Alyssa, Author at ObjectBox

ObjectBox Recognized as a Sustainable Profitable Tech Solution by the Solar Impulse Foundation

by Alyssa | Nov 30, 2020 | About us, Insights, IoT

ObjectBox is proud to be officially recognized as a sustainable and efficient solution by the Solar Impulse Foundation. Although we have self-identified as a #sustainabletech company since our induction, we’re proud to be recognized as an “efficient, clean and profitable solutions with a positive impact on environment and quality of life,” after taking part in an in-depth technical and business evaluation with the team at the Solar Impulse Foundation.

Empowering tech innovation

This label recognizes that ObjectBox empowers innovation with a highly efficient and sustainable technology. The Solar Impulse Efficient Label identifies sustainable tech solutions from around the world to help companies choose their tech stack responsibly.

solar-impulse-foundation-label-sustainable-software-for-the-edge

UN Sustainable Development Goals

All Solar Impulse awardees contribute to one or several of the UN Sustainable Development Goals; ObjectBox received the globally recognized label for supporting three of the Solar Impulse focused initiatives:

Affordable and Clean Energy: ObjectBox
Clean Water and Sanitation
Industry, Innovation and Infrastructure : ObjectBox
Sustainable Cities and Communities: ObjectBox
Responsible Consumption and Production

How is ObjectBox sustainable?

ObjectBox enables scalable and sustainable digitalization with a high performance edge database solution and synchronization solution. The ObjectBox database empowers local data storage, while ObjectBox Sync reduces unnecessary data traffic. ObjectBox is therefore ideally suited for efficient, useful, and sustainable Edge Computing.

Comparing the transmission of the same data sets, ObjectBox saves 20-60% on transmission data volume. By combining delta syncing with efficient compression based on standard and proprietary edge compression methods to keep data small, ObjectBox can reduce device energy consumption and thus CO2 emissions for data transmissions.

As our digital world grows, we all need to do what we can to structure these digital environments in an efficient and sustainable way. ObjectBox helps reduce digital waste. Digital waste unnecessarily burdens bandwidth infrastructure and fills cloud servers, forcing the expansion of cloud farms and in turn, contributing to the pollution of the environment. Therefore, we are excited to be part of the 1000solutions program.

Dr. Vivien Dollinger

CEO and Co-founder, ObjectBox

What does it mean to get a Solar Impulse Label?

The Solar Impulse Label: a label focused on both the environment and profitability

The first label to assess the economic profitability of products or processes that protect the environment. The Solar Impulse Efficient Solution Label is attributed following a strict selection process performed by external independent experts. By ensuring high standards of sustainability and profitability, this internationally recognized label is considered as a credible marker of quality for solution seekers in business and governments, facilitating their sourcing of solutions to reach environmental commitments.

About the Solar Impulse Foundation

The Solar Impulse Foundation aims to identify clean, efficient and profitable solutions in order to accelerate their implementation and the transition to a sustainable economy. Thanks to the awarding of a label with high standards of sustainability and profitability, the Foundation can support political and economic decision-makers in their efforts to achieve their environmental targets and encourage them to adopt more ambitious energy regulations, necessary for implementation at large-scale of these solutions on the market. A way to take the success of the first round-the-world solar flight further.

Interesting in finding out how ObjectBox can make your edge computing project more sustainable?

Get in Touch with our Team

What are Time Series Database Use Cases?

by Alyssa | Oct 20, 2020 | Edge Computing, Insights, IoT, Time Series

What do self-driving cars, smart homes, autonomous stock/crypto trading algorithms, or energy sensor systems have in common? These applications are all based on a form of data that measures how things change over time. It’s called time-series data and it plays a very important role in our lives today.

Accordingly, time-series databases also became a hot topic.

What is a time-series database?

A time-series database (TSDB) can be defined simply as a database optimized for storing and using time-stamped or time-series data. You don’t need to use a TSDB to work with time-series data. Any relational or NoSQL database or a key-value-store will do, e.g. MongoDB or redis. However, when dealing with time-series data (e.g. temperature, air pressure or car velocity data), a TSDB makes your life as a developer a hell of a lot easier.

Indeed, the two main reasons why TSDBs is the fastest-growing category of databases, are usability and scalability. A purpose-built time-series database typically includes common functions of time-series data analysis, which is convenient when working with time-series data. Because time-series data typically continually produces new data entries, data grows pretty quickly, and with high-frequency data or many time-series data sources, data ingestion quickly becomes a challenge. Time-series databases are optimized to scale well for time-series data with time being a common denominator and outperform any other database without specific time-series optimizations. This is why more and more people are adopting time-series databases and using them for a variety of use cases.

