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| 1 | +# Data Engineering - Best Practices Overview |
| 2 | + |
| 3 | +Costa Rica |
| 4 | + |
| 5 | +[](https://github.com) |
| 6 | +[](https://github.com/) |
| 7 | +[brown9804](https://github.com/brown9804) |
| 8 | + |
| 9 | +Last updated: 2025-05-02 |
| 10 | + |
| 11 | +---------- |
| 12 | + |
| 13 | +> Ensure that your Microsoft Fabric data engineering stack is designed for clarity, performance, scalability, and long-term maintainability. Each component (from your lakehouse to your APIs) should be organized with clear definitions, detailed documentation, and best-in-class practices integrated into every step of your data pipeline. |
| 14 | +
|
| 15 | +<details> |
| 16 | +<summary><b>List of References</b> (Click to expand)</summary> |
| 17 | + |
| 18 | +- [Data Engineering documentation in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-engineering/) |
| 19 | +- [Direct Lake overview](https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview) |
| 20 | +- [Create an API for GraphQL in Fabric and add data](https://learn.microsoft.com/en-us/fabric/data-engineering/get-started-api-graphql) |
| 21 | + |
| 22 | +</details> |
| 23 | + |
| 24 | +<div align="center"> |
| 25 | + <img src="https://github.com/user-attachments/assets/e50101a3-d856-474c-96a9-4771a88a97bf" alt="Centered Image" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/> |
| 26 | +</div> |
| 27 | + |
| 28 | +| **Best Practice** | **Description** | **Example** | |
| 29 | +|------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| 30 | +| **Structured Lakehouse Organization** | Organize your data lakehouse into well-defined zones (raw, processed, and curated) that include optimized storage formats, clear partitioning schemes, metadata tagging, access controls, and lineage tracking. This approach not only simplifies data discovery and query performance but also ensures robust data governance, highlighting every transformation. Designing your storage with structured hierarchies and access layers helps maintain both flexibility and compliance while enabling efficient audits and troubleshooting. | Create dedicated directories where:<br>- **Bronze Layer** Holds ingested data in its original format with minimal transformations to preserve source fidelity.<br>- **Silver Layer** Contains data that has been cleansed, validated, and enriched.<br>- **Gold Layer** Delivers analytics-ready datasets using formats such as Parquet or Delta Lake, partitioned by key fields like date or region. Enhance these folders with metadata catalogs and role-based access to safeguard sensitive information and track data lineage from ingestion to consumption. | |
| 31 | +| **Well-Documented Notebooks** | Develop notebooks that are modular, well-structured, and version-controlled to ensure end-to-end transparency and reproducibility. Use rich markdown annotations to explain the rationale behind data ingestion, transformation logic, and analytical models. Embedding inline comments, headers, and detailed commit histories allows your team to understand the evolution of code and quickly troubleshoot issues. This meticulous documentation fosters collaboration, ensures best practices are followed, and supports seamless handoffs between team members. | Split notebooks into logical sections such as:<br>- **Data Ingestion:** Code to load raw data with validation checks.<br>- **Data Transformation:** Segments dedicated to cleaning, merging, and enriching data with clear explanations of business logic.<br>- **Analysis & Visualization:** Final processing that translates data into actionable insights.<br>Adopt a distributed version control system (e.g., Git) and use clear commit messages along with markdown cells that detail the purpose, changes, and expected outputs of each segment, ensuring that both current and future team members can understand and extend your work. | |
| 32 | +| **Dedicated Environment Segregation** | Isolate your development, testing, staging, and production environments to enforce strong governance and reduce risk. This strategy ensures that experimental changes are rigorously validated before they reach a live production system. Use configuration management to maintain consistency across environments, automate testing and deployment pipelines, and apply strict role-based access controls to safeguard sensitive data and resources. This segregation minimizes disruptions and helps identify issues early in the release cycle. | Use Microsoft Fabric workspaces to create distinct environments:<br>- **Development:** Where new features are developed and initial testing occurs in a flexible setting.<br>- **Testing/QA:** An environment with close-to-production configurations where changes are validated using automated tests and performance metrics.<br>- **Staging/Production:** Highly controlled environments with rigorous access policies, ensuring stability and compliance. Automate promotion pipelines with version-controlled configuration files, and use tools like CI/CD frameworks to enforce consistency and retraceability of deployments. | |
| 33 | +| **Optimized Spark Job Definitions** | Define Spark jobs with parameterized configuration settings, comprehensive error-handling mechanisms, and integrated logging and monitoring. Use reusable templates that allow dynamic resource allocation—such as setting executor memory, partition count, and scheduling policies based on job size and data volume. Employ Spark’s built-in adaptive query execution and dynamic allocation features to optimize runtime performance and resource usage. Clear logging and metrics collection help quickly diagnose issues and refine job performance over time. | Develop standardized job definition templates (YAML/JSON) that include:<br>- **Dynamic Resource Parameters:** Fields for executor memory, number of cores, and partition strategies to optimize parallel processing.<br>- **Error Handling:** Logic for retry counts, graceful shutdowns, and alert triggers for failures.<br>- **Monitoring Hooks:** Integration points with monitoring dashboards or log aggregation tools (e.g., Azure Monitor) to provide real-time performance insights and alerts when thresholds are breached.<br>Such a template ensures that every Spark job follows the same robust setup, making scaling and troubleshooting more straightforward. | |
