Edge Database Comparison: SQLite and SQLite alternatives

Edge Database Comparison: SQLite and SQLite alternatives

SQLite and SQLite alternatives for the Mobile and IoT edge

Updated comparison of mobile databases / edge databases

Note: This is an updated version of an earlier Mobile Database Comparison. Last Update: 2020.

What is a mobile database?

While Wikipedia defines a mobile database as “Mobile computing devices (e.g., smartphones and PDAs) store and share data over a mobile network, or a database which is actually stored by the mobile device,” we solely refer to the latter ones as a mobile database. Meaning only databases that run on the mobile device (as the edge device) itself, locally, and store the data on the device. Therefore, we also refer to it as “on-device” database.

What is an edge database?

The term edge database is too young to have a Wikipedia article. However, we see it used in the IoT industry increasingly. In the field of IoT applications, it is important to distinguish databases that run locally – “on the edge” – as opposed to “in the cloud”. A mobile database is a subset of edge databases, meaning the only difference is the device the database runs on. The main difference is the operating system support: There simply are edge databases that do not run on Android and / or iOS. Thus, these databases, while small enough for Edge Computing, and indeed qualifying as edge databases, are not suited for typical mobile devices, and therefore no mobile database.

What is an edge device?

An edge device may be any device from a sensor, to an IoT gateway, to a car, to a Raspberry Pi, to a mobile phone (smartphone) to an on-premise server. Typically, the challenge arises when running on the smaller, more restricted devices. Generally, any database can run on a big on-premise server or cloud infrastructure with unlimited resources, but only few fit on a Raspberry Pi Zero. The other way around is no issue, meaning an edge databases can run well on a server. Therefore, we look at databases that run on Raspberry Pi type size of devices (Rule of thumb).

Edge Devices to run mobile /edge databases on

What are the advantages and disadvantages of working with SQLite?

SQLite is easily the most established edge database and probably the only “established” mobile database. SQLite is public domain and maintained by Richard Hipp. SQLite database has been around since the year 2000 and been embedded with iOS and Android since the beginning. SQLite is a relational database.

Advantages  Disadvantages
  • Toolchain, e.g. DB browser
  • No dependencies, is included with Android and iOS
  • Developers can define exactly the data schema they want
  • Developers have 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 analyze it
  • Rock-solid, widely used technology, established since the year 2000
  • Using SQLite means a lot of boilerplate code and thus inefficiencies (also in the long run with the app maintenance)
  • 1 MB BLOB Limitation on Android
  • No compile time checks (e.g. SQL queries)
  • The performance of SQLite is unreliable
  • SQL is another language to master
  • 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 SQLite alternatives?

There are plenty of alternatives to working with SQLite directly. If you simply want to avoid writing lots of SQL and boilerplate code, you can use an object abstraction on top of SQLite. This abstraction layer is usually an ORM (object/relational mapper), e.g. greenDAO. While an ORM makes it easy to use SQLite at the beginning, there typically comes a point “where you hit SQLite”; so even when using an abstraction layer you need to understand SQLite and SQL in the longrun.

However, if you rather seek a complete replacement for SQLite, there are a few alternative databases: Couchbase Lite, Interbase, LevelDB, ObjectBox, Oracle Berkeley DB (formerly Oracle’s mobile database was “Oracle Database Lite”), Realm (now Mongo Realm), SnappyDB, SQL Anywhere, and UnQLite.

Obviously, if your also looking for alternatives that run in the cloud, there are a lot of cloud / server options out there that you can use as a replacement like e.g. Firebase. Though, with these your app will not work offline, response rates will be slower than with an on-device database and cannot be guaranteed, and last not least you will have much higher networking / cloud costs. You can find out more about the benefits of Edge Computing.