What are time-series database use cases?

Monitoring sensor data

One of the use cases is the monitoring of sensor data for safety measurements, predictive maintenance, or assistance functions. E.g. a car stores and uses all kinds of sensor data like tyre pressure, surrounding temperature and humidity for driver assistance and maintenance support. An aircraft monitors gravity and aerodynamic principles to reassure pilots that everything is alright – or to alert them that something has gone wrong. In fact, a Boeing creates on average half a terabyte of data per flight, most of which is time-series data. [1]

Tracking assets

Tracking assets is ideal for a time-series database as you constantly want to monitor where assets are, e.g. the cars of a fleet or any goods you might be stocking or shipping. These applications typically include unique vehicle or asset IDs, GPS coordinates, and additional metadata per timestamp. Apart from keeping track of the assets in realtime, you also can use the data for logistics and optimize e.g. your stocking and delivery processes.

Analyzing and predicting shopping behavior

Or, many e-commerce systems store all information of an item from product inventory, logistics data and any available environmental data to transaction amount, all items of the shopping cart purchased, to payment data, order information etc. In this case, a TSDB will be used to collect these large amounts of data and analyze them quickly to determine e.g. what to recommend to customers to buy next or optimize the inventory or predict future shopping behavior.

What are the most popular time series databases?

Well, here is our list of popular / established time series databases to use in 2020 to get you started:

InfluxDB: an open-source time series database, written in Go and optimized for high-availability storage and retrieval of time series data for operations monitoring, application metrics, IoT sensor data, and real-time analytics
KairosDB: a fast distributed scalable time series database written on top of Cassandra.
Kdb+: is a column-based relational time series database with a focus on applications in the financial sector.
Objectbox TS: superfast object persistence with time-series data on the edge. Collect, store, and query time-series data on the edge and sync selective data to / from a central location on-premise or in the cloud as needed.
TimescaleDB: an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL and packaged as a PostgreSQL extension with full SQL support.

For an overview of time-series databases currently available for productive use, see DB Engines. The database of databases is also a good resource if you are deeply interested in the database landscape; it is more extensive, but it includes any DB available independent of the level of support or if it is still maintained, also hobby projects.

What do you do when you have more than just time-series data?

Typically, a time-series database is not well suited to model non-time-based data. Therefore, many companies choose to implement two databases. This increases overhead, disk space, and is especially impractical when you deal with edge devices.

Time Series + Object-Oriented Data Persistence

Storing and processing both time series data and objects, developers can collect complex datasets and combine them with time-series data. Combining these data types gives a more complete understanding and context to the data – not just what happens over time, but also other factors that affect the results.

The best option is a robust object-oriented database solution that lets you model your data as it reflects the factual use case / the real world in objects and on-top is optimized for time series data. You can model your world in objects and combine this with the power of time-series data to identify patterns in your data. If this is indeed a database optimized for restricted devices and Edge Computing, you can even use this data in real-time and on the device. By combining time series data with more complex data types, an object time-series edge database can empower new use cases on the edge based on a fast and easy all-in-one data persistence solution.

Still have questions? Feel free to contact us here!

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[1] Time Series Management Systems: A Survey Søren Kejser Jensen, Torben Bach Pedersen, Senior Member, IEEE, Christian Thomsen

How Building Green IoT Solutions on the Edge Can Help Save Energy and CO2

by Alyssa | Jul 23, 2020 | Edge Computing, Insights, IoT, Sync

The internet of things (IoT) has a huge potential to reduce carbon emissions, as it enables new ways of operating, living, and working [1] that are more efficient and sustainable. However, IoT’s huge and growing electricity demands are a challenge. This demand is due primarily to the transmission and storage of data in cloud data centers. [2] While data center efficiency and the use of green energy will reduce the CO2 emissions needed for this practice, it is not addressing the problem directly. [3]

With ObjectBox, we address this unseen and fast-growing CO2 source at the root: ObjectBox empowers edge computing, reducing the volume of data transmitted to central data storage, while at the same time, heightening data transmission and storage efficiency. [4] We’ve talked before about how edge computing is necessary for a sustainable future, below we dive into the numbers a bit deeper. TLRD: ObjectBox enables companies to cut the power consumption of their IoT applications, and thus their emissions, by 50 – 90%. For 2025, the potential impact of ObjectBox is a carbon emission reduction of 594 million metric tons (see calculations below).

