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Deploy a Hadoop cluster (Zookeeper, HDFS, YARN, Spark) with Cloudera Manager’s Python API. Hands on.
An excellent training about Software Engineering
Real World Hadoop – Automating Hadoop install with Python!
Note: This course is built on top of the “Real World Vagrant – Automate a Cloudera Manager Build – Toyin Akin” courseDeploy a Hadoop cluster (Zookeeper, HDFS, YARN, Spark) with Python! Instruct Cloudera Manager to do the work! Hands on. Here we use Python to instruct an already installed Cloudera Manager to deploy your Hadoop Services. The Cloudera Manager API provides configuration and service lifecycle management, service health information and metrics, and allows you to configure Cloudera Manager itself. The API is served on the same host and port as the Cloudera Manager Admin Console, and does not require an extra process or extra configuration. The API supports HTTP Basic Authentication, accepting the same users and credentials as the Cloudera Manager Admin Console. Here are some of the cool things you can do with Cloudera Manager via the API: Deploy an entire Hadoop cluster programmatically. Cloudera Manager supports HDFS, MapReduce, YARN, ZooKeeper, HBase, Hive, Oozie, Hue, Flume, Impala, Solr, Sqoop, Spark and Accumulo. Configure various Hadoop services and get config validation. Take admin actions on services and roles, such as start, stop, restart, failover, etc. Also available are the more advanced workflows, such as setting up high availability and decommissioning. Monitor your services and hosts, with intelligent service health checks and metrics. Monitor user jobs and other cluster activities. Retrieve timeseries metric data. Search for events in the Hadoop system. Administer Cloudera Manager itself. Download the entire deployment description of your Hadoop cluster in a json file. Additionally, with the appropriate licenses, the API lets you: Perform rolling restart and rolling upgrade. Audit user activities and accesses in Hadoop. Perform backup and cross data-center replication for HDFS and Hive. Retrieve per-user HDFS usage report and per-user MapReduce resource usage report. Here I present a curriculum as to the current state of my Cloudera courses. My Hadoop courses are based on Vagrant so that you can practice and destroy your virtual environment before applying the installation onto real servers/VMs. For those with little or no knowledge of the Hadoop eco systemUdemy course: Big Data Intro for IT Administrators, Devs and Consultants.I would first practice with Vagrant so that you can carve out a virtual environment on your local desktop. You don’t want to corrupt your physical servers if you do not understand the steps or make a mistake. Udemy course: Real World Vagrant For Distributed Computing.I would then, on the virtual servers, deploy Cloudera Manager plus agents. Agents are the guys that will sit on all the slave nodes ready to deploy your Hadoop servicesUdemy course: Real World Vagrant – Automate a Cloudera Manager Build. Then deploy the Hadoop services across your cluster (via the installed Cloudera Manager in the previous step). We look at the logic regarding the placement of master and slave services. Udemy course: Real World Hadoop – Deploying Hadoop with Cloudera Manager. If you want to play around with HDFS commands (Hands on distributed file manipulation).Udemy course: Real World Hadoop – Hands on Enterprise Distributed Storage. You can also automate the deployment of the Hadoop services via Python (using the Cloudera Manager Python API). But this is an advanced step and thus I would make sure that you understand how to manually deploy the Hadoop services first. Udemy course: Real World Hadoop – Automating Hadoop install with Python. There is also the upgrade step. Once you have a running cluster, how do you upgrade to a newer hadoop cluster (Both for Cloudera Manager and the Hadoop Services).Udemy course: Real World Hadoop – Upgrade Cloudera and Hadoop hands on
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