Microsoft Fabric: Metadata Driven Data Pipelines Framework

The challenges of effective Extract, Load, and Transform (ELT) processes are multifaceted. Low-quality source data, characterized by missing, inaccurate, inconsistent, or duplicate information, can significantly hinder performance and introduce errors. Additionally, the dynamic nature of data sources, with evolving formats, connections, and increasing volume and velocity, demands a flexible and adaptable data pipeline solution.  

Introducing our Metadata-Driven Data Pipelines Framework on Microsoft Fabric. Designed to streamline and automate data ingestion, transformation, and delivery, our innovative framework leverages the powerful capabilities of Microsoft Fabric to deliver efficient and scalable data pipelines. By centralizing metadata management and providing reusable templates, we empower organizations to rapidly develop and deploy data-driven solutions while ensuring data quality and consistency.

The framework addresses these challenges by providing a comprehensive solution for data ingestion, curation, and orchestration.

Benefits:

• Robust Data Processing.
• Advanced Data Integration.
• Reduced manual efforts, enhanced quality, and faster service delivery.
• Customizable Workflows.
• Improve resilience, security, and performance.
• Higher agility through well-defined architecture.
• Eliminates the need for a separate transformation process, which can result in cost savings.
• Simplified Integration and Improved Data Quality.

Business Value:

Yash Metadata Driven Data Pipelines Framework using Microsoft Fabric provides you with data pipelines for different sources and curate the raw data, eliminating the time-consuming complexity of manual coding for data quality and service creation for the ingestion and curation process, freeing the customers to focus on what matters most- strategic data initiative and innovation.  It provides Fabric notebooks to the developer/data engineer users for the curation process. The user only needs to create data pipelines and provide their configuration files in the desired storage/database and our framework take care of the rest.

 The Components of Metadata-Driven Data Pipelines Framework using Microsoft Fabric

• Data Factory Pipeline Ingestion Templates: Pre-built templates, with supporting deployment guidelines, are available to DA Products for consumption, enabling ingestion of various source system types (E.g. SQL, ODBC, File based, etc.).
•  Fabric Notebook Curation Templates: Enabling DA Products to curate data at 4 distinct levels, leveraging pre-defined notebooks:
  • L1: Basic Curation
  • L2: Site Source System View
  • L3: Normalized (3NF)
  • L4: (Star Schema)
  • Consumption.
• Data Ingestion Config Templates: The ingestion pipelines follow a metadata driven approach, using JSON configuration files, which allow DA Products to customize the templates/ patterns to their requirements. 
• Data Curation Config Templates: The curation engine follows a metadata driven approach, using JSON configuration files, which allow DA Products to customize the templates/ patterns to their requirements.

 Advantages of Metadata Driven Data Pipelines Framework using Microsoft Fabric

•  Speed to Delivery: Reduces the time required to deliver data solutions by automating ingestion and curation processes with the help of pre-built templates and    configuration files.
• Consistency: Consistency in the codebase enhances maintainability, making it easier to manage and update the framework over time ensuring standardization. 
• Optimization: By leveraging the robust capabilities of Microsoft Fabric, ensures achieves high throughput and low latency in data processin.
• Reusability: Modular design promotes the reuse of components across multiple projects, reducing duplication of effort. 
• Efficiency: Enhances overall team efficiency in the data supply chain and frees up time to focus on developing new features and improvements.