Book Image

Learn Azure Synapse Data Explorer

By : Pericles (Peri) Rocha
Book Image

Learn Azure Synapse Data Explorer

By: Pericles (Peri) Rocha

Overview of this book

Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data. This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you’ll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you’ll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users. By the end of this book, you’ll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data.
Table of Contents (19 chapters)
1
Part 1 Introduction to Azure Synapse Data Explorer
6
Part 2 Working with Data
12
Part 3 Managing Azure Synapse Data Explorer

Enabling real-time analytics with big data

Performing analytics by streaming data from applications at large volumes is a common scenario for companies that need to make quick decisions based on events that are happening in real time. The idea behind real-time analytics is that companies can make decisions soon after the data event has been captured. Here are a few examples:

  • Detecting patterns on financial transactions: The first blueprint in this chapter discussed using Data Explorer pools and Azure Machine Learning to detect credit card fraud. In financial services, however, there are several other opportunities to employ real-time analytics and machine learning for other scenarios, such as providing financial advice and risk assessments based on current market situations.
  • Optimizing retail websites based on user flow: This scenario can be used to read log data from web applications that describe how an e-commerce website is being used in response to promotions and, with...