Best Tools on Azure for Storing, Processing, and Exposing Data

Best Tools on Azure for Storing, Processing, and Exposing Data for Reporting


In today’s data-driven world, organizations are increasingly leveraging data to drive insights, make decisions, and achieve strategic goals. The challenge, however, lies in effectively storing, processing, and exposing this data—especially unstructured data—while ensuring it is accessible for reporting and historical analysis. Azure offers a comprehensive suite of tools that address these needs, allowing businesses to build robust data architectures. This article explores the best Azure tools for storing, processing, and exposing data for reporting, emphasizing the use of a medallion architecture and including data governance and cataloging solutions.


 Storing Data: Azure Data Lake Storage Gen2


Azure Data Lake Storage Gen2 (ADLS Gen2) is the backbone of data storage in Azure for handling large volumes of both structured and unstructured data. It combines the features of Azure Blob Storage with a hierarchical file system, making it ideal for big data analytics and complex data scenarios.


 Key Features:

- Scalable and Cost-Effective: ADLS Gen2 provides a scalable storage solution with tiered pricing options (hot, cool, and archive), making it cost-effective for storing massive amounts of data.

- Hierarchical Namespace: Supports a hierarchical file system that simplifies the organization and management of data files.

- High-Performance: Optimized for high-throughput and low-latency access, critical for big data analytics and processing workloads.

- Integrated Security: Provides robust security features, including encryption, access control lists (ACLs), and role-based access control (RBAC).


In a medallion architecture, ADLS Gen2 can be used to implement the Bronze, Silver, and Gold layers for data processing:


- Bronze Layer: Raw data storage, ingesting unprocessed data as it arrives.

- Silver Layer: Cleaned and transformed data, ready for intermediate analysis and processing.

- Gold Layer: Highly refined data, optimized for reporting and business intelligence applications.


 Processing Data: Azure Databricks


Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform. It provides a unified environment for data engineering, data science, and business analytics, making it a powerful tool for processing data in the medallion architecture.


 Key Features:

- Scalable Data Processing: Leverages Apache Spark’s distributed computing capabilities to process large datasets efficiently.

- Delta Lake Integration: Adds reliability and performance improvements through Delta Lake, which provides ACID transactions, schema enforcement, and efficient upserts.

- Collaborative Notebooks: Facilitates collaboration with interactive notebooks supporting multiple languages (Python, SQL, Scala, R).

- Real-Time Data Processing: Supports real-time streaming data analytics, allowing for immediate processing and insights from incoming data streams.


In the context of the medallion architecture:

- Bronze Layer: Databricks can process raw data from ADLS Gen2, cleaning and transforming it into a more structured format.

- Silver Layer: Further refinement and transformation of data, including aggregations, joins, and data enrichment.

- Gold Layer: Preparation of the data for reporting and analytical consumption, ensuring it is in the most optimized form for business intelligence.


 Exposing Data for Reporting: Azure Synapse Analytics and Power BI


To make data accessible for ad hoc querying and reporting, Azure Synapse Analytics and Power BI are the go-to tools on the Azure platform.


 Azure Synapse Analytics


Azure Synapse Analytics is an integrated analytics service that combines big data and data warehousing. It provides a unified platform to manage and query data at scale.


 Key Features:

- Dedicated and Serverless SQL Pools: Supports both dedicated SQL pools for structured data and serverless SQL pools for on-demand querying directly on data stored in ADLS Gen2.

- Data Lake Integration: Allows querying of data stored in ADLS Gen2 without moving it, facilitating seamless integration between big data storage and data warehousing.

- Integrated Analytics: Combines SQL-based analytics with Apache Spark, offering flexibility in processing and analyzing data.

- Synapse Studio: A unified workspace for data ingestion, exploration, transformation, and reporting.


Azure Synapse Analytics plays a crucial role in the Gold layer of the medallion architecture, where it enables high-performance querying and analysis of refined data. It serves as the central hub for data analysis, supporting both ad hoc queries and structured reporting.


 Power BI


Power BI is Microsoft’s business analytics service that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.


 Key Features:

- Interactive Dashboards: Allows the creation of rich, interactive dashboards and reports with drag-and-drop simplicity.