How ObjectBox’ Technology Reduces Overall Data Transmission

ObjectBox reduces data transmission in two ways: 1. ObjectBox reduces the need for data transmission, 2. ObjectBox makes data transmission more efficient. ObjectBox’ database solution allows companies to build products that store and process data on edge devices and work with that data offline (as well as online). This

not only improves performance and customer experience, it also reduces the overall volume of data that is being sent to the cloud, and thus the energy needed to transfer the data as well as store it in the cloud. ObjectBox’ Synchronization solution makes it easy for companies to transmit only the data that needs to be transmitted through 1) selective two-way syncing and 2) differential delta syncing. Synchronizing select data reduces the energy required for unnecessarily transmitting all data to the cloud.

We have demonstrated in exemplary case studies that ObjectBox can reduce total data transmissions by 70-90%, depending on the case. There will, however, typically be value in transmitting some parts of data to a central data center (cloud); ObjectBox Sync combines efficient compression based on standard and proprietary edge compression methods to keep this data small. ObjectBox also has very little overhead. Comparing the transmission of the same data sets, ObjectBox saves 40-60% on transmission data volume through the delta syncing and compression, and thus saves equivalent CO2 emissions for data transmissions. Additional studies support these results, and have shown that moving from a centralized to a distributed data structure, saves between 32 and 93% of transmission data. [5]

Calculations: How Does ObjectBox Save CO2?

Physically using a device consumes little energy directly; it is the wireless cloud infrastructure in the backend (data center storage and data transmission) that is responsible for the high carbon footprint of mobile phones [6] and IoT devices. Estimates say that IoT devices will produce around 2,8 ZB of data in 2020 (or 2,823,000,000,000 GB), globally. [7] Only a small portion of that data actually gets stored and used; we chose to use a conservative estimate of 5% [8] (141,150,000,000 GB) and of that portion, 90% is transferred to the cloud [9] (127,035,000,000 GB). Transferring 1 GB of data to the cloud and storing it there costs between 3 and 7 kWh. [10] Assuming an average of 5 kWh this means a 127,035,000,000 GB multiplied by 5kWh, resulting in a total energy expenditure of 635,175,000,000 kWh. Depending on the energy generation used, CO2 emissions vary. We are using a global average of 0,475 kgCO2 / 1 kwH. [11] In total this means that there will be 301,708,125,000 KG of CO2, or roughly 301 million metric tons of CO2 produced to transfer data to the cloud and store it there in 2020.

Projections for 2025 have data volumes as high as 79.4 ZB. [12] Following the same calculations as above, IoT devices would be responsible for 8 billion metric tons of CO2 in 2025.* We estimate that using ObjectBox can cut CO2 caused by data transmission and data centers by 50-90%, by keeping the majority of data on the device, and transmitting data efficiently. It will take time for ObjectBox to enter the market, so assuming a 10% market saturation by 2025 and an average energy reduction of 70%, using ObjectBox could cut projected CO2 emissions by 594 million metric tons in 2025.

ObjectBox is on a mission to reduce digital waste which unnecessarily burdens bandwidth infrastructure and fills cloud servers, forcing the expansion of cloud farms and in turn, contributing to the pollution of the environment. As our digital world grows, we all need to give some thought to how we should structure our digital environments to optimize and support useful, beneficial solutions, while also keeping them efficient and sustainable.

*Of course, in that time, the technologies will all be more efficient and thus use less electricity while at the same time CO2 emissions / kWh will have dropped too. Thus, we are aware that this projection is an oversimplification of a highly complex and constantly changing system.