To give you an overview, we have compiled a small comparison table:

Edge Database Android / iOS* Type of data stored Sync Central Sync P2P Offline Sync Data level encryption License / business model Short description Minimum Footprint size Company
Azure SQL Edge (in preview) No Relational DB No No No will provide encryption Proprietary Designed as a SQL database for the IoT edge; however, due to the footprint it is no edge database 500 MB+ Microsoft
Couchbase Mobile (prior Couchbase Lite) Android / iOS JSON Documents / NoSQL db Yes Yes No Database encryption with SQLCipher (256-bit AES) Apache 2.0 Embedded / portable database with P2P and central synchronization (sync) support. Secure SSL. < 3,5 MB Couchbase
extremeDB iOS In-memory relational DB, hybrid persistence No No No AES encryption Proprietary Embedded relational database 200kB McObject LLC
ForestDB Android / iOS Key-value pairs / NoSQL db No No No No Apache 2.0 Portable lightweight key-value store, NoSQL database    
InterBase ToGo / IBLite Android / iOS Relational No No No 256 bit AES strength encryption Proprietary Embeddable SQL database. 400 KB Embarcadero
LevelDB Android / iOS Key-value pairs / NoSQL db No No No No New BSD Portable lightweight key-value store, NoSQL, no index support; benchmarks from 2011 have been removed unfortunately. 350kB LevelDB
Team
LiteDB Android / iOS (with Xamarin only) NoSQL document store No No No Salted AES MIT license A .Net embedded NoSQL database 350kb  
Mongo Realm (acquired in 2019) Android / iOS Object Database Yes No   Yes Proprietary with Apache 2.0 License APIs Embedded object database 5 MB+ Realm Inc
ObjectBox 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 Apache 2.0 and Proprietary Embedded object-oriented NoSQL high-performance edge database with out-of-the-box data synchronization; fully ACID compliant; benchmarks available. < 1 MB ObjectBox
Oracle Database Lite Android / iOS Relational Yes Yes No 128-bit AES Standard encrytion Proprietary Portable with P2P and central sync support as well as support for sync with SQLite < 1 MB Oracle Corporation
redis DB No K/V in-memory store, typically used as cache No No No TLS/SSL-based encryption can be enabled for data in motion. Three clause BSD license, RSAL and Proprietary High-performance in-memory Key Value store with optional durability An empty instance uses ~ 3MB of memory. redislabs (the original author of redis left in 2020)
Snappy DB Android Key-value pairs / NoSQL db No No   No Apache 2.0 Portable lightweight key-value store, NoSQL database based on LevelDB   Nabil HACHICHA 
SQL Anywhere Android / iOS Relational Dependent No   AES-FIPS cipher encryption for full database or selected tables Proprietary Embedded / portable database with central snyc support with a stationary database   Sybase iAnywhere
SQLite embedded on iOS and Android Relational No No   No, Use SQLCipher to encrypt SQLite Public domain C programming library; probably 90% market share (very personal assumption, 2016) 500KiB Hwaci
SQL Server Compact Android / iOS Relational No No   Yes Proprietary Small-footprint embedded / portable database for Microsoft Windows mobile devices and desktops, supports synchronization with Microsoft SQL Server 2 MB Microsoft
UnQLite Android / iOS Key-value pairs / document store / NoSQL db No No     2-Clause BSD Portable lightweight embedded db; self-contained C library without dependency.   Symisc systems

Side note: According to the database of databases there are more than 700 databases as of 2020. However, that list does include hobby projects. DB-engines “only” lists databases that have significant traction and are well-maintained; they still count more than 300 databases as of 2020.

If you are interested in an indication of the diffusion rate of databases and mobile databases, check out the following database popularity ranking: http://db-engines.com/en/ran.

Thanks for reading and sharing. Please let us know what you’re missing.

What Drives Edge Computing?

What Drives Edge Computing?

Data is exploding in every respect: in data volume, data velocity, and data variety (the 3 v’s). One driver of this phenomenon is the growing number of Mobile and IoT devices and thus, data sources. Making this data useful is one of the driving forces behind the adoption of Edge Computing. New use cases don’t only rely on using this data, but also upon the usability and speed of usability of this ever growing data. There are several practical challenges with this growing data volume that drive the adoption of Edge Computing:

New Use Cases Drive Edge Computing

what-drives-edge-computing

Bandwidth Limitations

The existing network infrastructure cannot support sending all the data to the cloud. Particularly in urban areas there is a concentration of devices and data overburdens existing infrastructure. While 5G promises some relief, it is no hailbringer. First of all, if you want to implement your IoT project now, 5G is not yet available and many questions about 5G remain, e.g. pricing. But moreover, as the number of devices and data is growing ever faster, it is already clear that data volumes will outpace what 5G can support. Edge Computing will be an important technology alongside 5G to enable IoT.

