Business Need :

Create a unified Data analytics platform for Digital services infrastructure to address reporting needs on O365, EDM, Citrix, Solar Winds and Thousandeyes.

Solution :

• Created a central metadata database for all data sources 

• Used SHIR ADF for data ingestion from on-premise database servers on ADLS Gen2.

• Leveraged ITSM Databricks pipeline to ingest data from API based data sources and data processing

• Used Jinja templates for implementing various datavault tables

• Built Datavault model to store data in an organized and effective manner.

• •Built Dimensional model on top of Datavault model to serve the reporting needs.

Benefits :

• Metadata driven approach, code reusability.

• Better Slicing and dicing option on PowerBI reports.

• Addressed the custom KPIs need.

• Automated data pipeline and reporting refresh.

• Scaled the same solution to other service now entities.

• Low maintenance and Low operational cost

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Revolutionizing Digital Service Analytics: A Unified Data Platform

Introduction

In the fast-paced world of digital services, having a robust data analytics platform is no longer a luxury but a necessity. Businesses today rely heavily on various digital service infrastructures such as O365, EDM, Citrix, SolarWinds, and ThousandEyes. However, the diversity and complexity of these services often lead to siloed data and reporting challenges. This article explores a transformative solution that addresses these challenges head-on, by creating a unified data analytics platform tailored for digital services infrastructure.

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Business Challenge

The primary challenge lies in the disparate nature of digital service platforms, each generating vast amounts of data across different formats and structures. This diversity makes it exceedingly difficult to achieve a holistic view of the infrastructure's performance and health. The need for a unified analytics platform became evident, one that could centralize data from O365, EDM, Citrix, SolarWinds, and ThousandEyes, and provide comprehensive reporting capabilities.

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​​Solution Overview

To meet this critical business need, a multifaceted solution was devised, focusing on integration, automation, and scalability. Here’s a breakdown of the solution components:

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1. Central Metadata Database

The foundation of the unified analytics platform is a central metadata database designed to consolidate all data sources. This database serves as the backbone for data management, ensuring consistency and accessibility across the board.

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2. Data Ingestion with SHIR ADF

For on-premise database servers, the Self-hosted Integration Runtime (SHIR) in Azure Data Factory (ADF) was employed for data ingestion into Azure Data Lake Storage (ADLS) Gen2. This approach enabled seamless and secure data transfer from on-premise to cloud, catering to the complex data landscape of digital services.

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3. ITSM Databricks Pipeline

For API-based data sources, an IT Service Management (ITSM) Databricks pipeline was leveraged. This pipeline not only ingests data but also processes it, preparing it for analytics and reporting. The use of Databricks here underscores the solution's emphasis on flexibility and processing power.

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4. Implementation with Jinja Templates

Jinja templates were used to implement various Datavault tables, offering a dynamic and efficient way to handle data transformations. This template-driven approach allows for code reusability and simplifies the maintenance of data models.

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5. Datavault and Dimensional Models

At the heart of the platform is the Datavault model, which organizes data in a highly structured and secure manner. Built on top of this, the Dimensional model facilitates reporting by making data easily accessible and queryable for analytics purposes.

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Key Benefits

The implementation of this unified analytics platform brought forth several significant benefits:

Metadata-Driven Approach: Facilitates code reusability and streamlines data management across diverse data sources.

Enhanced Reporting: Offers superior slicing and dicing options in PowerBI, enabling deeper insights and more customizable reports.

Custom KPIs: The platform can accommodate specific KPIs, addressing unique business needs and providing tailored analytics.

Automation: Automates both the data pipeline and report refresh processes, reducing manual effort and increasing efficiency.

Cost Efficiency: Results in lower maintenance and operational costs, providing a cost-effective solution for comprehensive digital service analytics.

Conclusion

The creation of a unified data analytics platform for digital services infrastructure represents a leap forward in addressing the complex reporting and analytics needs of modern businesses. By centralizing data sources, automating data ingestion and processing, and providing flexible reporting capabilities, organizations can now gain unprecedented insights into their digital service operations. This holistic approach not only improves operational efficiency but also paves the way for strategic decision-making based on comprehensive, data-driven insights.