The Potential of Using Pulsar for IoT Analytics in the Automotive Industry

When it comes to IoT analytics, many people see it as the plain importation of sensor data within data lakes through which predictive analytical models can be created. However, even though this process is important, it’s not the only one that matters. 

The analytics must aggregate, process, and collect a big stream of data coming from sensors in real-time. Within the automotive industry, this technology is used to make real-time decisions within the vehicle to optimize performance, prevent issues, or adjust the passenger experience. 

However, creating this kind of network isn’t as easy as it sounds. It requires a lot of computation power and money. Let’s take a look at an example of Tesla and its lumber support system. 

Tesla Model 3 and Y lumbar support system 

Tesla is known for implementing different IoT devices within its vehicles. In fact, it’s one of the leading companies in terms of technology because it isn’t afraid to experiment with innovative solutions. 

One of those useful gadgets is the lumbar support system located within the front and back seats in Model 3 and Y. It was designed to help everyone in the vehicle feel more comfortable by making seat adjustments. 

The button for this adjustment was located on the side of the seats, but lots of owners didn’t even know they had this feature. The CEO of Tesla, Elon Musk, said that due to a lack of awareness and the expensiveness of this system, the company decided to remove lumbar support on front seats. 

It seems reasonable, especially because most people have never used this feature. However, the question is whether Tesla could have done a better job of implementing the feature and using automated IoT combined with machine learning to provide the state-of-the-art technology the company is known for. 

Possible alternative solution for Tesla with Pulsar 

Instead of going for a manual solution, Tesla could have created an automated lumber support system that would work on its own. Not only would this make it easier for vehicle owners, but they would also actually notice that the system is there. 

IoT is the future of automation, and it has many use cases, including the automotive industry. With Apache Pulsar, Tesla could have a serverless architecture that could be used for executing functions like lumbar support in a user-defined way. 

The functions can transform the input data into output data for all events that are received within the input topic. On top of that, they can create output data and publish it in the output topic, allow filtering, and provide routing when analytics are executed. 

Pulsar supports all the analytics execution that allows proper IoT support. For example, in terms of automation in the automotive industry, Pulsar can activate the lumbar support system and adjust it according to the user and their sitting position.

You could also use it for automatic climate control, light dimming, and so much more. 

Pulsar for IoT analytics in real-time 

Regardless of its source and goal, business data loses value quickly, mainly when data is used to monitor systems in real-time and provide the necessary output that needs to be timely. 

Datasets of high velocity and volume for these kinds of use cases have valuable insights that perish really quickly, and the insights need to be used almost instantly. To get the most value out of their data, companies need a different approach for real-time processing. 

It’s imperative to reduce decision latency when using perishable insights found within data streams operating in real-time. That allows systems to act on these insights and capture the window of opportunity. 

In a data environment that is both high in velocity and volume, generating insights quickly can be really challenging. The more data there is, the more time is needed for the data to be transmitted back and forth to get processed. 

Distributed analytics on the edge 

In a way, Apache Pulsar is a messaging tool that can do “on the edge” distributed analytics so that less data is transferred to data centers or to the cloud. These distributed analytics focus on generating results that need to be transferred to storage. 

This vital feature gives better accuracy when it comes to computing IoT results. For example, automotive companies often use IoT technologies to support different processes in their vehicles. 

They use different IoT devices to act as sensors or data collectors. When this data is collected, it can be directly sent to the messaging system (Pulsar). The output is sent to the analytics processors or real-time application, which can then alert the driver that they are going over the speed limit or that something is wrong with the vehicle. 

IoT applications within vehicles need robust log storage and streaming capabilities, and this is why Pulsar is ideal for this use. There are no similar systems currently on the market that can offer both of these requirements. 

Benefits of Pulsar for automotive IoT applications 


One of the essential benefits of Pulsar is that processing and storage are separated. In other words, it’s possible to scale processing without any effect on storage. On the other hand, companies can scale storage size without any impact on processing. 


The exceptional scalability means that you can easily adjust the whole system without any extra costs. At the same time, Pulsar also has its multi-tenancy feature that lets you use several devices or sensors in multiple places without having to open up a new cluster for data. 


Due to its architecture, this system offers excellent analytics speeds. The computation power is still on a high level, and it’s easy to get quick outputs that are necessary for the automotive industry and can prevent disasters. 


IoT analytics in the car industry is a complex issue. However, modern streaming platforms don’t offer any tangible solutions. With Apache Pulsar distributed analytics on the edge can significantly reduce the time and cost needed to capture data. 

Combined with a high degree of accuracy and cost-effectiveness, we can only expect to see an increase in the use of Pulsar for IoT analytics.

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