Fast Data Requirements  

Response time requirements are growing at the same time as data volumes are increasing. Sending data to the cloud for computation and storage means applications’ response times have a higher latency and depend on the network, which cannot guarantee response rates. Use cases that need speedy response times or guaranteed responses cannot rely on cloud computing. For example, in driver assistance, where every millisecond counts or in factory floors, where downtimes are too costly.

Sustainability

Sending data to the cloud and storing it there is inefficient and therefore costly – not only in plain €, but with regards to CO2 emissions too. The distance the data needs to travel needs hardware, connectivity and electric power. Therefore, sending data unnecessarily back and forth is wasteful beaviour and burdens the environment unnecessarily. With growing data volumes, that burden is growing. In fact, analysts predict  that cloud computing data centers will consume as much as 21% of the total global energy by 2030. [1]

To scale your prototype, you need to move to the edge

At the start of IoT projects, quick prototyping, testing and piloting on early iterations of an application’s functionalities, can effectively be done in the cloud. However, in productive environments when applications scale it is often hard or impossible to keep cloud costs at scale, making the business not viable. Then it is time to move to the edge.

At the same time, decreasing hardware costs and hardware sizes are enabling more complex local computing, meaning there is less need for additional cloud usage. E.g. increasingly AI and ML is done at the edge, including model training.

data-volume-edge-computing-solution-iot-mobile

Data accessibility and Smart Syncing

Today’s successful businesses require a smarter approach to data management and integration. Data synchronization increases operational efficiencies, saving time and resources by eliminating redundant data transfer. With data synchronization, only predefined, useful parts of a data set are sent to a central instance. This means that while large volumes of data can be collected and analyzed locally, not all of this data is sent to and saved in the cloud. This reduces the impact on bandwidth, utilizes the local hardware resources for fast guaranteed response times, and keeps project cloud costs low – ultimately creating a more sustainable and efficient model of data architecture, enabling long term project scalability. 

About ObjectBox

ObjectBox’ current database technology is enabling companies to persist and use data on edge devices, faster than any alternative on the market. This is a pre-requisite for true Edge Computing. On top, ObjectBox Sync is at a stage that allows developers to synchronize relevant information pieces of data from and to the edge with a central instance (e.g. an on-premise server or the cloud). Building upon this, ObjectBox plans to extend synchronization features to enable distributed synchronization and peer-to-peer synchronization, incorporating conflict resolution strategies. This enables networks of edge devices working without a central instance, enabling even more new use cases.

Objectbox-logo-white

The ObjectBox edge database and data synchronization solution solves
data persistence on the edge with speed, reliability, and ease.

10X Faster than
any Alternative

edge-cloud-white

From Edge
to Cloud

sync-data-synchronization-edge-cloud

Sync Data
Seamlessly

Time Series & Objects: Using Data on the Edge

Time Series & Objects: Using Data on the Edge

Many IoT projects collect, both time series data and other types of data. Typically, this means they will run two databases: A time-series database and a traditional database or key/value store. This creates fracture and overhead, which is why ObjectBox TS brings together the best of both worlds in one database (DB). ObjectBox TS is a hybrid database: an extremely fast object-oriented DB plus a time-series extension, specially optimized for time series data. In combination with its tiny footprint, ObjectBox is a perfect match for IoT applications running on the edge. The out-of-the-box synchronization takes care of synchronizing selected data sets super efficiently and it works offline and online, on-premise, in the cloud.

time-series-data-example-temperature

What is time series data?

There are a lot of different types of data that are used in IoT applications. Time-series is one of the most common data types in analytics, high-frequency inspections, and maintenance applications for IIoT / Industry 4.0 and smart mobility. Time series tracks data points over time, most often taken at equally spaced intervals. Typical data sources are sensor data, events, clicks, temperature – anything that changes over time.

Why use time series data on the edge?

Time-series data sets are usually collected from a lot of sensors, which sample at a high rate – which means that a lot of data is being collected.

