Big Data Analytics on Microsoft AZURE (Udemy.com)
HDInsight | Spark | Jupyter | Zappelin | Stream Analytics | Lake Analytics | Data Bricks
Created by: Pranjal Srivastava
Last updated May 2022
What you will learn
- In this course we would explore various Big Data Analytics services available on Microsoft Azure cloud.
- You would learn HDInsight, Apache Spark, Jupyter
- You would also learn about Zappelin, Stream Analytics
- And Lake Analytics, Data Bricks.
How it compares
Against the other 371 rated Udemy courses in Data Analysis on CourseDuck:
- Rating3.8 out of 5higher than 1% of them
- Reviews58#357 of 372 by number of reviews
- Length1.9 hoursshorter than 81% of them
- Last updatedMay 2022earlier than 79% of them
Course Description
In this course you will be learning various cloud Analytics options available on Microsoft Azure cloud platform. When you are building your career around Cloud computing, with a developer profile, you may be expected to handle big data in this ever-growing world of data. When big data and analytics is your prime domain, you can empower yourself with Data analytics services and tools provided on the cloud platforms, specifically Microsoft Azure. You will be learning following Data Analytics services with few hands-on practical examples.
We would be creating resources for Stream Analytics, Spark, HDInsight exploring options. Below is a list of Big data analytics services on Azure:
What is HDInsight?
It is a cloud distribution of Hadoop components. Azure HDInsight makes it easy, fast, and cost-effective to process massive amounts of data. You can use the most popular open-source frameworks such as Hadoop, Spark, Hive, LLAP, Kafka, Storm, R, and more.
What is Stream Analytics?
It is a real-time analytics service that is designed for mission-critical workloads. You can build an end-to-end serverless streaming pipeline with this service on Azure.
What is Lake Analytics?
It is an on-demand analytics job service that simplifies big data. Easily develop and run massively parallel data transformation and processing programs in U-SQL, R, Python, etc.
What is Data Bricks?
It provides data science and data engineering teams with a fast, easy and collaborative Spark-based platform on Azure. You can use data bricks for Big Data processing and Machine Learning.
Instructor Details
- 3.8 Rating
58 Reviews
Pranjal Srivastava
I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer.
I have authored 78 courses with over 115,000 students worldwide across 175+ countries on wide array of technologies like devops, containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.
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Reviews
By Haritha on 6/18/2020
voice is not clean and the cource is full theoritical
By Goli Aapte on 5/14/2020
it made easy learning to handle big data on cloud.
By Pritam on 2/18/2020
Very Poor Quality. Straight reading from Books. No innovative way to explain things. Worst Teaching methods
By Balaji Challuru on 11/9/2019
Not worth a penny. It's a waste of time. The tutor simply reads the slides without providing an explanation.
By Matt Jose on 10/21/2019
I work as a tester in agile team at my company, i was fascinated to learn Big data analytics on Cloud from starting but not going just theoratically but wanted to learn with some real stuff. This course prooved to be my guide, and i asked a lot of question to intructor while learning, he always replied within 24 hours. Sometimes i had doubts while watching lectures, that i cleared by asking him.
By Andre Sergei on 10/20/2019
better than some other courses on same topic
By Yuti hirota on 10/19/2019
I have completed about 70% of course, till now it is good.
By Robert Leon on 10/18/2019
I am glat to get this career oriented technical course. topics are clear and easy to understand.
By Yasin Bhutto on 10/14/2019
a technical course with relevant topics.
By Kumar BLN on 8/26/2019
Course is good and provided overview of Azure Analytics.
Quality Score
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Overall Score : 76 / 100












