Building a Cloud-Based Data Lakehouse for Enhanced Organizational Insights
Securitas sought to transform its data management strategy by establishing a centralized repository for all organizational data. The primary objectives included making data accessible to everyone, creating a well-organized data source, ensuring continuous data refresh, and converting raw data into actionable insights for improved decision-making. To achieve this, the client envisioned a robust data lake-house architecture on the cloud.
Approach
I adopted a phased approach, encompassing five key stages:
1. Data Collection: Leveraging AWS DataSync Agent, I facilitated the collection of raw data from diverse sources.
2. Ingestion: Employing Airflow, I designed a seamless data ingestion process to handle and integrate large volumes of data efficiently.
3. Storage and Metadata Processing: Utilizing Hive Metastore, I established a storage infrastructure with embedded metadata processing capabilities to enhance data governance.
4. Cataloging: Developed a comprehensive data cataloging tool to facilitate easy navigation and understanding of the stored data.
5. BI/Reporting: Established a Business Intelligence (BI) endpoint, ensuring that end-users could effortlessly derive insights from the centralized repository.
Challenges
The project encountered several challenges:
1. Diverse Data Formats: Collating and processing data in its raw format from various sources requires a nuanced approach.
2. Scalability: Building an infrastructure capable of handling large data volumes demands meticulous planning and execution.
3. Integration of Technologies: Integrating different technologies seamlessly to construct a unified system posed a significant challenge.
4. Balancing Accessibility and Security: Ensuring easy access to data for end-users while upholding stringent data security and governance standards requires a delicate balance.
Deliverables
The project yielded the following deliverables:
1. Data Collection Layer: Facilitated the gathering of data from diverse sources through a robust data transfer layer.
2. Data Ingestion Layer: Designed and implemented an efficient data ingestion process to handle large data volumes.
3. Storage and Metadata Processing Layer: Established a storage infrastructure with embedded metadata processing capabilities for improved data governance.
4. Cataloging Layer: Developed a user-friendly data cataloging tool to enhance accessibility and understanding of the stored data.
5. BI/Reporting Layer: Implemented a Business Intelligence (BI) endpoint to empower end-users in deriving actionable insights from the centralized repository.
Results
The implemented cloud-based data lakehouse architecture successfully centralized organizational data, making it accessible to all stakeholders. Raw data was transformed into actionable insights, thereby enhancing decision-making capabilities. The project exemplified a harmonious integration of technology to address the client's data management needs, ensuring a secure and governed approach to data accessibility.