For example, if a Raspberry Pi gateway collects 20 data points/second, typically that would mean 1200 entries a minute measuring e.g. 32 degrees. As temperatures rarely change significantly in short time frames, does all of this data need to go to the cloud? Unless you need to know the exact temperature in a central location every millisecond, the answer is no. Sending all data to the cloud is a waste of resources, causing high cloud costs without providing immediate, real-time insights.

time-series-objects-edge

The Best of Both Worlds: time series + object oriented data persistence

With ObjectBox you aren’t limited to only using time series data. ObjectBox TS is optimized for time series data, but ObjectBox is a robust object oriented database solution that can store any data type. With ObjectBox, model your world in objects and combine this with the power of time-series data to identify patterns in your data, on the device, in real time. By combining time series data with more complex data types, ObjectBox empowers new use cases on the edge based on a fast and easy all-in-one data persistence solution. 

Bring together different data streams for a fusion of data; mix and match sensor data with the ObjectBox time series dashboard and find patterns in your data. On top, ObjectBox takes care of synchronizing selected data between devices (cloud / on-premise) efficiently for you.

time-series-data-visualization-dashboard

Get a complete picture of your data in one place

Use Case: Automotive (Process Optimization)

Most manufacturers, whether they’re producing cars, the food industry, or utilities, have already been optimizing production for a long period of time. However, there are still many cases and reasons why costly manual processes prevail.  One such example is automotive varnish. In some cases, while the inspection is automatic and intelligent, a lot of cars need to be touched up by hand, because the factors leading to the errors in the paint are not yet discovered. While there is a lot of internal expert know-how available from the factory workers, their gut feel is typically not enough to adapt production processes.

How can this be improved using time series and object data? 

The cars (objects) are typically already persisted including all the mass customization and model information. If now, all data, including sensor data, of the manufacturing site like temperature, humidity, spray speed (all time-series data) is persisted and added to each car object, any kind of correlations between production site variables, individual car properties and varnish quality can be detected. Over time, patterns will emerge. The gut feel of the factory workers would provide a great starting point for analyzing the data to discover Quick Wins before longterm patterns can be detected. Over time, AI and automatic learning kicks in to optimize the factory setup best possible to reduce the need for paint touch ups as much as possible. 

Use Case: Smart Grids

Utility grid loads shift continually throughout the day, effecting grid efficiency, pricing, and energy delivery. Using Smart Grids, utilities companies can increase efficiency and reliability in real time. In order to get insights from Smart Grids, companies need to collect a large volume of data from existing systems. A huge portion of this data is time series, e.g. usage and load statistics. On top, they incorporate other forms of data, e.g. asset relationship data, weather conditions, and customer profiles. Using visualization and analytical tools, these data types can be brought together to generate business insights and actionable operative goals.

ObjectBox TS: time series with objects

Storing and processing both time series data and objects on the edge, developers can gather complex data sets and get real time insight, even when offline. Combining these data types gives a fuller understanding and context for data – not only what happens over time, but what other factors could be influencing results. Using a fast hybrid edge database allows developers to save resources, while maintaining speed and efficiency. By synchronizing useful data to the cloud, real time data can be used for both immediate action, and post-event analysis.

Get in touch with our team to get a virtual demo of ObjectBox TS, or check out the sample GitHub repo to see more about the code.

What is Edge Computing?

What is Edge Computing?

Today, over 90 percent of enterprise data is sent to the cloud. In the next years, this number will drop to just 25 percent according to Gartner. The rest of the data is not going anywhere. It is being stored and used locally, on the device it was created on – e.g. cars, trains, phones, machines, cameras. This is Edge Computing – and since the Corona outbreak it is more relevant than ever.

Obviously, this is cutting the discussion short. With edge consortia springing up like mushrooms, there is no lack of overlapping definitions around the terms Edge Computing and Fog Computing.

what is edge computing

Benefits of Edge Computing put simply

The benefits of edge computing stem from its underlying paradigm: Edge Computing is a decentralized computing architecture as opposed to a centralized computing model (today typically cloud computing).

  • Data ownership / privacy: With Edge Computing data can stay where it is produced, used and where it belongs (with the user / on the edge devices)
  • Networking costs / Cloud costs: Reducing data transferrals and central storage reduces networking and cloud costs significantly
  • Bandwidth constrains: Bandwidth is limited and the data volumes are growing much faster than the bandwidth can be expanded (e.g. with 5G networks); it therefore puts a hard stop on many applications that can be overcome by building on the edge
  • Application / Data speed: Processing on the device – instead of sending data to the cloud and waiting for an answer – is way faster (latency)
  • Offline-capability: With Edge Computing, devices operate independent from a network / cloud connection, so the application always works and data parts that are needed centrally can be synced when convenient, needed, connected
  • The decentralized edge: Edge devices can communicate between each other directly. This decentralized Edge Computing approach more efficient (usually translating to speed) due to the short distances and because the power and information of several devices can be combined (for more info see: ultra low latency networks, peer-2-peer, M2M actions). On top, it adds resilience.
  • Security: A central instance with millions of data is more attractive to hack; also the data transferral adds an additional vulnaribility.