- Real-Time Analytics: Supports real-time data monitoring and analytics, integrating with various data sources for up-to-date insights.

- Self-Service BI: Empowers business users to explore data and generate insights without deep technical expertise.

- Direct Query and Import Modes: Offers flexibility in data access, either by querying data in real-time or importing it for analysis.


Power BI connects directly to Azure Synapse Analytics, enabling users to visualize and report on data stored in the Gold layer. It is ideal for creating interactive and shareable reports that provide insights to business stakeholders.


 Data Governance and Catalog: Azure Purview


Azure Purview is a unified data governance service that helps manage and govern data across your organization. It provides a comprehensive view of your data landscape, ensuring data is discoverable and well-managed.


 Key Features:

- Data Discovery and Classification: Automatically scans and classifies data across various sources, providing a detailed inventory of your data assets.

- Data Lineage: Tracks data movement and transformations, offering visibility into data origins and how it evolves over time.

- Unified Data Catalog: Creates a centralized catalog of data assets, making it easier for users to discover and understand data.

- Security and Compliance: Supports data access controls and policies, helping ensure data security and regulatory compliance.


Azure Purview integrates with Azure Data Lake Storage Gen2, Azure Databricks, and Azure Synapse Analytics, providing a comprehensive data governance framework. It ensures that data across the Bronze, Silver, and Gold layers is well-managed and accessible to authorized users while maintaining compliance with organizational policies.


 Building the Complete Solution


Combining these Azure tools creates a powerful and scalable data architecture that can handle vast amounts of unstructured data, process it effectively, and expose it for analysis and reporting. Here’s how you can integrate these tools into a cohesive solution:


1. Data Ingestion and Storage:

  - Use Azure Data Lake Storage Gen2 to store raw, unstructured data in the Bronze layer. Utilize its hierarchical namespace and cost-effective storage tiers to manage and organize data efficiently.


2. Data Processing and Transformation:

  - Employ Azure Databricks to process and transform data. Start by cleaning and structuring data in the Bronze layer, then refine it further in the Silver layer, and finally optimize it for reporting in the Gold layer using Delta Lake for enhanced reliability and performance.


3. Data Modeling and Querying:

  - Leverage Azure Synapse Analytics to model and store refined data in the Gold layer. Use dedicated SQL pools for high-performance structured data storage and serverless SQL pools for flexible, on-demand querying directly from the data lake.


4. Reporting and Visualization:

  - Connect Power BI to Azure Synapse Analytics to create interactive reports and dashboards. Utilize Power BI’s capabilities to perform ad hoc queries and generate business insights from the refined data in the Gold layer.


5. Data Governance and Cataloging:

  - Implement Azure Purview to manage and govern data across the architecture. Use Purview’s data catalog and lineage features to maintain a comprehensive view of data assets and ensure compliance with data governance policies.


 Pros and Cons of the Solution


 Pros:

- Scalability: The architecture is highly scalable, capable of handling large volumes of unstructured data and supporting growing data needs.

- Flexibility: Combines the best of data lakes and data warehouses, allowing for flexible data storage, processing, and querying.

- Performance: Offers high-performance data processing and querying, ensuring timely insights and efficient data management.

- Integration: Seamlessly integrates with various Azure services, providing a unified platform for data management and analytics.

- Governance: Ensures data is well-governed and discoverable, supporting compliance and data quality.


 Cons:

- Complexity: The architecture involves multiple components and requires careful planning and management.

- Cost Management: While flexible, managing costs across different Azure services requires diligent monitoring and optimization.

- Learning Curve: Teams may need to acquire new skills to fully leverage the capabilities of Azure Databricks, Synapse Analytics, and Purview.


 Conclusion


Azure offers a robust set of tools for storing, processing, and exposing data for reporting, making it an ideal platform for organizations dealing with large volumes of unstructured data. By leveraging Azure Data Lake Storage Gen2, Azure Databricks, Azure Synapse Analytics, Power BI, and Azure Purview, businesses can build a scalable and efficient data architecture that supports complex data processing, interactive querying, and comprehensive data governance