Since the advent of time, it has always been a core human desire to look beyond the present and try to forecast the future. This form of analysis further enhances the decision support mechanisms for users, as illustrated in the following diagram:įigure 1.2 – The evolution of data analyticsīoth descriptive analysis and diagnostic analysis try to impact the decision-making process using factual data only. An example scenario would be that the sales of a company sharply declined in the last quarter because there was a serious drop in inventory levels, arising due to floods in the manufacturing units of the suppliers. The core analytics now shifted toward diagnostic analysis, where the focus is to identify anomalies in data to ascertain the reasons for certain outcomes. Very quickly, everyone started to realize that there were several other indicators available for finding out what happened, but it was the why it happened that everyone was after. A hypothetical scenario would be that the sales of a company sharply declined within the last quarter. This type of analysis was useful to answer question such as " What happened?". For many years, the focus of data analytics was limited to descriptive analysis, where the focus was to gain useful business insights from data, in the form of a report. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way.īy the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.ĭata analytics has evolved over time, enabling us to do bigger and better. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes.
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