How NaNLABS Developed a Cloud Native Observability Platform and Optimize Data Processing For An EV Charging Company

The story repeats itself, but this time with electric vehicle (EV) chargers.

Tesla uses proprietary chargers and stations, similar to Apple’s business model. It controls the hardware and software ecosystem, ensuring a positive experience for its users. “This allows Tesla to provide a reliable charging experience for their customers, unlike other EV charging firms that have to deal with interoperability across various car models and charging hardware,” says the Chief Software Engineer at EV Rechargery.

EV Rechargery aims to revolutionize the EV charging experience by launching over 30,000 charging stations across the US by 2030. “We want to create the largest non-Tesla EV charging network in North America,” he shares.

To successfully build this scalable network, EV Rechargery needed real-time visibility into the data generated from thousands of chargers. To make this happen, EV Rechargery’s Chief Software Engineer turned to a trusted data engineering partner for support. This is when NaNLABS came in as their technical sidekick. Here's what we achieved together. 

Table of contents

• EV Rechargery: A US-leading EV charging network funded by top brands

• NaNLABS and EV Rechargery: The meet cute

• EV Rechargery’s roadblocks: Infrastructure, data, and interoperability

• How NaNLABS helped EV Rechargery overcome its challenges

• Preliminary results: Putting the observability platform to the test

• Augment your team and meet your engineering goals fast

• Frequently Asked Questions

EV Rechargery: A US-leading EV charging network backed by top brands

Quick note: For confidentiality reasons, we won’t be revealing our client’s real name. But every superhero needs an alias—so in this story, they go by EV Rechargery.

EV Rechargery is an innovative EV charging network backed by eight major automakers, including some global industry leaders. 

The company goes beyond the traditional role of Charging Point Operators (CPOs) by integrating its charging infrastructure directly with participating automakers’ in-vehicle and in-app experiences.

EV Rechargery has the ambitious goal of launching 30,000 EV charging stations across North America by 2030. This requires scaling efforts that nearly double the industry's current pace. “The most anyone has deployed in a year is around three or four thousand,” confirms the Chief Software Engineer at EV Rechargery. 

Also, to meet EV drivers expectations, EV Rechargery needs to gather data about what happens during each charging session. As the company is still in its early stages, it must maximize its time and budget while developing high-performing cloud-native data engineering solutions. That’s one of the main reasons they turned to NaNLABS.

NaNLABS and EV Rechargery: The meet cute

NaNLABS is a cloud data engineering partner that doesn’t stick to the traditional nearshore experience, especially as an augmented team.

The Chief Software Engineer had previously worked with NaNLABS at a different company so he was certain our team was exactly what EV Rechargery needed. “We didn’t have much time or budget to go through a hiring process and get in-house developers. And, onboarding NaNLABS usually lasts around two sprints,” he shares.

The decision to partner with NaNLABS was also influenced by our team’s hands-on approach and technical expertise. Our focus on transparency and open communication aligned well with the fast-paced, high-stakes nature of the project.

EV Rechargery’s roadblocks: Infrastructure, data, and interoperability

To meet its goals, the EV charging company first needs to overcome certain product-specific complexities, related to:

• Physical infrastructure. Each charging station requires construction and a comfortable design for people to pass the time while the car charges. This is a complex and heavy operation that involves coordinating multiple different parties.

• Car interoperability. “There are 62 different EV models in the US. These have to work with 50 to 60 charger manufacturers and a whole set of cloud-based management systems and applications,” says the Chief Software Engineer. EV Rechargery has the challenging job of making these different parts work together in the same ecosystem.

Additionally, according to Harvard research, one in five public charging stations fail. Imagine driving to the service station to load your tank and leaving without gas because the pump didn’t work. “The challenge for us is for each session to work perfectly,” adds the Chief Software Engineer.

However, EV Rechargery doesn’t necessarily know what the different parties do during these sessions. “This leads to black boxes and we have to do our best to figure out what those are. As the CPO, it’s challenging to see the end-to-end view of a charging session and improve the service,” adds the Chief Software Engineer. This is just one example of how managing data is complex for a company like EV Rechargery, especially due to:

• Data streaming and processing. Each station streams data from the chargers and point-of-sale (POS) systems. The chargers track various data points throughout each session, helping our client assess overall performance. Now, imagine 30,000 stations, each with multiple dispensers and hundreds of customers daily, generating at least seven rows of data per session—it quickly becomes impossible to review manually.

• Data visibility. Without proper data normalization, the raw data would be too heavy for an analyst to review and gain quick insights. This could affect EV Rechargery’s response to issues and make it lose its competitive advantage.

NaNLABS worked alongside EV Rechargery’s in-house team to address data complexities and gain session observability. “We needed to pull the data into one central spot so everyone could see it,” shares the Chief Software Engineer.

How NaNLABS helped EV Rechargery overcome its challenges

To address these data challenges, NaNLABS and EV Rechargery built a scalable observability platform to provide real-time insights into EV charging operations and shed light on those black boxes. This solution gives EV Rechargery's internal team better visibility into the performance of charging stations, enabling them to make more informed business decisions.

In collaboration with our client’s team to build the observability platform, we:

• Designed its data architecture to stream, transform, normalize, store, and process information coming from different IoT devices. 

• Built near-real-time data streaming pipelines, enabling the Charging Point Operator (CPO) to receive alerts and take quick action to prevent or resolve issues. The platform streams data every 15 minutes, though some APIs have internal time restrictions and refresh only once a day. 

• Automated data processing as our client’s team accessed data manually from different systems. “To make analyses, its team had to access different applications and copy and paste the different IDs and data sets into various spreadsheets,” says Dario Scrivano, Full-stack Developer at NaNLABS.

• Improved data visualization by using data coming from lab-generated tests. EV Rechargery’s team was also creating manual charts in Excel. They can now see their KPIs on visual, always-updated, and interactive dashboards. “We embedded these dashboards into the front-end so they [our client’s team] can make edits, add KPIs, or choose a different period without much hassle,” says Matias Pompilio, Full-stack Developer at NaNLABS.

To achieve these results, our team followed the client's specs and technical designs and assessed the viability of using different technologies. In one case, the team had to meet directly with a tool provider to identify best practices and adapt the system to meet the client’s needs. 

Preliminary results: Putting the observability platform to the test

The team developed the observability platform with data coming from lab-generated tests. With four active stations now in beta, our client’ team is stress-testing the platform in real-world conditions. The goal is for this tool to help prevent errors and offer a reliable experience to users. 

At the time of writing, the observability platform has already enabled our client’s team to improve productivity by reducing the number of places they need to access to find data. “Initially when they showed us the data structure, it was all very manual and time-consuming as data was scattered around. Seeing it now on a single source is magnificent,” says Brenda Quispe, Data Engineer at NaNLABS. 

Build Smarter, Faster Cloud-Native Data Solutions with NaNLABS

At NaNLABS we’re used to embedding into other companies' development teams and working alongside them to solve complex challenges. As we did with this EV charging company, we can help you simplify processes, establish a robust data architecture, leverage AI/ML models, or develop responsive applications.

What can you expect? As your tech sidekick, we go beyond building data solutions: we anticipate, adapt, and make things happen, combining our deep cloud-native data expertise with resourcefulness and unwavering commitment to every project.

If you’re facing data engineering challenges and need a team that gets you vision, solves problems before they arise, and helps you move faster, let’s talk!

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Editorial contributions: Camila Mirabal