[1] https://www.theclimategroup.org/sites/default/files/archive/files/Smart2020Report.pdf
[2] https://www.iea.org/reports/tracking-buildings/data-centres-and-data-transmission-networks
[3]“Data centres… have eaten into any progress we made to achieving Ireland’s 40% carbon emissions reduction target.” from https://www.climatechangenews.com/2017/12/11/tsunami-data-consume-one-fifth-global-electricity-2025/
[4] https://medium.com/stanford-magazine/carbon-and-the-cloud-d6f481b79dfe
[5] https://www.researchgate.net/publication/323867714_The_carbon_footprint_of_distributed_cloud_storage
[6] https://www.resilience.org/stories/2020-01-07/the-invisible-and-growing-ecological-footprint-of-digital-technology/
[7] https://www.idc.com/getdoc.jsp?containerId=prUS45213219, https://priceonomics.com/the-iot-data-explosion-how-big-is-the-iot-data/, https://www.gartner.com/en/newsroom/press-releases/2018-11-07-gartner-identifies-top-10-strategic-iot-technologies-and-trends, https://www.iotjournaal.nl/wp-content/uploads/2017/02/white-paper-c11-738085.pdf, ObjectBox research
[8] Forrester (https://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/Preventing-IoT-data-waste-with-the-intelligent-edge), Harvard BR (https://hbr.org/2017/05/whats-your-data-strategy), IBM (http://www.redbooks.ibm.com/redbooks/pdfs/sg248435.pdf), McKinsey (https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world)
[9] https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/
[10] According to the American Council for an Energy-Efficient Economy: 5,12 kWh of electricity / GB of transferred data. According to a Carnegie Mellon University study: 7 kWh / GB. The American Council for an Energy-Efficient Economy concluded: 3.1 kWh / GB.
[11] https://www.iea.org/reports/global-energy-co2-status-report-2019/emissions
[12] https://www.idc.com/getdoc.jsp?containerId=prUS45213219

How EV Charging Benefits from Edge Computing

by Alyssa | Jan 28, 2020 | Case Study, Edge Computing, Insights, IoT

Edge computing allows data to be stored and used on local devices. Integrating Edge Computing directly within electric vehicle charging infrastructure improves station usability and also allows for real-time energy management.

Car charging and electric vehicles

The era of electric vehicles (EV) is coming: Already one in every 250¹ cars on the road is electric. While it is uncertain when electric vehicles will overtake traditional combustion engine vehicles, electric is clearly the future. Car charging infrastructure is critical for electric vehicle expansion – and one of the largest bottlenecks to EV adoption. Range anxiety is still one of the primary concerns for potential EV customers,² and charging station proliferation is still far behind traditional gas stations.

State of the electric vehicle charging Market

The electric vehicle charging infrastructure market is still very divided, with many players vying for this large-growth sector – some predictions forecast over 40% CAGR for the car charging infrastructure market in the coming years.³ Car manufacturers, gas & oil, OEMs, and utilities companies (e.g. Tesla, VW, BMW, Shell, GE, Engie, Siemens, ABB) are actively taking part in the development of the market, recognizing the need to support future EV customers and the huge growth potential. Startups in the space like EcoG, Wirelane, flexEcharge and Elli offer solutions that focus on accessibility, efficiency and improving end user experiences.

Why Car Charging Stations need Offline Capability (Edge Computing)

First, let’s look at the challenges a vehicle charging provider needs to solve from a basic data perspective: Customers interfacing with charging stations require an account linked with basic information and payment methods. In order to charge a car, the user needs to be verified by the charging station, and is often required to have a pre-booked charging slot. Typically, a user would create a new account via a website or mobile phone beforehand, but not on the spot at the car charging station. Also booking slots are handled via a mobile app or website. However, the car charging station needs this information to allow a car to be charged.

This is only the most basic necessity. In the future, charging stations will provide more services to users, e.g. identifying users preference like cost over speed of charging, or choosing to charge with green energy.

Depending on where the car charging station sits, it can be offline more or less often, e.g. in France there are quite many electric car charging stations in the country site, where the connection is typically flaky – and might not be available for days. On the other hand, there are stations that reside within a parking house or hotel and use a fixed land line for connectivity. In the latter case, your uptime can be very consistent, but typically you cannot guarantee the car charging station will be connected.

If the charging station tries to access this data only when it needs it, because a car is trying to charge, it may or may not have an internet connection at the time and thus the likelihood of failure is rather high. Accordingly, any new information should be pushed to the car charging stations when a connection is available and stored on the station. The hardware of a car charging station is capable enough to hold a lightweight database and persist data as is needed and useful.

Choosing a data persistence layer (database) over a simple caching ensures not only that no data is lost, but can also allow more processing to happen on the station and allows for autonomous reactions. In combination with edge synchronization, which enables persistence layers to synchronize between car charging stations (that share a data space), fast data persistence allows for efficient load balancing as well as easily updating the configurations of all car charging stations.

