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

Data Analysis and Exploration with KQL and Python

Now that you’ve learned how to ingest data into Data Explorer pools, let’s look at ways to analyze this data to extract the insights you need to support a decision-making process. Part of the data analysis process is to explore your data, see its shape, and adjust it to make it more useful for you and other consumers of this data. There’s no unique way to analyze data, so in this chapter, you will explore different means to achieve this task.

The chapter starts with an overview of data analysis using Kusto Query Language (KQL). You will look at examples to help retrieve data, summarize it, visualize it in simple charts, and make sense of the data by looking at its distribution. Before you move on from KQL, you will look at some quick examples to help you detect outliers in your data and use linear regression to fit the best line that represents your data.

Next, you will work with Python on Azure Synapse notebooks...