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Updated in [March 06th, 2023]

This four-week course, DAT202.2x: Implementing Real-Time Analytics with Hadoop in Azure HDInsight, provides an introduction to real-time analytics and streaming data solutions using Hadoop technologies like HBase, Storm, and Spark on Microsoft Azure HDInsight. Learners will gain an understanding of the fundamentals of real-time analytics and streaming data solutions, and how to implement them using Hadoop technologies.

The course begins with an overview of real-time analytics and streaming data solutions, and then dives into the details of how to implement them using HBase, Storm, and Spark on Microsoft Azure HDInsight. Learners will gain hands-on experience with the technologies by completing a series of lab exercises.

At the end of the course, learners will have a solid understanding of the fundamentals of real-time analytics and streaming data solutions, and how to implement them using Hadoop technologies. They will also have the skills and knowledge to develop their own real-time analytics and streaming data solutions.

To complete the hands-on elements in this course, learners will require an Azure subscription and a Windows, Linux, or Mac OS X client computer. They can sign up for a free Azure trial subscription (a valid credit card is required for verification, but they will not be charged for Azure services). Note that the free trial is not available in all regions. It is possible to complete the course and earn a certificate without completing the hands-on practices.

This course is the second in a series that explores big data and advanced analytics techniques with HDInsight; and builds on the batch processing techniques learned in DAT202.1x: Processing Big Data with Hadoop in Azure HDInsight.

[Applications]
After completing this course, learners can apply the knowledge gained to develop real-time analytics solutions using Hadoop technologies on Microsoft Azure HDInsight. Learners can also use the skills acquired to develop batch processing solutions using Hadoop technologies on Microsoft Azure HDInsight.

[Career Paths]
1. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover trends and insights. They use a variety of tools and techniques, including machine learning, to develop predictive models and uncover hidden patterns. Data Scientists are in high demand, and the demand is expected to continue to grow as organizations become increasingly data-driven.

2. Big Data Engineer: Big Data Engineers are responsible for designing, building, and maintaining the infrastructure and systems that store and process large datasets. They use a variety of technologies, including Hadoop, Spark, and NoSQL databases, to create scalable and reliable data pipelines. Big Data Engineers are in high demand, and the demand is expected to continue to grow as organizations become increasingly data-driven.

3. Business Intelligence Analyst: Business Intelligence Analysts are responsible for analyzing data to identify trends and insights that can be used to inform business decisions. They use a variety of tools and techniques, including data visualization, to uncover patterns and insights. Business Intelligence Analysts are in high demand, and the demand is expected to continue to grow as organizations become increasingly data-driven.

4. Data Architect: Data Architects are responsible for designing and implementing data architectures that are optimized for performance, scalability, and reliability. They use a variety of technologies, including Hadoop, Spark, and NoSQL databases, to create data architectures that meet the needs of the organization. Data Architects are in high demand, and the demand is expected to continue to grow as organizations become increasingly data-driven.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, such as programming, algorithms, data structures, and software engineering. It also covers topics related to big data and analytics, such as machine learning, artificial intelligence, and data mining. This degree path is becoming increasingly popular as the demand for data-driven solutions grows.

2. Master of Science in Data Science: This degree path focuses on the application of data science techniques to solve real-world problems. It covers topics such as data mining, machine learning, artificial intelligence, and predictive analytics. This degree path is becoming increasingly popular as organizations look to leverage data to gain insights and make better decisions.

3. Master of Science in Business Analytics: This degree path focuses on the application of analytics techniques to business problems. It covers topics such as data mining, predictive analytics, and optimization. This degree path is becoming increasingly popular as organizations look to leverage data to gain insights and make better decisions.

4. Master of Science in Artificial Intelligence: This degree path focuses on the application of artificial intelligence techniques to solve real-world problems. It covers topics such as machine learning, natural language processing, and computer vision. This degree path is becoming increasingly popular as organizations look to leverage AI to gain insights and make better decisions.