Smart Energy Load Management – the need for fast response on the Edge

Managing energy is one of the greatest challenges for EV infrastructure providers. The difficulty here is less about overall energy consumption increasing – rather managing, predicting and preparing for high-demand peaks. Imagine everyone needs charging during a large public event, or at charging stations during holiday travel times – peak demands like these need to be anticipated and planned for. The future with electric cars needs to balance demand with a combination of smart chargers, efficient energy grid management, Vehicle-to-Grid (V2G) solutions, and perhaps even on-site batteries at larger charging stations to improve time-to-charge and optimize for electricity prices.

Edge computing will play an important role in providing real-time, accurate energy load control, necessary for maintaining grid stability, particularly in emergency situations.⁴ At charging stations where many EVs plug in, smart edge nodes can balance charge schedules in real-time, optimizing based on EV owner requirements without overloading local transformers.⁵ On a larger scale, smart energy meters can use real-time edge computing to shift energy quickly to high-demand locations, cutting energy from low-priority appliances, limiting charge speeds, or pulling excess energy from V2G networks.

Thinking about energy management, the conversation fluidly moves from EV charging infrastructure to thinking about smart mobility, utilities, and smart city infrastructure on a larger scale. Car charging systems will be complex, interconnected and will progress in alignment with other ongoing digitization efforts to create data drive infrastructure across cities and the world. Edge computing, and base technologies like ObjectBox that enable working on the edge, are important enablers to ensure that real-time computing can happen anywhere and digitization is affordable, scalable, and sustainable.

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¹ https://qz.com/1620614/electric-car-forecasts-are-all-over-the-map/
² https://airqualitynews.com/2019/08/27/range-anxiety-still-biggest-ev-turn-off-survey-finds/
³ https://www.mordorintelligence.com/industry-reports/electric-vehicles-charging-stations-market
⁴ https://www.geirina.net/paper/IoT%20and%20Edge%20Computing%20Based%20Direct%20Load%20Control%20for%20Fast%20Adaptive%20Frequency%20Regulation.pdf
⁵ https://projects.iq.harvard.edu/files/energyconsortium/files/rwp18-026_lee_1.pdf

MoodSpace Mobile App Use Case

by Alyssa | Jan 20, 2020 | Android, Case Study, Insights, ObjectBox

Ian Alexander

Co-founder, MoodSpace

We speak with Ian Alexander, founder and lead developer at MoodSpace, a beautiful app making mental health exercises accessible to everyone. MoodSpace was released in 2019, and has over 150k+ downloads. The COVID-crises highlights the importance of digital support for wellbeing and saw MoodSpace surge. After trying several databases, Ian settled on ObjectBox because of its high performance and ease of use.

Alyssa: Hi Ian, thank you so much for joining me and for using ObjectBox. Let’s start with the basics about MoodSpace and your role there.

Ian: Hi, Alyssa, thanks for having me. I’m the software developer, founder, and runner of the company – a jack of all trades. MoodSpace is an app that teaches concepts from mental health. There is a massive problem with accessibility to mental health. In the UK, for example, you have something like 1 in 4 people that have some sort of mental health problem, but only 1 in 113 go through therapy and complete it. So our essential goal is to take concepts from therapy and bring them closer to people, teaching them techniques that they can do on an ongoing basis. There’s no end date like in therapy, no waiting list, and it’s a lot easier to use it in places where you wouldn’t necessarily have access to a therapist. In the western world it is much easier to access therapy, still difficult in some ways, but much of the world doesn’t have that benefit. So that’s the goal we’re trying to reach. We started last year, we released the MoodSpace MVP in September, and now we’re going through the next stage of trying to raise our next round of funding – it’s quite exciting.

A: That’s great, congratulations! Can you tell me a bit more about your team?

I: We’re based in the UK, and in terms of the technical side, it’s just me. We also have various other roles: designer, copywriter, and another co-founder who handles much of the business side. But in terms of technical, it is just me for now. Hopefully after we get funding, we’ll be able to expand the technical team..

A: What’s your background, what did you do before MoodSpace?

I: Actually, I was originally a chemical engineer – I worked in oil & gas for a couple of years, but then I taught myself to develop and for the last 5-6 years, I’ve worked across a lot of startups, for example the dating app Once, when they were just starting up, also ITV, and then started on MoodSpace last year. Moodspace has actually existed for quite some time, it started as a hobby project of mine about 5 years ago.

A: There are other mental health apps out there, what makes MoodSpace special?

