Hello, and welcome to 'This Is My Architecture'.
I'm Joe, and I'm joined today Parsh from LeasePlan.
-Welcome Parsh. -Thank you Joe.
So Parsh, talk to me a little about LeasePlan and what you guys do.
Sure. We are the world's largest fleet management company
with over 1.8 million vehicles under management.
We believe in providing sustainable fleet solutions to our customers
so that they can do what they do best.
Outstanding. Fleet management is a big topic these days.
Talk to me a little about your solution you've built. Can you walk us through that?
Sure. So this is called OneConnect.
It's an integrated Telematics platform.
You know, Telematics brings a lot of data to us,
but the problem we are trying to solve is how can we bring all this data
and combine it with other sources that we have such as fuel data.
Excellent, excellent. So talk to us about how the customer
comes into your solution and how they leverage the data that's maybe on a vehicle.
Sure. So we have IoT devices on these vehicles that are capturing this information for us.
They have a number of sensors such as what's the speed of the vehicle,
what are the g-forces experienced by the car, GPS access, access to the computer of the car.
And all this information is being sent to us in raw form to a Lambda.
This is where we capture this information in S3 to sort of form our data lake.
Excellent. Now that's a lot of data coming in. How are you managing performance?
How are you managing that data coming in?
So we take this data into fixed batch sizes and load it into SQS.
This is where we can scale this processing of this information
as well as introduce some durability to the process.
Outstanding. Message drops, anything like that?
Not really, because SQS sort of helps us make sure that we process every piece of data that comes to us.
That's a great selection. Absolutely.
So talk to me about OpenStreetMaps. How does that play into this?
So we have enriched this information with primarily map data,
but using a third-party provider is cost priority because of the number of data points that we have.
So this is why we use OSM data that's provided by AWS
as part of the Open Data Initiative in S3.
Outstanding. So when you bring in OpenStreetMaps and merge that with the Telematics data,
walk me through that process. How does that work?
Are you losing your Lambda? How does that flow go?
Sure. So one of the challenges that we had with OSM data is the amount of data size that we had
for us to process in Lambda.
So what we did was we took the OSM data, if I may draw the U.S. map--
We took the data, and as you can see,
the map data is sort of bound by about 100 degrees in longitude and 50 degrees in latitude.
So we sort of pre-chunked the data into 5,000 one-degree chunks,
stored it in S3, allowing us to process it in a very rapid fashion.
Outstanding. Is that done through Athena?
How did you do that? How did you chunk that data and search for that data?
So we sort of did a background process that pre-chunked this data using Athena.
Okay, excellent. And then from Athena where does that data go?
Are we using that data anywhere else?
So this data is then sent to Kinesis where we sort of can cast a net
to identify interesting data points.
We have sliding window queries that identify whether there is a speeding event.
The sensors for example tell us the vehicle was traveling at 60 miles per hour,
OSM tells us what's the legal speed limit at that, let's say it's 50 miles per hour,
then we know it's a speeding event.
Yep. Absolutely. How does the customer know that this event has happened?
Is there some kind of reporting? How do you show that?
So we bring this data to S3 and from there we, using Glue,
bring it to our on-prem data centers
which allow us to sort of use it in various applications, reports, and dashboards.
We also run Athena queries on our raw data
and also collect sort of summary information
such as how many miles did the driver drive
and all of this information is then brought to the customers.
Outstanding. So, overall, what did you say your biggest requirement was to actually build this solution?
So our biggest first focus was safety.
This is why we focused on events such as speeding, seatbelt violations, harsh accelerations.
This allowed us to score the drivers and rank them.
So the customers can then see who their top drivers are
and who their not so top drivers are.
They can provide necessary training for them.
So, one of our pilot customers saw 31% reduction in the incident rate when they implemented this project.
Outstanding, outstanding. This is a great solution.
I'm sure it's very viable in this industry and probably saved lives.
I do appreciate you showing this solution to us and definitely leveraging the AWS platform.
-Thank you for being here with us. -Thank you Joe, it was my pleasure.
And thank you for watching 'This Is My Architecture'.