Import Data from Local Files

We will be following through the process summarized below to import environment data into MPA:

  1. create portfolio
  2. Identify/locate the data to import
  3. Import the data following upload wizard
  4. Check and resolve any data issues

1. Create Portfolio

You will be greeted with following screen. On the left-hand navigation panel is your existing Portfolios, on the right is brief summary of key features of MPA and recent updates.

Click Get started to start creating a portfolio or view an existing portfolio Start Screen

  1. Click Create Portfolio :
    1. In the Portfolio name, put MPA_immersion_day
    2. Select No data(empty)
    3. for initial dataset → we will import the data through file uploads later.
    4. Select Test (or non-customer) data only for Data classification
    5. Click Create portfolio

Create Portfolio

For real customer data, please select Customer data and fill in customer’s Account name.

2. Identify / locate the data to import

For this lab, we will be downloading CSV and excel files locally and upoload to the MPA.

You can also import partially through CSV/excel and fill the rest through AWS ADS or vice versa. Note that importing duplicate data from ADS will overwrite the existing data in MPA and vice versa. A Quick Guide on how to import from ADS.

  1. You will use the files that you downloaded from the top of this page, under the session attachements
  2. Open servers_table.xlsx and inspect it. The file contains CMDB data on servers we would like to migrate. servers_table.xlsx
  3. Go to the left-hand navigation panel: Portfolio data → Import → Import from file
    1. Select data type Servers
    2. Click Upload local file
    3. Select the servers_table.xlsx from file directory upload local file

These data template don’t enforce a standard header format. You can configure Header mapping using the upload navigator. This semi-automatic workflow will help reduce the effort required in normalizing the data.

3. Import the data following upload wizard

  1. Header mapping The upload wizard will give recommended header mapping, you can also change File header to desired mapping. The sample values are shown when you select a file header from the drop down. Leave everything as default.

    header mapping

    Review the optional header mapping. Leave everything as default. optinal mapping

  2. Resolve Duplicate in the file: note that the OS Version is highlighted in red, select the first option, OS Version = 16.

    resolve duplicate

  3. Preview uploaded data preview

4. Check and resolve any data issues

Navigate to Portfolio data → Assets. Go to the Servers tab, Verify that server data is imported. asset servers.xlsx

You can ignore the following banner for now. validation issues We will explore Data Validation Rules feature further in next module - Data Verification.


Repeat Step 2-3 for the rest of the files following the mapping below:

Data File name
Server servers_table.xlsx
Application app_table.xlsx
Server to application servertoapp_table.xlsx
Server Communication servercomm_table.xlsx
Databases databases_table.xlsx
Database to application databasetoapp_table.csv
Application dependency appdep_table.xlsx

You can also combine all of these into a single file on different sheets. MPA will automatically read in the sheets. Refer to sample template file provided in upload page.

When doing your own upload, watch for these common mistakes: • Incomplete data • Unreal data (RAM < 1 KB, RAM > 20000GB, CPU > 5000) • Wrong unit of storage/memory (GB,MB,KB) • Wrong unit of utilization attributes • Wrong matched attributes • Data redundancy, and • Wrong Storage IOPS


Important Attributes

MPA uses assumptions when some data is not available. These are the important metrics that have major significance for cost estimates. Customers must supply these attributes, and check the data for outliers or unreal values.

  • Server OS
  • CPU count
  • RAM size and utilization percentage
  • CPU and utilization percentage
  • Storage size and utilization percentage
  • Server usage percentage (Uptime)
  • Storage IOPS
  • Server type (environment), such as production, non-production,
  • Physical, or Virtual