From mist to fog to edge to cloud – a short overview

To bring some light into the terminology mess: The terms “mist computing” and “cloud computing” constitute the ends of a continuum. In our definition, the edge covers everything from cloud to any end device, however tiny and limited it may be. In this definition, there really is only the cloud and the edge.

However, some authors additionally use the terms fog computing and mist computing.

Mist covers the computing area that takes place on really tiny, distributed, and outspread devices, e.g. humidity or temperature sensors. To make it a bit more tangible: These devices generally are too small to run an operating system locally. They just generate data and send that data to the network.

As opposed to mist computing, the cloud refers to huge centralized data centers. The terms “fog” and “edge” fall within this continuum and – depending on whose definition you follow – can be used interchangeably.

what is edge computing

From edge to cloud and back: History repeating itself

If these terms seem familiar to you, that is probably because edge computing is just another cycle in a series of computing developments.

Computing has seen constant turns between centralized and distributed computing over the decades, and with recent developments in hardware capacity, we’re again entering into a decentralized cycle.

edge vs cloud

Edge Computing has been around for 20 years, see a quick history here:

Cloud or edge? – one to rule them all?

Neither the cloud nor the edge is a solution for all cases. As always: It depends. There are cases, where the edge makes more sense than the cloud and vice versa. Most cases however, do need both. If you can, putting the bulk of your computational workload on the edge does make sense though from an economic as well as environmental perspective.

 Interested in learning more? Read why Android developers should care about Edge Computing or discover Edge IoT use cases.

A last word on “edge consortia”

There is no lack of consortia defining terms around edge computing – it’s a lot like the Judean People’s Front against the People’s Front of Judea. After a year of battle, the most prominent edge consortium emerging currently seem to be EdgeX under the umbrella of the Linux Foundation – fully open source, while also largely supported and driven by Dell, who initiated it. Other notable players trying to get a foothold in this space is the Deutsche Telekom with MobiledgeX and HPE with Edge Worx. A European counterpart, ECCE, formed in spring 2019 and might be worthwhile watching, as it is supported by many industry players like e.g. KUKA, Intel, and Huawei.

Why Edge Computing is More Relevant in 2020 Than Ever

Why Edge Computing is More Relevant in 2020 Than Ever

The world has recently been forced to digitize – both more quickly and to a greater extent; coronavirus has created the need to remodel how work, socializing, production, entertainment, and supply chains function. Despite decades of digitization efforts, with the pandemic upon us, digitization challenges have become transparent. Many companies and countries realize now, they have fallen behind. And those that have not yet digitized are hit hardest by the pandemic. [1] With people leaning heavily on online digital solutions, internet infrastructure is at its capacity limit. [2] Accordingly, users are seeing broadband speeds drop by as much as half. [3] In Europe, governments even requested to reduce the quality of Netflix, Amazon Prime, Youtube and other streaming services to improve network speeds. [4]

These challenges bring to light the growing need for an alternative to cloud computing. Cloud computing is an inherently centralized computing paradigm. Edge Computing helps overcome many of the disadvantages of centralized computing. Edge Computing is inherently decentralized and keeps data local, at the ‘edge’ of the network. Edge Computing is ideal for both, data-intensive content and latency-sensitive applications. Edge Computing makes efficient use of local data and reduces the amount of traffic in the network.

edge computing 2020

Coronavirus accelerates the need to digitize

It was clear even before the outbreak that internet infrastructure was struggling to keep up with growing data volumes. However, the pandemic has made broadband limitations more apparent to everyday users.

