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Scalable Data Analytics with Azure Data Explorer

Scalable Data Analytics with Azure Data Explorer

By : Jason Myerscough
4.8 (8)
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Scalable Data Analytics with Azure Data Explorer

Scalable Data Analytics with Azure Data Explorer

4.8 (8)
By: Jason Myerscough

Overview of this book

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters. The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance. By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.
Table of Contents (18 chapters)
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1
Section 1: Introduction to Azure Data Explorer
5
Section 2: Querying and Visualizing Your Data
11
Section 3: Advanced Azure Data Explorer Topics

Chapter 4: Ingesting Data in Azure Data Explorer

In the previous chapters, we created our Azure Data Explorer (ADX) clusters and databases, learned how to use the Data Explorer Web UI, executed our first Kusto Query Language (KQL) query. Now, we are ready to look at data ingestion in detail and start to ingest data. Data ingestion is the process of taking data from an external source and importing it into your big data solution, in our case, ADX. As you will soon see, once the data has been ingested, we can begin to analyze the data and generate visuals such as graphs and reports.

In this chapter, we will introduce data ingestion and discuss the different types of data (structured, semi-structured, and unstructured). Then we will examine the different data ingestion methods that ADX supports and learn how ADX ingests data via its Data management service, which we introduced in Chapter 1, Introducing Azure Data Explorer.

Next, we will learn about schema mapping, which is the process...

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