A definitive guide to turn CSV files into Power BI visuals using Azure

A definitive guide to turn CSV files into Power BI visuals using Azure

A definitive step-by-step guide to turn COVID-19 CSV data into stunning Power BI visuals using Microsoft Azure data platform technologies. In this article, we look at a holistic approach of transforming and loading data in Azure as we move through different phases of ELT until consumption with the aid of an example.

Cloud, Big Data, and Business Intelligence are the three buzz words of the decade. Everyone is talking about them. Everyone wants to do it. But no one tells you how to do it. How do you use the cloud to process your big data and build _intelligence _around it to make _business _decisions? There are multiple answers to that question, and in this guide, we tried to answer that question using Microsoft’s cloud solution (Azure) and Microsoft’s BI tool (Power BI) to get you started in the right direction.

Microsoft Azure is one of the leading providers of cloud solutions, offering an end-to-end set of tools and techniques to ingest, analyze, and consume vast sources and formats of data.

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Caution

Microsoft Azure is a paid service, and following this article can cause financial liability to you or your organization.

Prerequisites

An active Microsoft Azure subscription

How to get an Azure subscription?

The very first step in the journey towards representing data in an easy, digestive, and usable form is recognizing the source and format of the data. Traditionally data professionals used to focus on Extract, Transform & Load (ETL) to load and transform data. The advent of Azure has opened the doorway to process structure-less data on an unlimited and unprecedented scale. This change has shifted the transformation and loading data to Extract, Load & Transform (ELT). The basic principles and steps remain the same; they just follow a different order. A data project in Azure typically involves the following steps:

  1. Ingest: Identify the tools, technologies, and method to load the data
  2. Prep and train: Identify the tools, technologies, and method to transform the data

Followed by two additional steps to analyze and consume the cleansed data

  • Model and serve: Identify the tools and methods to model and analyze the data
  • Consume: Identify the tools and techniques to consume or present the data

In this article, we look at a holistic approach of transforming and loading data in Azure as we move through different phases of ELT until consumption with the aid of an example. To begin our journey, we will:

  • Take publicly available COVID-19 data from GitHub (source)
  • Store the CSV files to Azure Data Lake Storage Gen2 with the help of Azure Data Factory (ingest)
  • Transform and cleanse the CSV files to relational data in Azure Databricks (prep and train)
  • Store the cleansed data in Azure Synapse Analytics data warehouse (model and serve)
  • And finally, present the prepared data in the form of Power BI visuals (consume)

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