Learn Hadoop and Spark analytics
On Microsoft Azure using HdInsights
IT & Software,IT certifications,Data Science,Hadoop
Lectures -13
Duration -1 hours
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Course Description
In this course, we are going to explore big data, big data analytics and cloud computing on the Microsoft Azure cloud platform.
Here we would be covering all the big data analytics services which are available on Azure. Firstly we would explore HDinsight services where we would go to create clusters and also explore different cluster configurations. Once the cluster is ready we would able to use many big data tools like HDFS, YARN, MapReduce, Hive, Pig and many other tools which come under the Hadoop ecosystem.
Then we would also explore Spark another open-source distributed cluster-computing framework. Spark is a data processing engine developed to provide faster and easy-to-use analytics than Hadoop MapReduce.
Once you would complete the course you would be able to find which one is better: Hadoop or Spark
EXTRAS
Also, we would use different notebooks like Zapelline, Jupyter, etc as wells as a use case of stream analytics
Goals
What will you learn in this course:
- Learn the basics concepts of Big Data.
- Learn how to use mostly widely used tool for working with big data... Hadoop and Spark
- Confidently work with various Big Data Analytics services on Microsoft Azure
Prerequisites
What are the prerequisites for this course?
- A basic understanding of cloud computing and big data
Curriculum
Check out the detailed breakdown of what’s inside the course
HDInsight
5 Lectures
- Data analytics 08:20 08:20
- Hdinsight Part-1 05:00 05:00
- Hdinsight Part-2 12:11 12:11
- Jupyter 06:39 06:39
- Zappelin 09:21 09:21
Spark Analysis
1 Lectures
Streaming Analysis
6 Lectures
Instructor Details
Pranjal Srivastava
SME of AWS & IBM CloudI am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer.
I have authored 60+ courses on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.
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