Hadoop and Spark Analytics Training
In this course, explore big data, big data analytics, and cloud computing on the Microsoft Azure cloud platform
Development ,Data Science,Big Data
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 will be covering all the big data analytics services that 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 will able to use many big data tools like HDFS, YARN, MapReduce, Hive, Pig and many other tools that 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 complete the course you will be able to find which one is better: Hadoop or Spark. Also, we would use different notebooks like Zapelline, Jupyter, etc as wells as a use case of stream analytics.
Goals
- Learn the basic concepts of Big Data.
- Learn how to use mostly widely used tools for working with big data... Hadoop and Spark.
- Confidently work with various Big Data Analytics services on Microsoft Azure.
Prerequisites
- 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
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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