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Explores a topic that is popular in enterprise cloud management
Focuses on security, cluster reliability, and monitoring, topics that are essential for cloud management
Provides guidance on selecting open source analytics frameworks, storage solutions, and ISV applications
Shares common patterns and best practices for building secure, performant, and cost efficient big data analytics pipelines
Discusses emerging customer scenarios, equipping learners to stay ahead of industry trends

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Career center

Learners who complete Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their knowledge of statistical methods, machine learning, and other data analysis techniques to build models and algorithms that can help businesses make better decisions. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can help build a foundation for a career as a Data Scientist by providing a deep understanding of statistical methods and tools. The course also covers real-world use cases, which can help students develop their problem-solving skills and learn how to build and deploy models and algorithms.
Big Data Architect
Big Data Architects design, build, and maintain big data solutions. They use their knowledge of big data technologies, cloud computing, and data engineering to develop and implement big data solutions.
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They use their knowledge of machine learning algorithms, big data technologies, and cloud computing to develop and implement machine learning solutions.
Data Engineer
Data Engineers design, build, and maintain data systems and infrastructure. They use their knowledge of data engineering principles, big data technologies, and cloud computing to develop and implement data solutions. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can provide Data Engineers with a deep understanding of big data technologies and cloud computing. The course also covers real-world use cases, which can help Data Engineers develop their problem-solving skills and learn how to design and implement data solutions.
Data Analyst
Data Analysts collect, clean, process, and analyze data to help businesses understand trends, patterns, and opportunities. They use their knowledge of statistical methods and tools to identify insights and make recommendations based on data. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can provide essential skills for a career as a Data Analyst by teaching students how to collect, clean, and analyze data using open source analytics tools.
Risk Analyst
Risk Analysts use their knowledge of risk management, data analysis, and modeling to help businesses identify, assess, and mitigate risks. They use their skills in data analysis and forecasting to develop models and recommendations that can help businesses make better decisions about risk.
Financial Analyst
Financial Analysts use their knowledge of financial markets, accounting, and economics to help businesses make investment decisions. They use their skills in data analysis and forecasting to develop models and recommendations that can help businesses maximize their returns. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can provide Financial Analysts with a deeper understanding of statistical methods and tools. The course also covers real-world use cases, which can help Financial Analysts develop their problem-solving skills and learn how to apply statistical methods to financial problems.
Operations Analyst
Operations Analysts use their knowledge of business processes, data analysis, and operations management to help businesses improve their efficiency and effectiveness. They use their skills in data analysis and forecasting to identify opportunities and develop solutions that can help businesses reduce costs, improve quality, and increase productivity. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can provide Operations Analysts with a deep understanding of statistical methods and tools. The course also covers real-world use cases, which can help Operations Analysts develop their problem-solving skills and learn how to apply statistical methods to operations problems.
Statistician
Statisticians collect, analyze, interpret, and present data to help make informed decisions. They use their knowledge of statistical methods to design and conduct surveys, experiments, and other studies. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can help build a foundation for a career as a Statistician by providing a deep understanding of statistical methods and tools. The course also covers real-world use cases, which can help students develop their problem-solving skills and learn how to apply statistical methods to real-world problems.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages, software engineering principles, and cloud computing to develop and implement software solutions. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can provide Software Engineers with a deep understanding of big data technologies and cloud computing. The course also covers real-world use cases, which can help Software Engineers develop their problem-solving skills and learn how to design and implement software solutions.
Database Administrator
Database Administrators design, implement, and maintain databases. They use their knowledge of database management systems, data structures, and data security to ensure that databases are reliable, performant, and secure.
Cloud Architect
Cloud Architects design, build, and maintain cloud computing solutions. They use their knowledge of cloud computing platforms, services, and security to develop and implement cloud solutions.
Marketing Analyst
Marketing Analysts use their knowledge of marketing principles, data analysis, and consumer behavior to help businesses develop and implement marketing campaigns. They use their skills in data analysis and forecasting to identify opportunities and develop strategies that can help businesses reach their target audience. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can enhance a Marketing Analyst's skillset by providing a deep understanding of statistical methods and tools. The course can also help Marketing Analysts develop their problem-solving skills and learn how to apply statistical methods to marketing problems.
Data Visualization Engineer
Data Visualization Engineers design and develop data visualizations. They use their knowledge of data visualization tools and techniques to create data visualizations that are clear, concise, and informative.
Business Analyst
Business Analysts help businesses understand their operations and make better decisions. They use their knowledge of business processes, data analysis, and financial analysis to identify opportunities and develop solutions. Gaining Business Insights with Open Source Analytics on Azure HDInsight: Patterns and Best Practices can enhance a Business Analyst's skillset by providing a deep understanding of statistical methods and tools. The course can also help Business Analysts develop their problem-solving skills and learn how to apply statistical methods to business problems.

Reading list

We haven't picked any books for this reading list yet.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
An older title specifically about HDInsight on Windows Azure (the previous name for Microsoft Azure), this book provides a historical perspective and covers deploying and using Hadoop on the platform. While dated due to the evolution of Azure and HDInsight, it can offer insights into the origins and core concepts of HDInsight. It's more valuable as additional historical reading than a current reference.
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.

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