-care-edge-computing

      Edge computing alongside other developing technologies like 5G, Augmented and Virtual Reality, Artificial Intelligence and Machine Learning 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.

      Get in touch to learn more about ObjectBox' Edge Technology

      Sources
      1. https://www.accenture.com/us-en/insights/health/leaders-make-recent-digital-health-gains-last
      2. https://www.cbinsights.com/research-state-of-healthcare-q3-2020
      3. https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare%C2%A0
      4. https://www.grandviewresearch.com/industry-analysis/wearable-medical-devices-market
      5. https://www.marketsandmarkets.com/PressReleases/iot-healthcare.asp
      6. https://www.grandviewresearch.com/press-release/global-mhealth-app-market
      7. https://www.globenewswire.com/news-release/2020/05/23/2037920/0/en/Global-Digital-Health-Market-was-Valued-at-USD-111-4-billion-in-2019-and-is-Expected-to-Reach-USD-510-4-billion-by-2025-Observing-a-CAGRof-29-0-during-2020-2025-VynZ-Research.html
      8. https://www.sciencemag.org/features/2020/11/telemedicine-takes-center-stage-era-covid-19
      9. https://go.forrester.com/blogs/will-virtual-care-stand-the-test-of-time-if-youre-asking-the-question-its-time-tocatch-up/
      10. https://knowledge4policy.ec.europa.eu/foresight/topic/accelerating-technological-change-hyperconnectivity/hyperconnectivity-iot-digitalisation_en
      11. https://mobidev.biz/blog/technology-trends-healthcare-digital-transformation
      12. https://www.computerworld.com/article/3529427/how-iot-is-becoming-the-pulse-of-healthcare.html
      https://www.gartner.com/en/documents/3970072
      13. https://science.sciencemag.org/content/360/6391/915
      14. http://emag.medicalexpo.com/disinfection-robots-against-covid-19/
      15. https://www.theverge.com/2019/12/13/21020811/fda-closed-loop-insulin-system-software-diabetes-tandemcontrol-iq
      16. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
      17. https://www.prnewswire.com/news-releases/healthcare-analytics-market-worth-84-2-billion-by-2027–growingat-a-cagr-of-26-from-2020–pre-and-post-covid-19-market-opportunity-analysis-and-industry-forecasts-bymeticulous-research-301117822.html
      18. https://www.nature.com/articles/s41437-020-0303-2
      19. June 2019, https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0
      20. https://www2.stardust-testing.com/en/the-digital-transformation-trends-and-challenges-in-healthcare
      21. https://www.geekwire.com/2018/snowworld-melts-away-pain-burn-patients-using-virtual-reality-snowballs/
      22. https://www.pwc.com/gx/en/government-public-services/assets/five-megatrends-implications.pdf
      23. https://www2.stardust-testing.com/en/the-digital-transformation-trends-and-challenges-in-healthcare
      24. https://www.accenture.com/_acnmedia/PDF-133/Accenture-Digital-Health-Tech-Vision-2020.pdf#zoom=40
      25. https://www.lexology.com/library/detail.aspx?g=99b83b76-3f2f-4b23-a5c3-30ad576af369
      26. https://www.medicaleconomics.com/view/cyberattack-threat-to-health-care-providers-on-the-rise
      https://www.healthcareitnews.com/news/fbi-hhs-warn-increased-and-imminent-cyber-threat-hospitals
      https://blogs.microsoft.com/on-the-issues/2020/11/13/health-care-cyberattacks-covid-19-paris-peace-forum/
      27. https://www.vodafone.co.uk/business/5g-for-business/5g-customer-stories/connected-ambulance
      28. https://www.digitalhealth.net/2019/04/london-ambulance-access-patient-data/
      29. https://news.crunchbase.com/news/for-health-tech-startups-data-is-their-lifeline-now-more-than-ever/