May 1, 2024
Updated July 6, 2025
13 minute read
Azure HDInsight is a managed, cloud-based service from Microsoft that enables businesses to efficiently process and analyze large volumes of data at scale using open-source frameworks like Apache Hadoop, Apache Spark, and Apache Hive.
Why Learn Azure HDInsight?
The increasing adoption of big data technologies and the growing need for real-time data analysis and insights have made Azure HDInsight a popular choice for businesses. Here are some reasons why you may want to learn about Azure HDInsight:
1i14x1|
Find a path to becoming a Azure HDInsight. Learn more at:
OpenCourser.com/topic/1i14x1/azure
Reading list
We've selected 30 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Azure HDInsight.
Offers a comprehensive guide to building and maintaining big data platforms on Azure. Written by a Microsoft data engineer, it provides practical guidance on infrastructure, orchestration, workloads, and governance. It's highly relevant for solidifying understanding of Azure data services and valuable reference for professionals. It covers data inventory, governance, quality, compliance, distribution, automated pipelines, ingestion, storage, and distribution, aligning well with the data engineering aspects of HDInsight.
Targeted specifically at the DP-203 certification, this guide provides comprehensive coverage of the exam objectives. It's valuable for those preparing for the certification and seeking in-depth knowledge of the Azure data stack. The book covers designing and implementing data lake solutions, partition strategies, Synapse Analytics, data transformations, using Azure Databricks/Synapse Spark, security, monitoring, and optimization. It's a strong resource for solidifying understanding and preparing for professional roles.
Another excellent resource for the DP-203 certification, this study guide offers a practical approach to preparing for the exam and a career in Azure data engineering. It covers all exam objectives and the roles and responsibilities of an Azure data engineer. The book includes study aids, practice questions, and electronic flashcards, making it a useful tool for both learning and exam preparation.
This cookbook provides a pragmatic, recipe-centered approach to various data engineering techniques in Azure. It's suitable for database administrators, developers, and ETL practitioners. The book offers practical solutions for common scenarios in building data engineering pipelines on Azure, including working with Azure Data Lake, Azure Data Factory, Azure SQL Database, Azure Databricks, and Azure Synapse Analytics. It's a useful reference tool for hands-on learning.
Focuses specifically on Azure HDInsight, covering the fundamentals of big data, Hadoop, and how HDInsight fits in. It delves into creating solutions with HDInsight and the Hadoop Ecosystem, including Hive, Pig, HBase, Storm, and Spark. The book provides real-world scenarios and code examples, making it valuable for gaining hands-on experience with HDInsight components.
Written by creators of Apache Spark, this book definitive resource for understanding and using Spark. As Spark key component of HDInsight, this book is highly relevant for deepening understanding of a core processing engine used on the platform. It covers Spark's structured APIs, Structured Streaming, and various operations.
Practical guide to Apache Spark, covering its core concepts, programming models, and advanced techniques. It is suitable for both beginners and experienced developers who want to learn how to use Spark for big data processing.
Covers designing and implementing robust data engineering solutions using a range of Azure services, including Data Factory, Databricks, Synapse Analytics, and Data Lake Storage Gen2. It emphasizes optimizing performance and scalability and includes topics like ELT, DevOps, and analytics. While one review notes potential issues with technical review and lack of code downloads, the subject matter is highly relevant to the topic.
This updated edition covers Spark 3.0 and good resource for understanding Spark's structured APIs and operations. As Spark core component of HDInsight, this book is valuable for users who want to delve deeper into Spark programming and optimization within the Azure environment.
Provides a comprehensive overview of Hadoop, covering its architecture, components, and ecosystem. It is suitable for beginners who want to learn about Hadoop from the ground up.
Provides a hands-on approach to data analytics using Hadoop and Spark. It covers topics such as data ingestion, data processing, and data analysis. It is suitable for data scientists and developers who want to learn how to use Hadoop and Spark for big data analytics.
Provides a practical guide to using Hadoop for big data processing. It covers topics such as data ingestion, processing, and analysis. It is suitable for data scientists and developers who want to learn how to use Hadoop for their big data projects.
Considered a classic in the big data space, this book provides a comprehensive introduction to Hadoop concepts and usage. While not Azure-specific, it's essential for understanding the underlying technology of HDInsight. It covers fundamental components like MapReduce, HDFS, and YARN, and is valuable for gaining prerequisite knowledge.
Provides a collection of recipes for common Hadoop operations tasks. It covers topics such as cluster management, data security, and performance tuning. It is suitable for system administrators and DevOps engineers who are responsible for managing Hadoop clusters.
This guide provides a practical look at Apache Kafka, a key technology for real-time data processing and streaming, which is available on HDInsight. It covers Kafka's design principles, APIs, and architecture. Understanding Kafka is crucial for working with streaming data scenarios on Azure HDInsight.
Provides a comprehensive overview of Apache Hadoop, covering its architecture, components, and ecosystem. It is suitable for beginners who want to learn about Hadoop from the ground up.
This cookbook provides recipes for accelerating and scaling real-time analytics solutions using Azure Databricks. It covers integrating with Azure services like Synapse Analytics and HDInsight Kafka Cluster, using Databricks SQL, and productionizing solutions with CI/CD. It's a practical reference for leveraging Databricks, which is closely related to the Spark capabilities within HDInsight.
Focuses on using Apache Spark with Azure Databricks, another analytics service on Azure that complements or can be used alongside HDInsight. It covers fundamentals of running analytics on large clusters in the cloud and introduces advanced topics like data lakes, data ingestion, and machine learning. It's relevant for understanding how Spark is leveraged in the Azure ecosystem.
For users looking to optimize their Spark workloads on HDInsight, this book provides best practices for scaling and performance tuning. It dives into more advanced topics related to Spark's internal workings and can help users get the most out of their HDInsight clusters.
Focuses on Azure Data Factory, a key service for orchestrating data movement and transformation on Azure. While not directly about HDInsight's processing engines, Data Factory is often used to ingest data into and move data out of HDInsight clusters. Understanding Data Factory is essential for building end-to-end data pipelines involving HDInsight.
Provides a strong foundation in the principles and practices of data engineering. While not Azure-specific, it covers essential concepts like planning and building robust data systems, which are crucial for working with platforms like HDInsight. It's valuable for gaining foundational knowledge in the field.
Focuses on using Apache Kafka for real-time data streaming, including setting up Kafka on public cloud offerings like Azure Event Hub (which has Kafka protocol support) and HDInsight Kafka. It's relevant for understanding how to leverage Kafka within the Azure ecosystem for streaming data scenarios.
Provides a foundational understanding of Azure data services, including storage, databases, and analytics. While not solely focused on HDInsight, it offers essential background knowledge for anyone starting with data on Azure. It's ideal for beginners and those preparing for the DP-900 Azure Data Fundamentals certification.
While broader than just HDInsight, this book covers using Azure Databricks for big data analytics with Spark and integrating with other Azure services like Azure Machine Learning and Azure Synapse. It provides context on how HDInsight fits into a larger data science and MLOps workflow on Azure. It's suitable for those looking to understand the broader ecosystem.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/1i14x1/azure
Save
