Getting Started as Qubole User (Experience Learning)

Getting Started as Qubole User (Experience Learning)

Hands on Lab for Getting Started as Qubole User

About this course

Welcome to Experience Based Learning

We are excited to offer you the opportunity to practice the relevant concepts with hands on labs to facilitate experienced based learning. The labs can be launched using the link associated with the environment your company is using in production from the list below. In order to perform the labs your administrator will need to have set up and invited you to your company's training environment, typically called "qubole_training". Before you begin we recommend checking to confirm that you have a training account available for you to use.

 

Cannot Find Your Training Account?

If you have not yet been invited please contact your administrator for assistance - if you are unsure who your administrator is please contact your customer success manager. Once there is a training account you may select the hands on lab appropriate for you based on your Qubole deployment (AWS, Azure, GCP)

 

No Account Set Up?

If you are responsible for creating and managing your company's Qubole accounts please follow the steps below to configure a training account consistent with the instructions presented in the hands on labs. If you are unsure who has the permissions to complete this set up please contact your customer success manager.

  • Create a new Qubole Account and give it the title "qubole_training" so that all users can easily find the account. 
  • As the administrator create an isolated Hive metastore for the qubole_training account. This will allow users to practice creating and using schema tables for lab purposes. Create a hive database named “qubole_training” in the newly created and configured metastore - the lab instructions will direct users to write to this location. (For details on creating a custom metastore see: https://docs.qubole.com/en/latest/user-guide/hive/custom-hive-metastore.html)
  • Make sure qubole_training can read from the "paid-qubole" bucket as this is where the lab specific datasets reside and users will need to read from here for the labs.

AWS

If you are using S3 then the following changes will be necessary to the IAM Roles that govern the new qubole-training account so that users can access the lab datasets within paid-qubole.

 

{

"Effect": "Allow",

"Action": [

  "s3:GetObject",

"s3:ListBucket"

   ],

 "Resource": [

"arn:aws:s3:::paid-qubole/*",

  "arn:aws:s3:::paid-qubole"

  ]

  }

 

GCP

If you are using GCP no changes are necessary in order for your users to access the paid-qubole bucket and access the lab datasets. 

 

Azure

If you are using Azure no changes are necessary in order for your users to access the paid-qubole bucket and access the lab datasets. 

  • Make sure the following clusters exist and are named as such - hadoop2, spark, presto.
  • Using your cloud credentials make sure qubole_training can read / write to / from your company storage and compute locations so that users can launch clusters.

Once the above has been completed your training account will be fully set up - Happy Learning!

Curriculum

  • Qubole User (AWS) - Hands on Lab
  • Qubole User (GCP) - Hands on Lab
  • Qubole User (Azure) - Hands on Lab

About this course

Welcome to Experience Based Learning

We are excited to offer you the opportunity to practice the relevant concepts with hands on labs to facilitate experienced based learning. The labs can be launched using the link associated with the environment your company is using in production from the list below. In order to perform the labs your administrator will need to have set up and invited you to your company's training environment, typically called "qubole_training". Before you begin we recommend checking to confirm that you have a training account available for you to use.

 

Cannot Find Your Training Account?

If you have not yet been invited please contact your administrator for assistance - if you are unsure who your administrator is please contact your customer success manager. Once there is a training account you may select the hands on lab appropriate for you based on your Qubole deployment (AWS, Azure, GCP)

 

No Account Set Up?

If you are responsible for creating and managing your company's Qubole accounts please follow the steps below to configure a training account consistent with the instructions presented in the hands on labs. If you are unsure who has the permissions to complete this set up please contact your customer success manager.

  • Create a new Qubole Account and give it the title "qubole_training" so that all users can easily find the account. 
  • As the administrator create an isolated Hive metastore for the qubole_training account. This will allow users to practice creating and using schema tables for lab purposes. Create a hive database named “qubole_training” in the newly created and configured metastore - the lab instructions will direct users to write to this location. (For details on creating a custom metastore see: https://docs.qubole.com/en/latest/user-guide/hive/custom-hive-metastore.html)
  • Make sure qubole_training can read from the "paid-qubole" bucket as this is where the lab specific datasets reside and users will need to read from here for the labs.

AWS

If you are using S3 then the following changes will be necessary to the IAM Roles that govern the new qubole-training account so that users can access the lab datasets within paid-qubole.

 

{

"Effect": "Allow",

"Action": [

  "s3:GetObject",

"s3:ListBucket"

   ],

 "Resource": [

"arn:aws:s3:::paid-qubole/*",

  "arn:aws:s3:::paid-qubole"

  ]

  }

 

GCP

If you are using GCP no changes are necessary in order for your users to access the paid-qubole bucket and access the lab datasets. 

 

Azure

If you are using Azure no changes are necessary in order for your users to access the paid-qubole bucket and access the lab datasets. 

  • Make sure the following clusters exist and are named as such - hadoop2, spark, presto.
  • Using your cloud credentials make sure qubole_training can read / write to / from your company storage and compute locations so that users can launch clusters.

Once the above has been completed your training account will be fully set up - Happy Learning!

Curriculum

  • Qubole User (AWS) - Hands on Lab
  • Qubole User (GCP) - Hands on Lab
  • Qubole User (Azure) - Hands on Lab