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Unleashing the Power of Modern Analytics with Azure Databricks

  • Writer: Madhur Sharma
    Madhur Sharma
  • Jan 23, 2024
  • 2 min read


🚀 Welcome to a fascinating exploration of modern analytics architecture with Azure Databricks! Let's journey through one of the most widely deployed analytics architectures in Azure. Let's dive into the intricacies of this architecture and unravel the magic together.


🎯 Defining the Goals: Before we delve into the architecture, it's crucial to understand the goals it needs to meet. The scenario requires the architecture to:


  1. Ingest and analyse data from various sources, including streaming data.

  2. Run efficiently and reliably at any scale.

  3. Provide insights through analytics dashboards powered by machine learning.



The Architectural Blueprint: The architecture diagram showcases the four main layers: Ingest, Process, Store, and Serve. Let's explore each layer and the corresponding data flows:

  1. Ingest Layer:

  • Azure Databricks ingests raw streaming data from Azure Event Hubs.

  • Event Hubs, a fully managed big data streaming platform, seamlessly integrates into the Azure ecosystem.

  1. Process Layer:

  • Data Factory loads raw batch data into Data Lake Storage Gen2.

  • Data Lake Storage Gen2, a scalable and secure data lake, accommodates structured, semi-structured, or unstructured data.

  1. Store Layer:

  • Data Lake Storage Gen2 organises data into Bronze (raw), Silver (cleaned), and Gold (aggregated) layers.

  • Delta Lake, a specialised part, ensures well-organized and refined data accessible to different tools and systems.

  1. Serve Layer:

  • Azure Databricks facilitates data scientists in tasks such as data preparation, exploration, and model training.

  • MLflow, an open-source platform, manages the machine learning lifecycle with tracking and deployment capabilities.

  1. Analytics and Visualization:

  • ML models built in Azure Databricks are stored in the MLflow Model Registry.

  • Models can be deployed to Azure Machine Learning web services or Azure Kubernetes Service (AKS).

  1. SQL Analytics:

  • SQL Analytics runs SQL queries on the data lake and visualises data with Azure Databricks.

  1. Power BI Integration:

  • Power BI generates analytical reports and dashboards from the unified data platform.

  • A built-in Azure Databricks connector enhances data visualisation capabilities.

  1. Export to Azure Synapse:

  • Gold datasets from the data lake are exported to Azure Synapse SQL pools.

  • Azure Synapse provides a data warehousing and compute environment.

  1. Additional Azure Services:

  • Microsoft Purview for data governance.

  • Azure DevOps for CI/CD capabilities.

  • Azure Key Vault for secure storage and access control.

  • Microsoft Entra ID (Azure AD) for identity and access management.

  • Azure Monitor for telemetry analysis.

  • Azure Cost Management and Billing for cloud spending management.


🌐 Conclusion: This comprehensive overview showcases our modern analytics architecture leveraging Azure Databricks. From ingesting streaming data to implementing machine learning models and generating insightful reports with Power BI, this solution provides a unified platform for data analytics.

If you found this video insightful, don't forget to like, share, and subscribe. Until next time, stay curious and stay data-driven! 🚀

 
 
 

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