The Objectives

Our client’s key business objective is to implement Data Mesh Architecture to provide unified architecture across the organization, enhance lake house platform to give business users the self-service capability to build business data products by implementing Infrastructure as Code, Common Data models, and aggregated data products. Implement meta-data driven data pipelines ingestion framework to support different source data, robust data anonymization for a diverse array of data products, optimizing the seamless ingestion of multi-sheet Excel data from SharePoint folders, and improving data visualization through Azure Databricks tables. This strategic initiative aims to eliminate inefficiencies in the existing data ingestion framework and ensure a streamlined and secure data management process.

Challenge

• Inefficient Data Ingestion Framework: Dealt with an inefficient data ingestion process that hindered development efforts and lesser scope of re-usability.
• Complex Parallel Data Loading: Faced challenges in efficiently ingesting data from over 30 source systems. No Metadata config driven framework.
• Multiple Architectures: Facing challenges with multiple architecture patterns such as Enterprise, and Commercial Lake house platforms. No unified best practise lake house architecture to cater to self-service users to use Infrastructure as Code, Common Data Models and Data products.
• Unsupported Business Self Service: Less provision to Business users to produce self-service use case-focused data products using common data models and aggregated data models.
• Pipeline Time and Cost Overruns: Experienced delays and increased costs due to suboptimal data processing efficiency.
• Data Quality Assurance: Needed to implement automated data quality checks to maintain data integrity.
• Data Security and Compliance Risks: Confronted technical complexities that posed challenges in ensuring data security and compliance.

Solution

Scalable Data Mesh Solution:

Three Data Product Categories:

• Source-Aligned Data Product: Collects and transforms operational data into a consumable format.
• Aggregate Data Product: Creates higher-level data models for global enterprise use by consolidating source data.
• Consumer-Aligned Data Product: Tailored for specific use cases, producing focused models for select clients without reusability.

Data mesh on Medallion Architecture:

• Microsoft Cloud Adoption Framework: Implemented a scale-out, decentralized data platform according to the Microsoft Cloud Adoption Framework. This organizes resources, including Snowflake, Azure Data Factory (ADF), Databricks, Azure Storage, and Azure Data Lake Storage, into Domain Data teams. This ensures data availability to key stakeholders.
• Hierarchical Architecture:
• Data Layer: Manages Ingestion, Transformation, Storage, and Serving segments.
• Platform Layer: Covers Monitoring, Infrastructure as Code (IaC), Master Data Management (MDM) using Reltio, Data Catalog, DevOps/MLOps, Security, Data Lineage, and Networking.
• Data Landing Zone as Scaling Unit: Establishes data landing zones representing Azure Subscriptions, enabling fine-grained cost analysis. These zones are provisioned using infrastructure-as-code tools, maintaining uniformity and centralized management. Each data landing zone hosts a separate data lake for domain-specific data storage.

Data Anonymization and Security:

• Advanced Data Masking: implemented a robust data masking strategy using Azure Databricks and Python. This strategy ensures the anonymity of sensitive data columns, guaranteeing data security and confidentiality
• Strengthened Access Control: To enhance data security and compliance, we have enforced role-based access policies and utilized encryption techniques effectively. This provides protection against unauthorized access and data breaches. Leveraging Collibra for Data Discovery and Governance.

Efficient Data Ingestion and Processing:

• Customized SharePoint Framework: Developed a customized and automated data ingestion framework. This framework leverages web activity, HTTP linked services, Pandas, Openpyxl, and Databricks to streamline the process of ingesting multi-sheet Excel data into Azure Databricks. As a result, we have eliminated inefficiencies, reduced development effort and time, and optimized data efficiency.
• Optimizing Parallel Data Processing: Harnessed Databricks Delta Lake and its partition mechanism to seamlessly integrate raw data from source systems into the cloud. This approach facilitates efficient parallel inserts and updates for handling extensive data volumes.
• Advanced ETL Optimization: Implemented advanced optimizations across end-to-end ETL processes using Databricks and Azure Data Factory (ADF) includes techniques like caching, memory optimization, and ADF distributed copy. These optimizations enhance overall performance.

Optimizing Data Processing Efficiency:

• To enhance data processing efficiency, we have implemented strategies that include fine-tuning cluster usage within Databricks, optimizing data workflows in Azure Data Factory (ADF), and leveraging parallel data loading techniques with intelligent partitioning. These efforts have resulted in reduced execution time and costs.

Data Quality Automation:

• Implemented automated data quality checks at multiple levels to ensure data integrity. Customized code has been developed to effectively handle erroneous data, ensuring exceptional data quality.

Impact

• 50% Faster Data Processing: Efficient Metadata Driven Pipeline
• 40% Cost Savings: Proficient Databricks Cluster Management
• Orchestrated Seamless Integrations: Achieved streamlined data connections using ADF and Databricks
• Enhanced Data Support across 16 Modules
• Security Compliance: Fortified Data Security with Azure Cloud Compliance and Databricks
• 30% Faster Operations: Centralized Data for Enhanced Efficiency and robust DevOps framework