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

Choosing a data load strategy

Two of the key parameters you have to consider when you design your data load strategy are your tolerance for latency and the data volume that you will process.

In some scenarios, your business may require seeing the latest data available as soon as possible to enable quick decision-making, while in others you may be able to work with data from a day, or a few days ago, to perform analysis on historical data. Working with low latency in data ingestion implies inserting smaller chunks of data more frequently, while at a higher latency, you will insert larger volumes of data maybe once a day, or a few times per day.

To address these scenarios, Azure Synapse Data Explorer works with two ingestion strategies: streaming ingestion and batching ingestion. Let’s look at these strategies in detail.

Streaming ingestion

When real-time analytics is a hard business requirement, you will most likely implement a streaming ingestion strategy. This strategy...