Hadoop Tutorials

Course Overview

Hadoop Tutorial - Inaugural.
Hadoop Tutorial - Introduction.
Hadoop Tutorial - HDFS Features.
Hadoop Tutorial - Architecture.
Hadoop Tutorial - High Availability, Fault Tolerance & Secondary Name Node.
Hadoop Tutorial - Installing a Hadoop Cluster.
Hadoop Tutorial - HDFS Commands.
Hadoop Tutorial - The Map Reduce.
Hadoop Tutorial - Map Reduce Examples - Part 1.
Hadoop Tutorial - Map Reduce Examples - Part 2.
Hadoop Tutorial - Map Reduce Examples - Part 3.
Hadoop Tutorial - The YARN.
Hadoop Tutorial - File Permission and ACL.
Hadoop Tutorials - Kerberos Authentication - Part 1.
Hadoop Tutorials Kerberos Authentication - Part 2.
Hadoop Tutorials - Kerberos Integration - Final.
Google Cloud Tutorial - Hadoop | Spark Multinode Cluster | DataProc.

(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
1. Learners can obtain an understanding of the fundamentals of Hadoop, including its architecture, features, and components. They will learn about the HDFS, MapReduce, and YARN, and how they work together to provide a distributed computing platform. They will also learn about the different types of file permissions and access control lists (ACLs) that can be used to secure data.

2. Learners can gain an understanding of how to install and configure a Hadoop cluster, as well as how to use HDFS commands to manage data. They will also learn how to use MapReduce to process data and how to use YARN to manage resources.

3. Learners can gain an understanding of how to use MapReduce to write programs to process data. They will learn how to write MapReduce programs to perform various tasks, such as sorting, filtering, and aggregating data. They will also learn how to use MapReduce to perform complex tasks, such as machine learning algorithms.

4. Learners can gain an understanding of how to use Kerberos to authenticate users and secure data. They will learn how to configure Kerberos to authenticate users and how to use Kerberos to secure data. They will also learn how to integrate Kerberos with Hadoop and how to use Kerberos to secure data in the cloud.

5. Learners can gain an understanding of how to use Google Cloud to deploy a Hadoop and Spark cluster. They will learn how to use Google Cloud to create a cluster and how to use DataProc to manage the cluster. They will also learn how to use the cluster to process data and how to use the cluster to run machine learning algorithms.

[Applications]
After completing this course, users should be able to apply the knowledge gained to create and manage a Hadoop cluster, use HDFS commands, and understand the Map Reduce and YARN frameworks. Additionally, users should be able to use Kerberos authentication and integrate it with Hadoop, as well as use Google Cloud to create a Hadoop | Spark Multinode Cluster | DataProc.

[Career Paths]
1. Hadoop Developer: Hadoop Developers are responsible for designing, developing, and maintaining Hadoop applications. They must have a strong understanding of the Hadoop architecture and be able to write code in Java, Python, and other programming languages. The demand for Hadoop Developers is increasing as more organizations are adopting Hadoop for their data processing needs.

2. Big Data Engineer: Big Data Engineers are responsible for designing, developing, and maintaining Big Data solutions. They must have a strong understanding of the Hadoop architecture and be able to write code in Java, Python, and other programming languages. They must also be able to work with other technologies such as Apache Spark, Apache Kafka, and Apache Flink.

3. Data Scientist: Data Scientists are responsible for analyzing large datasets and extracting insights from them. They must have a strong understanding of statistics, machine learning, and data visualization. The demand for Data Scientists is increasing as more organizations are leveraging data-driven insights to make better decisions.

4. Cloud Architect: Cloud Architects are responsible for designing, developing, and maintaining cloud-based solutions. They must have a strong understanding of cloud computing technologies such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The demand for Cloud Architects is increasing as more organizations are migrating their applications to the cloud.