Data Engineering Made Easy

Introduction

This article, where I aim to highlight innovative data management solutions using the powerful features offered by Microsoft’s Azure platform. My aim is to demonstrate how Azure can be a key ally in creating efficient and scalable data solutions. From data modeling to the implementation of complete data pipelines, this article explores each step of the process, providing valuable insights into how to make the most of Azure services to meet the growing demands of data analysis and processing. With a focus on data modeling and the implementation of automated pipelines, this article aims to provide a comprehensive overview of the best practices and strategies for handling data efficiently and productively in the Azure cloud environment.

This facilitates detailed analysis of the implementation of these solutions, providing a comprehensive understanding of the process.

Denormalized vs. Normalized Data:

It is initially observed that it follows a normalized structure, with separate tables for each entity and relationships defined by foreign keys. This format is valuable in transactional environments, where the priority is data integrity and storage efficiency. However, when bringing this data into an analysis environment, such as a Business Intelligence (BI) system, normalization can present challenges. The need to perform multiple joins to access relevant information can slow down query performance and make it difficult for end users to understand the data.

On the other hand, by denormalizing the dataset, combining related tables into broader, denormalized structures, we can simplify analysis and improve query performance. This is especially useful in BI environments, where the emphasis is on being able to quickly retrieve meaningful insights. By creating dimensions and a fact table in star schema format, we make it easier to navigate and visualize data, allowing users to explore relationships more intuitively and efficiently. This denormalized approach therefore provides a solid foundation for data analysis and the generation of valuable business insights.

Step by step through the Azure environment:

Before we dive into the detailed step-by-step, it’s important to note that this article takes a practical approach, providing clear and concise instructions on how to implement a data management solution using Azure resources. From setting up the initial environment to configuring automated data pipelines, each step will be carefully outlined to provide a comprehensive overview of the process.

• Create resource group: Here, we establish a logical grouping to organize and manage all the resources related to the project, including subscription definitions, region, and spending control.
• Create the storage account (Blob Storage): This step involves creating a storage service in Azure, enabling the hierarchical namespace to take advantage of the advanced features of Data Lake Gen2, allowing efficient and scalable management of large volumes of data.
• Make the connection between Data Lake Gen2 and Databricks: We have established a direct integration between Data Lake Gen2 and Databricks, allowing access to the data stored in the Data Lake for analysis and processing in the Databricks environment.
• Create the application registry: Essential for secure integration between services, the application registry allows you to assign specific permissions for access to resources, guaranteeing data security and governance.
• IAM and ACL access control: We implement access control policies based on Identity and Managed Access (IAM) and Access Control Lists (ACL), ensuring that only authorized users have access to the necessary resources.
• Provision and configure Databricks: We set up a robust data analysis environment, provisioning and tuning Azure Databricks to run distributed processing operations, enabling DBFS and creating clusters to support scalable workloads.
• Data access: We developed a script to set up Databricks’ distributed file system (DBFS), making it easier to access and manipulate data stored in different sources and formats.
• Creating folders in Data Lake Gen2: We organized data storage in Data Lake Gen2, creating folder structures such as Inbound (for raw data), Bronze (for data already transformed into star schema format) and Silver (for aggregated data), ensuring efficient and scalable data organization.

• Creation of notebooks for data processing: We develop notebooks in Databricks to perform data processing, transforming and moving data between Data Lake folders, ensuring that data is properly prepared for analysis and visualization.
• Creation of a function inserted into notebooks to transform the table to Delta: Delta tables offer complete ACID transactions and native support for version history, guaranteeing efficiency, reliability and, optimization in data operations.

• Provisioning and configuring Azure Data Factory: We configure Azure Data Factory to orchestrate and automate the flow of data between the different stages of the pipeline, ensuring efficient and reliable execution of data processing tasks.
• Connecting Databricks notebooks to the pipeline in Azure Data Factory: We set up the integration between Databricks notebooks and the pipeline in Azure Data Factory, allowing the notebooks to run as part of the data processing workflow.
• Creation of a trigger to start the pipeline: We configured a trigger in Azure Data Factory to start the pipeline automatically every hour, ensuring the regular and scheduled execution of data processing tasks.

• Connecting Power BI Desktop to Data Lake Gen 2 using an access token: We established a direct connection between Power BI Desktop and Data Lake Gen 2 using an access token, allowing data stored in Data Lake to be extracted and analyzed directly in Power BI.

• Building a simple dashboard using data from the Silver layer: We developed a basic dashboard in Power BI using the processed and aggregated data from the Silver layer, providing a clear and concise visualization of the insights generated from the data stored in the Data Lake.
• Creation of a dashboard in Power BI: — A bubble chart showing the movie categories in relation to the length of the rental, where the size of the bubbles is proportional to the sum of the rental values. — A scatter plot showing the relationship between categories and sales value. — A simple bar chart showing the value of sales according to the "Is it a weekend or not?" feature.

Conclusion:

In completing this end-to-end project, we highlighted the importance of a solid, well-planned architecture when implementing data management solutions in the Azure cloud. From initial data modeling to building automated pipelines and creating interactive dashboards, each stage was carefully designed to ensure the system’s efficiency and scalability. Using resources such as Azure Databricks, Azure Data Factory and, Power BI, we were able to create a robust architecture that allows data to be ingested, transformed, analyzed and, visualized in an integrated and effective way. In addition, the integration of Delta tables provided greater reliability and flexibility for data operations, demonstrating not only the versatility of the tools offered by Azure but also the importance of a holistic approach in creating modern and efficient data solutions.