Projections estimate that by 2025 there will be 20 million IoT devices [5] and 1.7MB of data created per second per person. It is slow, expensive, and wasteful to send all of this data to the cloud for storage and processing. This practice overburdens bandwidth and data center infrastructure. It makes projects expensive and unsustainable. Working with the data, locally, on the edge, where it was produced and is used, is more efficient than sending everything to the cloud and back. It brings reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data and heightened data privacy – and even reduces CO2. Indeed, prior to the pandemic, edge computing was on the strategic roadmap for over 50% of mobility decision makers. [6]

As the world begins to recover from the coronavirus pandemic, digitization efforts will no doubt increase. We will see intelligent systems implemented across industries and value chains, accelerating innovation and alongside: data volumes and subsequent strain on network bandwidth. Edge computing is a key technology to ensure that this digitalization is both scalable and sustainable.  

Edge Computing takes the ‘edge’ off bandwidth strain

what is edge computing?

What is Edge Computing

Today, over 90% of enterprise data is sent to the cloud to be stored and processed. By 2025, this number will drop to just 25%. [7] The remaining data is stored and used on the device it was created on. This is called edge computing. It entails that data is stored and used locally, on the “edge” of the network, e.g. a smart phone or IoT device. Edge computing delivers faster decision making, local and offline data processing, as well as reduced data transfer to the cloud (e.g. filtered, computed, extra- or interpolated data), which saves both bandwidth and cloud storage costs. 

The Edge complements the Cloud

Although some might set cloud and edge in competition, the reality is that edge computing and cloud computing are both useful and relevant technologies. Both have different strengths and ideal use cases. Together they can provide the best of both worlds: decentralized local storage and processing, making efficient use of hardware on the edge and central storing and processing of some data, enabling additional centralized insights, data backups (redundancy), and remote access. To combine the best of both worlds, relevant and useful data must be synchronized between the edge and cloud in a smart and efficient way.  

Edge computing is an ideal technology to reduce the strain on data centers, so those functions that need cloud connection have adequate bandwidth; while those use cases that benefit from reduced latency and offline functionality are optimized on the edge.

The Edge: interface between the Physical and the Digital World

Edge devices handle the interface between the physical world and the cloud, enabling a whole set of new use cases. “Data-driven experiences are rich, immersive and immediate. But they’re also delay-intolerant data hogs”. [8] And therefore need to happen locally, on the edge. We may see edge computing enabling new forms of remote engagement [9], particularly in a post-corona environment.

Edge devices can be anything from a thermostat or small sensor to a fridge or mobile phone or car – and they are part of our direct physical world and use data from their local environment to enable new use cases. Think self-stocking fridges, self-driving cars, drone-delivered pizzas. In the same way, Edge Computing is the key to the first real world search engine. I am waiting for it every day: “Hey Google, where are my keys?” Within a location like a house, the concepts and technologies to enable such a real-world search engine are all clear and available – it is just a matter of time and ongoing digitization. The basis will need to be a fast and sustainable edge infrastructure. 

Sustainability on the Edge

Centralized data centers consume a lot of energy, produce a lot of carbon emissions and cause significant electronic waste. [10] While data centers are seeing a positive trend towards using green data centers, an even more sustainable approach is to cut unnecessary cloud traffic, central computation and storage as much as possible by shifting computation to the edge. Edge Computing strategies that harness the power of already deployed available hardware (like e.g. smartphones, machines, desktops, gateways) make the solution even more sustainable.

sustainability on the edge

Intelligent Edge: AI and Edge advance hand in hand

The growth of Artificial Intelligence (AI) and the Edge will go hand in hand. As more and more data is generated at the edge of the network, there will be a greater demand for intelligent data processing and structured optimization to reduce raw data loads going to the cloud. [11] Edge AI will have the power to work with data on local devices, keeping data streams more useful and usable. In the near future, Machine Learning applications will have the ability to learn and create unique, localized, decentralized insights on the edge – based on local inputs.

“With Edge AI, personalization features that we want from the app can be achieved on device. Transferring data over networks and into cloud-based servers allows for latency. At each endpoint, there are security risks involved in the data transfer”. [12] Which is part of the reason why the Edge AI Software market is forecasted to reach 1.12 trillion dollars volume by 2023. The development of AI accelerators, which improve model inferencing on the edge, namely from NVIDIA, Intel and Google are helping to make AI on the edge more viable. [13] A fast edge database is a necessary base technology to enable more AI on the edge. 

