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https://issues.apache.org/jira/browse/DATALAB-2646?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vira Vitanska updated DATALAB-2646:
-----------------------------------
Description:
As a user I want to use HDInsight (Data Engine Service) on Azure in DataLab, so
that I can simplify running big data frameworks.
----
[[Compute Engine] Feature: New Compute Engine - Azure HDInsight - EPMA-DPAF -
EPAM Knowledge
Base|https://kb.epam.com/display/EPMADPAF/%5BCompute+Engine%5D+Feature%3A+New+Compute+Engine+-+Azure+HDInsight]
User should be able to create HDInsight for the next types of notebooks:
Apache Zeppelin, RStudio, Jupyter.
*{color:#de350b}Creation popup is below (it should be discussed by DevOps and
Back-end):{color}*
{color:#de350b}1. Does HDInsight has one or two masters?{color}
{color:#de350b}2. If HDInsight has two master do we allow user to choose slave
for each master node?{color}
{color:#de350b}3. Total instance number does include master (1/2)?{color}
{color:#de350b}4. What can be max instance number for HDInsight?{color}
{color:#de350b}5. Does HDInsight has particular trait which is supposed to be
portrayed on WEB DataLab UI?{color}
!image-2022-07-15-14-40-44-836.png!
*{color:#de350b}Instance (master/slave) size (it should be discussed by DevOps
and Back-end):{color}*
Memory optimized:
*S* Standard_E4s_v3
*M* Standard_E16s_v3
*L* Standard_E32s_v3
Compute optimized:
*S* Standard_F2s
*M* Standard_F8s
*L* Standard_F16s
was:
As a user I want to use HDInsight (Data Engine Service) on Azure in DataLab, so
that I can simplify running big data frameworks.
----
[[Compute Engine] Feature: New Compute Engine - Azure HDInsight - EPMA-DPAF -
EPAM Knowledge
Base|https://kb.epam.com/display/EPMADPAF/%5BCompute+Engine%5D+Feature%3A+New+Compute+Engine+-+Azure+HDInsight]
User should be able to create HDInsight for the next types of notebooks:
Apache Zeppelin, RStudio, Jupyter.
*{color:#de350b}Creation popup is below (it should be discussed by DevOps and
Back-end):{color}*
{color:#de350b}1. Does HDInsight has one or two masters?{color}
{color:#de350b}2. If HDInsight has two master do we allow user to choose slave
for each master node?{color}
{color:#de350b}3. Total instance number does include master (1/2)?{color}
{color:#de350b}4. What can be max instance number for HDInsight?{color}
{color:#de350b}5. Does HDInsight has particular trait which is supposed to be
portrayed on WEB DataLab UI?{color}
!image-2022-07-15-14-40-44-836.png!
instance (master/slave) size (it should be discussed by DevOps and Back-end):
> [Front-end] Create HDInsight on Azure
> -------------------------------------
>
> Key: DATALAB-2646
> URL: https://issues.apache.org/jira/browse/DATALAB-2646
> Project: Apache DataLab
> Issue Type: Task
> Components: DataLab Main
> Reporter: Vira Vitanska
> Assignee: Hennadii Shpak
> Priority: Minor
> Labels: AZURE, Debian, Front-end, Github_issue, RedHat
> Attachments: image-2022-07-15-14-40-44-836.png
>
>
> As a user I want to use HDInsight (Data Engine Service) on Azure in DataLab,
> so that I can simplify running big data frameworks.
> ----
> [[Compute Engine] Feature: New Compute Engine - Azure HDInsight - EPMA-DPAF -
> EPAM Knowledge
> Base|https://kb.epam.com/display/EPMADPAF/%5BCompute+Engine%5D+Feature%3A+New+Compute+Engine+-+Azure+HDInsight]
> User should be able to create HDInsight for the next types of notebooks:
> Apache Zeppelin, RStudio, Jupyter.
> *{color:#de350b}Creation popup is below (it should be discussed by DevOps and
> Back-end):{color}*
> {color:#de350b}1. Does HDInsight has one or two masters?{color}
> {color:#de350b}2. If HDInsight has two master do we allow user to choose
> slave for each master node?{color}
> {color:#de350b}3. Total instance number does include master (1/2)?{color}
> {color:#de350b}4. What can be max instance number for HDInsight?{color}
> {color:#de350b}5. Does HDInsight has particular trait which is supposed to be
> portrayed on WEB DataLab UI?{color}
> !image-2022-07-15-14-40-44-836.png!
> *{color:#de350b}Instance (master/slave) size (it should be discussed by
> DevOps and Back-end):{color}*
> Memory optimized:
> *S* Standard_E4s_v3
>
> *M* Standard_E16s_v3
>
> *L* Standard_E32s_v3
> Compute optimized:
> *S* Standard_F2s
>
> *M* Standard_F8s
>
> *L* Standard_F16s
>
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