Book Image

Scalable Data Analytics with Azure Data Explorer

By : Jason Myerscough
Book Image

Scalable Data Analytics with Azure Data Explorer

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)
1
Section 1: Introduction to Azure Data Explorer
5
Section 2: Querying and Visualizing Your Data
11
Section 3: Advanced Azure Data Explorer Topics

Calculating moving averages with KQL

There may be instances where your time series data is clean and all the components such as seasonality, trends, and variations are visible to the point that you can confidently make decisions based on the data without having to manipulate or clean the data. In reality, there will be noise and variations in the data that may obfuscate patterns and anomalies. KQL provides a rich set of functions for analyzing time series data. One subset of those functions is for calculating moving averages. Moving averages allow us to remove noise and smoothen our data.

The goal of this section is to learn how to use series_fir() to calculate moving averages to smoothen our data. Finite Impulse Response (FIR) is a filtering technique that is commonly used in signal processing and time series.

As you may recall, in Chapter 6, Introducing Time Series Analysis, we used demo_make_series1, which is a table in the help cluster (https://help.kusto.windows.net) to...