Playbook for metadata driven Lakehouse implementation in Microsoft Fabric
Co-Author – Gyani Sinha, Abhishek Narain
Overview
A well-architected lakehouse enables organizations to efficiently manage and process data for analytics, machine learning, and reporting. To achieve governance, scalability, operational excellence, and optimal performance, adopting a structured, metadata-driven approach is crucial for lakehouse implementation.
Building on our previous blog, Demystifying Data Ingestion in Fabric, this post explores how to design and implement an end-to-end metadata-driven Lakehouse using Microsoft Fabric. While the core design principles are applicable to platforms like Azure Databricks and Microsoft Fabric, this blog focuses on leveraging Microsoft Fabric to create a scalable, governed, and high-performance lakehouse—rooted in real-world customer implementations.
Basic components of metadata driven Lakehouse implementation framework
Below graphic represents the basic building blocks (components) of the reference lakehouse implementation.
The table outlines the different components and their respective purposes.
| # | Component name | Purpose |
| 1 | Control Tables | Implement a control table to orchestrate and manage data ingestion, validation, and processing in a Lakehouse. Control tables must store parameters and configurations for ingestion, validation, and ETL processes, enabling dynamic adjustments without modifying the code. |
| 2 | Data Ingestion | Implement the metadata driven Data Ingestion component leveraging the control table to copy data from various source systems to the lakehouse. You may also use Shortcuts and the Mirroring feature in Microsoft Fabric to streamline ingestion and minimize data movement. Mirroring continuously replicates your existing data estate directly into Fabric’s OneLake, including data from Azure SQL Database, Azure Cosmos DB, and Snowflake. |
| 3 | Data Validation | Implement a data validation component to ensure the integrity of the migrated data. This component will help detect and resolve any discrepancies that might arise during the migration or ingestion process. By systematically validating the data, you can ensure its accuracy, completeness, and consistency, ultimately safeguarding the quality of the migrated datasets. |
| 4 | Data Profiling and Data Quality | Implement data profiling to gain a comprehensive understanding of your data. This process provides a descriptive overview, highlighting aspects like missing values, datatype distributions, and key statistics. Additionally, it helps identify anomalies by triggering alerts, enabling proactive data quality management and ensuring the data is ready for further processing or analysis. |
| 5 | Transformation and enrichment | Implement a data transformation and enrichment component to transform and enrich the data for consumption based on configurable transformation, enrichment, and standardization rules. |
| 6 | Auditing | Implement auditing component to audit the records ingested and processed into lakehouse. Auditing includes identifying any errors or issues that may have occurred during the process, as well as reviewing performance metrics to identify areas for improvement. |
| 7 | Notification | Implement notification component to notify in case of success or failure events. This is super critical to ensure that the operation team is notified during all critical events. |
| 8 | Config Management | Implement centralized config management component for managing the configuration to different source and target systems. |
| 9 | Reporting | Implement a reporting component to visualize metrics related to ingestion, validation, and transformation processes. This will provide the operations team with a single pane of glass, offering an end-to-end status of the lakehouse and enabling them to effectively monitor and manage all processes. |
Metadata driven framework implementation
Now that we’ve covered the key components, let’s explore how these elements can be integrated to create a robust, metadata-driven lakehouse implementation using Microsoft Fabric.
The diagram above highlights the key processing components, such as data ingestion, data profiling, and transformation, at the top, along with cross-cutting components like configuration management, auditing, notification, and reporting that support all processing activities.
Key components used in building the metadata-driven lakehouse implementation include –
- Data ingestion – There are several approaches to implementing data ingestion in Fabric, including a low-code method using Microsoft Fabric Data Pipeline with pre-built source and destination connectors, or a code-based approach using Microsoft Fabric’s Spark-based implementation. The destination in Fabric can vary based on the use case or customer preference, including lakehouse files, lakehouse tables, or Data Warehouse. Additionally, other ingestion methods like mirroring and shortcuts are available, which also need to be managed.
The benefits of storing data in lakehouse files are it supports both structured and unstructured data in their original format, making it a reliable source of truth for future consumption.
The image illustrates the key modules and the high-level data ingestion flow, starting with the control module, which queries the ‘ingest_control’ table to manage and control the ingestion process based on the configured sources handled by the config module. The data ingestion module is responsible for transferring data into the target lakehouse and utilizes the notification module to send alerts in case of failures.
All activities are logged in the ‘ingest_audit’ table and presented through the reporting module, offering a single pane of glass to monitor the ingestion process into the lakehouse. The monitoring module also tracks shortcut and mirrored items to ensure these are properly captured, using the ‘shortcut_audit’ and ‘mirroring_audit’ tables.
Data validation – The validation module, typically written in Spark, uses the configuration to validate both the source data and the target lakehouse or data warehouse data, checking for completeness and reasonableness. The validation results are stored in the ‘validation_results’ table and are presented through the reporting module, providing the operations team with insights into any discrepancies introduced during the migration or ingestion process.
The table provides examples of validation category, scope & criteria.
| Validation Category | Validation Scope | Validation Criteria |
| Completeness | Row Level | Record Count Validation |
| Completeness | Column Level | Columns Count Validation |
| Completeness | Column Level | Columns Datatype Validation |
| Reasonableness | Column Level | Numerical Columns Aggregation Check |
| Reasonableness | Column Level | Categorical Columns Group By Count Validation |
| Reasonableness | Row Level | Checksum Validation (first 100 rows) |
Data quality – The metadata-driven data quality component leverages metadata to automatically assess and enforce data quality rules across the lakehouse or data warehouse. It validates data consistency, accuracy, and compliance with predefined standards. The results are captured in a data quality results table and visualized through the reporting module, enabling the operations team to proactively identify and address any data quality issues. You can use Microsoft Purview’s Data Quality feature to scan lakehouse data and configure data quality rules for automated reporting of data health. Refer to Data Quality for Fabric Lakehouse in Unified Catalog for more information.
PII anonymization – PII anonymization component involves identifying and anonymizing personally identifiable information (PII) from datasets to protect individuals’ privacy while still allowing for data analysis and decision-making. This is essential in scenarios where sensitive data, such as names, addresses, and social security numbers, is involved. In a lakehouse scenario, PII anonymization is crucial for protecting data from unauthorized access and breaches, ensuring compliance with regulations like GDPR and CCPA, and maintaining data quality and integrity by preventing biases. This process helps organizations use data responsibly without compromising individuals’ privacy.
Transformation & enrichment – The transformation and enrichment component utilize the enrich control table to retrieve the list of datasets to be transformed, enriched, and stored from the bronze layer to the silver layer. This component handles both simple Spark-based transformations, such as renaming columns and changing data types, as well as more complex rule-based operations, like conditional filtering, any custom business logic or deriving new columns managed through ‘transformation_config’ table. Once the data is transformed, the serve control table is used to determine which datasets should be served into the gold layer after performing necessary aggregations. The audit module tracks the transformed records and logs the status of each run for accountability and traceability.
Bringing it all together using task flow in Microsoft Fabric
Conclusion
Implementing a metadata-driven Lakehouse using Microsoft Fabric enables organizations to efficiently manage and process data with governance, scalability, and optimal performance. By following the structured framework outlined in this playbook, key components like data ingestion, validation, profiling, transformation, and auditing ensure data integrity and quality.
This guide emphasizes the importance of cross-department collaboration for seamless implementation, unlocking the full potential of data to drive insights and innovation. We hope this playbook serves as a valuable resource for your Lakehouse implementation journey.
Special shout out to Winnie and Dharmendra for their continued contributions!
