AWS Data Lake Formation for an automotive software giant
This project entails the development of a proof of concept to build a scalable and efficient data architecture, laying the groundwork for innovative AI and business intelligence applications.
Project summary
The goal was to build a proof of concept aimed at establishing a unified data infrastructure using AWS Data Lake Formation. This involved designing and implementing a scalable and secure data lake architecture that will serve as the foundation for AI and business intelligence applications by centralizing data management, enhancing accessibility, and enabling advanced analytics.
Project challenges
The problem statement centered around integrating and consolidating fragmented data sources from diverse systems within the existing client's infrastructure and setting up an automated infrastructure on the AWS cloud. This infrastructure needed to effectively manage new data sources, enable real-time analysis, and support informed decision-making for businesses in the automotive sector.
Our process
We prioritize seamless integration, stakeholder collaboration, and continuous optimization to deliver data lake and analytics solutions that empower our clients to innovate and excel in their industries.
Our solution
We designed our approach to tackle the key pain points head-on, empowering the client to leverage the full potential of their data assets and drive actionable insights.
Built a scalable architecture that maps the integration of fragmented data sources from diverse systems, optimizing data flow, and ensuring seamless interaction between data ingestion, storage, processing, and analytics components.
Configured data pipelines ensure reliable and timely ingestion, regardless of the source or format, streamlining the collection process, thus, enabling continuous data updates while ensuring the integrity and accessibility of critical information.
Established a scalable and highly secure storage solution using Amazon S3, tailored to handle large volumes of diverse data types to encompass efficient data organization, versioning, and accessibility within the AWS ecosystem.
Employed sophisticated machine learning algorithms to analyze data and generate actionable insights for predictive analytics, facilitating immediate data querying and dynamic visualization to support informed decision-making in real time.
" I was impressed by Applify’s smart teammates who understood us and communicated well. "
Final result
The project integrated 15 fragmented data sources into a unified AWS Data Lake, achieving a 40% improvement in data accessibility, security, and scalability, alongside 70% operational efficiencies.
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