Project Overview
The main objective of the project is to build a strong data infrastructure that helps support the development of analytics applications for insurance providers, drivers, and fleet management companies.
The client wanted their IoT devices to capture IMU (Inertial Measurement Unit) data and video feeds that needed to be processed, stored, and then analyzed in real-time.
Another objective was to develop the solution in such a manner that it can be easily deployed on AWS Cloud Infrastructure for scalability and efficiency.
Scope:
- Data Pipeline Development: Build a serverless ETL pipeline that has the capability to store, process, and analyze mobility data that is present in huge volumes.
- Real-time Analytics: Enable real-time analytics on the data lake in order to provide instant insights for improved and data-driven decision-making.
- Data Transformation: Transform raw and unstructured data into structured and clean data, which can be easily stored in a data warehouse for further analysis.
Key Challenges
- High Data Frequency To handle the high frequency of data retrieval from various IoT devices, a highly efficient storage and processing mechanism was required. This mechanism was needed to heelp the client handle the data streams in an efficient and effective manner and without delays.
- Unstructured Data at Scale The management of huge volumes of unstructured mobility data led to many challenges as it contained varying data schemas from multiple devices.
- Data Quality and Preprocessing To ensure that the data is ready for analysis, the data is first required to be preprocessed, verified, and sanitized. This process includes handling incomplete or inconsistent data and filtering out various data noises for improved analysis.
Our Solution
Data Collection & Preparation
- Designed a fully managed, automated data pipeline that helped in the secure collection and processing of large volumes of streaming IoT data. The pipeline also made sure of zero packet loss while integrating multiple data sources.
- We built a serverless data lake on AWS to help in the storage of raw data, and used a strong ETL process in order to clean and structure the data for further analysis.
Real-time Analytics
- Implemented a query solution with the help of AWS services that led to real-time data analytics, ad hoc querying, and data visualization. As a result, the client was able to run real-time queries and gain immediate insights effectively and efficiently.
Technology Stack
- AWS Cloud Infrastructure services, such as AWS IoT Core, S3, Glue, Athena, QuickSight, and Redshift were used in order to build the solution.
- Python, SQL, and Tableau were used in order to run visualizations, extract insights, and build predictive models in order to achieve advanced analytics.
Benefits Delivered
Scalable Data Infrastructure
Built a highly secure and scalable data infrastructure that has the capability of storing and analyzing clean data at scale.
Ad-hoc Query Capabilities
Provided the data science team of the client, the ability to perform ad-hoc queries on mobility data, leading to improved real-time analysis and decision-making.
Improved Data Quality
The structured data that is stored in the data warehouse is considered to be ready for further analysis, which leads to getting reliable and accurate insights for fleet management and insurance providers.
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