Edge Computing – an answer to Data Privacy concerns and a need for Resilience

As data collection grows in both breadth and depth, there is a stronger need for data privacy and security. Edge computing is one way to tackle this challenge: keeping data where it is produced, locally, makes data ownership clear and data less likely to be attacked and compromised. If compromised, the data compromised is clearly defined, making notification and subsequent actions manageable. ObjectBox, in its core and as an edge technology, is designed to keep data private, on those devices it was created on, and only share select data as needed. 

The more our private and working lives as well as the larger economy depend on digitalization, the more important it is that systems, underlying computing paradigms as well as networks have strong resilience and security. In computer networking, resilience is the ability to “provide and maintain an acceptable level of service in the face of faults and challenges to normal operation.” [14]

 storing as much data as possible on the  0 01.05.2020Edge Computing shifts computer workloads – the collection, processing, and storage of data – from central locations (like the cloud) to the edge of the networks to many individual devices such as cell phones. Accordingly, any strain is distributed to many devices. Therefore, the risk of a total breakdown is reduced: If one device does not work anymore, the rest is still working. Depending on the setup, the individual devices could even compensate for devices that have a problem.

The same applies to security risks: Even if data from one device is compromised, all other data sets are still safe; the loss is thus very limited and clear.  Overall, as a complement to the cloud, edge computing provides improved strength and security in local networks around the world. These local infrastructures can relieve the pressure on the existing complex dependencies, and in turn make the wider system more resilient and flexible. With Edge Computing crisis response can therefore in all likelihood be faster, better informed, and more effective. [15]

Why Corona-Tracking-Apps need to work on the edge

There has been quite some debate about taking a centralized versus decentralized approach to Corona-Tracking-Apps. [16] Many people are rightly worried about their data. Edge Computing – storing most parts of the data locally, on the user’s device – could be a great way to avoid unnecessary data sharing and keeping data ownership clear. At the same time, data would be by and large much more secure and less likely to be attacked and hacked, as the data to be gained is very reduced. An intelligent syncing mechanism then takes care that the data which needs to be shared, is shared in a selective, transparent and secure way.

UPDATE 01.05.2020: The German government changed its initial decision and will now be using a decentralized approach, storing as much data as possible on the edge, for the Corona-Tracking-App.

The next few years will see big cultural changes in both our personal and professional lives – a portion of those changes will be driven by increased digitalization. Edge computing is an important paradigm to ensure these changes are sustainable, scalable, and secure. Ultimately, we have the chance to rise from this crisis with new insights, new innovation, and a more sustainable future.

1. https://www.netzoekonom.de/2020/04/11/die-oekonomie-nach-corona-digitalisierung-und-automatisierung-in-hoechstgeschwindigkeit/
2. https://www.cnet.com/news/coronavirus-has-made-peak-internet-usage-into-the-new-normal/
3. https://www.nytimes.com/2020/03/26/business/coronavirus-internet-traffic-speed.html
4. https://www.theverge.com/2020/3/27/21195358/streaming-netflix-disney-hbo-now-youtube-twitch-amazon-prime-video-coronavirus-broadband-network
5. https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf
6. https://www.forbes.com/sites/forrester/2019/12/02/predictions-2020-edge-computing-makes-the-leap/#1aba50104201
7. https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/
8. https://www.iotworldtoday.com/2020/03/19/ai-at-the-edge-still-mostly-consumer-not-enterprise-market/
9. https://www.accenture.com/us-en/insights/high-tech/edge-processing-remote-viewership
10. https://link.springer.com/article/10.1007/s12053-019-09833-8
11. https://www.forbes.com/sites/cognitiveworld/2020/04/16/edge-ai-is-the-future-intel-and-udacity-are-teaming-up-to-train-developers/#232c8fab68f2
12. https://www.forbes.com/sites/cognitiveworld/2020/04/16/edge-ai-is-the-future-intel-and-udacity-are-teaming-up-to-train-developers/#232c8fab68f2
13. https://www.forbes.com/sites/janakirammsv/2019/07/15/how-ai-accelerators-are-changing-the-face-of-edge-computing/#2c1304ce674f
14. https://en.wikipedia.org/wiki/Resilience_(network)
15. https://www.coindesk.com/how-edge-computing-can-make-us-more-resilient-in-a-crisis
16. https://venturebeat.com/2020/04/13/what-privacy-preserving-coronavirus-tracing-apps-need-to-succeed/