Hello. This is Bryan Kang from 'This is My Architecture'.
I'm with Jongmin Park, director of LG Electronics' Digital Transformation Technology Center.
This is Jongmin Park from LG Electronics Digital Center.
Yes, I would like to introduce LG Electronics' ThinQ service first.
LG ThinQ is the brand name for products and services equipped with artificial intelligence.
Today, I want to introduce ThinQ Cloud connected with
LG ThinQ products.
There are various products linked to ThinQ Cloud such as home appliances, TVs, and
robot IoT products.
LG ThinQ Cloud provides connectivity, collects data,
and provides services.
What challenges did you try to solve while creating this architecture?
We progressed with two main goals.
First, as data-based services continued to expand,
Wi-Fi modules in all our home appliances were installed.
Scalability and stability were very important in order to process the data on the installed devices.
The first challenge was to respond to that.
Second challenge was making progress with such a big goal. We had to build an integrated architecture
while maintaining backward compatibility with existing services and products.
Please introduce the architecture.
ThinQ Cloud is composed of four layers.
It is composed of a layer that provides connectivity to products and collects digitals,
and a layer that processes requests from apps and data collected from devices,
and there is a layer that loads and analyzes.
As an example of a home appliance,
A ThinQ product is connected with AWS IoT through mutual authentication,
and the data collected by EC2 and Lambda is processed according to the data request method.
Also, depending on the nature of the data, each EC2 and Lambda are using
RDS and DynamoDB mixed in both sides.
This is because the collected data is finally loaded into the data lake.
Yes, you are using IoT Core at the front.
Please tell us the reason for choosing IoT Core.
We experienced that providing a stable 24/7 connectivity with global services is
a very resource-intensive task to maintain, so we reviewed the application
of AWS IoT from the initial stage.
The connectivity provided by AWS IoT, data collection and processing, and security-related functions
met our requirements well.
From the initial stage of development, we worked with AWS IoT
and conducted several architectural reviews.
In the meantime, we added a lot of new functions.
With new functions added to AWS IoT, we were able to apply a more efficient structure.
Yes, if you look at the next layer, you use EC2 and Lambda for the processing layer.
In the end, you are using the same functionality, right?
In the early stages of the service, API Gateway and Lambda were actively
used to respond to fast requirements.
As the service grew more and more, we were able to achieve
some cost-effectiveness by processing the data through EC2
when the workload was predictable.
And, If you look at the layer stored in the back, It seems that you are mixing RDS and DynamoDB.
What is the reason?
The data collected by the device will be stored in different storages
according to the properties of the data.
DynamoDB specialized for scalability is used for time series data that is continuously
generated for 24 hours, such as device status change.
And, RDS is used for traditional relational data.
Although it is not directly used in the service, data used for data analysis and AI training data are
loaded into S3 through Kinesis Firehose.
Yes, there seems to be a lot of data loaded in the data lake at the end.
How are you using it?
All data collected from the device is finally being loaded into the the data lake.
This loaded data is being used to analyze the usability of the device through data analysis,
or to review the addition of new functions in the future.
In addition, we are operating the data portal system
so that planners and analysts in the team can easily analyze data.
In addition, in order to respond to the requirements at the system level, we are operating the data hub platform for system integration.
Through this platform, we provide preprocessing of data and then transmission of data in real-time.
How are you going to improve this architecture in the future?
According to its growth stage, the cloud platform will need re-architecturing.
As the number of devices linked to the current device continues to increase,
we plan to continue to re-architecture so that we can secure stability over scalability accordingly.
Currently, the service is based on the usability data and user activity data collected from the device,
but in the future, we plan to integrate all data related to the production of the device, purchase and repair.
And then, we'll expand our service based on it.
Thanks for your explanation.
Thank you.
This has been 'This is My Architecture'.