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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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:
Followed by two additional steps to analyze and consume the cleansed 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:
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.