| 34 | +| **Standardized GraphQL API Integration** | Build GraphQL API endpoints with strict schema consistency, versioning, and thorough documentation. By defining clear naming conventions, providing detailed schema definitions, and implementing robust error management, you create APIs that are predictable and reliable. This standardization not only simplifies client-side integration but also establishes a clear contract between data services and consuming applications. Additionally, documenting sample queries, expected results, and potential errors fosters a smooth onboarding process for developers and reduces the learning curve. | Construct GraphQL APIs that adhere to a predefined structure:<br>- **Naming & Versioning:** Use consistent endpoint nomenclature (e.g., /api/v1/data) and maintain version tags to support backward compatibility.<br>- **Schema Documentation:** Leverage tools to auto-generate documentation (such as GraphiQL or Swagger-like interfaces) that detail each field, parameter, and possible mutation.<br>- **Error Handling:** Implement uniform error codes and messages that guide developers towards quick resolutions. Provide sample queries and mutation examples in your API documentation to illustrate correct usage and expected responses, helping streamline integration efforts across teams. | |
| 35 | + |
| 36 | +## E.g Deployment Structure |
| 37 | + |
| 38 | +1. **Lakehouse Segmentation**: Organize your data lakes into clearly defined zones for Raw, Processed, and Curated data. |
| 39 | + - **Zones & Hierarchy:** Set up separate directories for each zone and enrich them with metadata catalogs and tagging. Define partitioning logic based on anticipated query patterns (e.g., by date or region) and enforce access controls at each layer to ensure data integrity and security. |
| 40 | + - **Optimized Formats:** Use cloud-optimized file formats like Parquet or Delta Lake, which support efficient columnar storage and faster queries. |
| 41 | + |
| 42 | +2. **Notebook Modularity**: Structure your notebooks for clarity and collaboration. |
| 43 | + - **Sectional Breakdown:** Divide into discrete units such as ingestion, transformation, and visualization. Use detailed markdown descriptions and inline comments so that each part of the process is self-explanatory. |
| 44 | + - **Version Control:** Incorporate Git or Azure DevOps to track changes over time, ensuring that all modifications are documented, and previous versions are easily accessible for audits or rollbacks. |
| 45 | + - **Logging & Metrics:** Embed logging at critical steps to capture runtime performance and facilitate debugging. |
| 46 | + |
| 47 | + <img width="550" alt="image" src="https://github.com/user-attachments/assets/26a0a594-efa7-4fec-8b25-0de5e5d58701" /> |
| 48 | + |
| 49 | +3. **Environment Isolation**: Maintain strict separation between development, testing, and production. |
| 50 | + - **Isolated Workspaces:** Allocate dedicated workspaces for each environment, ensuring that development changes cannot inadvertently affect production. |
| 51 | + - **Role-Based Access:** Apply RBAC policies to control environment-specific access and manage sensitive data with confidence. |
| 52 | + - **Automated CI/CD:** Enforce automated promotion pipelines that validate code through rigorous testing and regression checks at every stage, bolstering the integrity of each deployment across environments. |
| 53 | + |
| 54 | +4. **Spark Job Optimization**: Ensure your Spark jobs are robust and adaptable. |
| 55 | + - **Reusable Templates:** Standardize job definitions using parameterized templates that allow for quick modifications and scaling. Customize resource configurations based on job demands. |
| 56 | + - **Adaptive Performance:** Leverage Spark’s adaptive query execution and caching capabilities. Implement comprehensive logging and monitoring to identify performance bottlenecks and automatically adjust resource allocation as needed. |
| 57 | + - **Error & Retry Logic:** Integrate built-in error handling that facilitates automatic retries and graceful handling of failures, reducing manual intervention during peak workloads. |
| 58 | + |
| 59 | +5. **GraphQL API Standardization**: Deploy GraphQL APIs that are maintainable and clear. |
| 60 | + - **Consistent Naming & Versioning:** Adopt naming conventions that provide clarity and easy integration. Use versioning to manage changes and communicate deprecation policies effectively. |
| 61 | + - **Comprehensive Schema Documentation:** Create detailed, auto-generated documentation for every endpoint; include sample queries, expected responses, and precise error messages to aid developer understanding. |
| 62 | + - **Robust Error Handling:** Implement consistent, informative error responses and integrate thorough test suites to guarantee smooth operation and backward compatibility as the API evolves. |
| 63 | + |
| 64 | + https://github.com/user-attachments/assets/8971651d-9aff-4b41-94ca-9a35b9241f22 |
| 65 | + |
| 66 | +<div align="center"> |
| 67 | + <h3 style="color: #4CAF50;">Total Visitors</h3> |
| 68 | + <img src="https://profile-counter.glitch.me/brown9804/count.svg" alt="Visitor Count" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/> |
| 69 | +</div> |
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