Abhijit Singh
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Abhijit Singh

Building a Cloud-Based Data Lakehouse for Securitas

Building a Cloud-Based Data Lakehouse for Enhanced Organizational Insights

Process Diagram

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.

Solution Architecture

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.