I: If you look at apps in the space, they’re generally fairly small and limited – they’ll have maybe 4 or 5 exercises. Versus the realm of therapy, which has literally thousands of exercises. Any app that exists at the moment takes just a fraction of a percent of therapy and tries to teach it. Our USP is that we are a very technically minded team, and with new technologies which have come out along with our internal processes, we can make a much bigger app, building something far bigger than what currently exists, much cheaper and far faster. Our USP is strangely, less about the app, and more about our process and the technology that we use to make the app. The tech we will be using is Kotlin Multiplatform, which is a cross platform framework which lets us maintain a single codebase which enables us to build fully native apps with full access to native APIs.

A: It sounds like the app is quite comprehensive – who is your target audience?

I: At the moment, we haven’t hit the product-market-fit stage. We’re still figuring out who the typical user is. We find that the unique art style of our app has helped our growth so far, we often find a lot of people sharing screenshots of the app on social media. So we seem to have hit a niche, but we’re still figuring out what that niche is!

A: Do you have any direct interaction with your users?

I: Mid-last year, I put a survey in the app, so after using it for a certain time users get the survey. There are some questions like who you are, why you are using it, and they gave us way more knowledge about who is using it and what they use it for, which was very helpful. Apart from that though, it is very difficult to know.

A: Yes, it can be very challenging, we’re familiar with that struggle at ObjectBox as well. Switching gears a bit – I’d love to hear a bit more about how and why you ObjectBox.

I: As I mentioned, MoodSpace is about 5 years old, so it’s gone through several databases. One of them was really time consuming to make – it wasn’t ORM based, so you had to write a lot of stuff yourself. Then the next was an ORM called Sugar, but it stopped being developed – it was a side project by someone, so maybe I shouldn’t have used it in the first place (laughs).

I: So then we switched to ObjectBox, and actually the reason we switched was essentially to skip asynchronous code – I’ve always been a frontend developer and what I’ve come across is that asynchronous code makes things very complex, and it means app development takes much longer. Because we had a lot of time constraints and we wanted to develop as much as quickly as possible, I actually wanted to completely skip asynchronous code – which I wouldn’t recommend – but essentially ObjectBox let us do that because it’s very fast. You’d have to have a ton of data in the app, before it would visibly slow it down – and I did a lot of testing around that and it would have needed several years of data before noticeably slowing down the app. So, that was our original reason, perhaps a bit of a strange reason. And we’ve since changed the app so it’s asynchronous, so it won’t slow down any longer, no matter how much data you add in the app. Overall, I like ObjectBox a lot – it’s just simple, very easy to use.

A: What features in your app use the database?

I: Actually everything is in the app, as we don’t have a backend. So we need it to store all the data in the app.

A: Okay, sure. Keeping everything in the app is also practical from a data privacy standpoint. How did you actually find ObjectBox?

I: It was someone I used to work with at Once – they used greenDAO and mentioned that ObjectBox (by the same people) was coming out. I looked into it a little bit and wanted to use it for a while, but it wasn’t I started developing MoodSpace again that I had a chance to.

ObjectBox is very fast, it would have needed several years of data before noticeably slowing down the app. Overall, I like ObjectBox a lot – it’s just simple, very easy to use.

A: Are there any other developer tools that you’re excited about and would want to share with the community?

I: Yes, Kotlin Multiplatform. Having been an Android developer, having used Kotlin for quite some time and having tried cross-platform tools before, I think Kotlin Multiplatform will change the way you make cross platform apps, because it lets you share so much of the code base without sacrificing the native experience. It has the potential of leading to massive cost savings in app development. Maybe in the next year or two I can see it having a huge impact on frontend development across mobile, web, and desktop.

A: What are your big picture goals for MoodSpace? Upcoming milestones? Does ObjectBox help with those at all?

I: Actually, it potentially will, with regards to ObjectBox Sync, which is part of my plan for that app. The app right now is only available on Android, and providing we get our next round of funding, we are going to be adding iOS – where we’ll need some sort of backend. We want to avoid, again, spending much money, and one of ObjectBox Sync, Realm Cloud or Firestore can help us do that – obviously as ObjectBox Sync is nearly ready, we’d want to use that. The main point around that is cost saving because it solves a lot of problems that otherwise we would have to solve ourselves – things like offline access and syncing with an API, that would otherwise be very time intensive.

A: Ian, thank you for your time and sharing more about MoodSpace and working with ObjectBox. We wish you the best of luck with your fundraising round!

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