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Making a sandbox in Microsoft Partner Center

One of the things those who sell through Microsoft Azure Marketplace, Azure Portal, or AppSource will note is that there isn’t a nice place to test the offer. Many people want a sandbox environment to test away while only incurring charges on their bill for Azure resources without being billed for the offer itself. Today, no sandbox exists. There are valid reasons for this, but this article isn’t going to go into those. Instead, I want to focus on how to get the ability to test away without encountering extra, unexpected charges. I will only focus on the three main transactable types in Partner Center: Software as a Service (SaaS), Azure Application: Managed Application (AMA), and Virtual Machines. The pattern is the same for all three of these.

For every offer you publish in Partner Center, you need a companion test offer. The two offers are identical in all areas except for three:

  • Offer ID
  • Price on plans
  • Co-sell documentation (you don’t bother uploading this for the test offer)

Keep the offer IDs the same across both environments. This allows you to test your logic for provisioning or performing other actions on SaaS and AMA offers. This will also allow you to make sure that a VM which uses the Instance Metadata Service (works for Windows and Linux) to verify the running image has the right logic. It is possible that you’ll have a test case for VMs which requires running under an “unknown” plan name, just to make sure your logic for running only ‘blessed’ images works.

Any pricing on the test plans should be set to $0. In this case, not even a small $0.00001, but $0. I’ve had partners use the $0.00001 meter, then have test code cause a $10000 bill against that meter in a single month. This lets you validate units being reported to the reports in Partner Center without you being asked to pay $10000 so that Microsoft can take their processing fee and deposit the balance back in your bank account.

The test offer will contain the same text, plan IDs, descriptions, contact information, and so on. It will point to the same technical assets. The test environment will copy everything, with a couple of optional tweaks to allow development after your first publication:

  • SaaS: You may choose to point the landing page and web hook to the version of the asset which is in development or test.
  • AMA: Each plan will use the dev version of the zip file for the ARM template, UI Definition, and other assets. The technical configuration will also be updated. Once the test AMA is ready, these assets get copied to the production plans. You may also choose to point the Notification webhook to a test/dev version of the endpoint.
  • Virtual machine: Each VM SKU will get tested. Once all is working, the URLs for the OS disk and any data disks is copied to the production instance.

Finally, when publishing the test offer, never let the offer get past the Preview stage. If you do accidentally publish a preview, test offer, you can stop selling pretty quickly (a stop sell can take up to a few hours).

Once all looks fine, copy the updated technical details to the production offer, review, and publish.

And that is how you give yourself a sandbox environment in Microsoft Partner Center.

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Azure Managed Application: Customize allowed customer actions

When publishing an Azure Managed Application, many ISVs choose to make some functionality available to the owner of the application. You know which of the Azure built-in roles you want to use, but you aren’t sure what actions to include. That built-in roles page also includes the list you need for Allowed control actions and Allowed data actions. Everything under Actions for the role goes into Allowed control actions. Anything under DataActions for the role goes into Allowed data actions. You just need to add the list as a semi-colon delimited list and you are good to go.

If you don’t want to read the docs and you know exactly what you want, you can also pull this information through the az cli or Azure PowerShell. To list all roles, run:

Az cli: az role definition list

PowerShell: Get-AzRoleDefinition

If you already know which role you need details on, you can run another command to get just the specifics for that role. For example, let’s say I know I need the Reader and Data Access role from Storage. I can run:

Az cli: az role definition list --name 'Reader and Data Access'

PowerShell: Get-AzRoleDefinition -Name 'Reader and Data access'

Once you have the specific role, you can then emit the right values for the control actions and data actions. This is fairly easy to do in PowerShell.

$roleDefinition = Get-AzRoleDefinition -Name 'Reader and Data access'

Write-Host "Control actions:" ($roleDefinition.Actions -join ";")

Write-Host "Data actions:" ($roleDefinition.DataActions -join ";")

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ARM Templates: Pass all parameters to a script

I had an interesting inquiry the other day. I was talking to a developer. She had a script which consumed most of the parameters in her ARM template. Every time she added a new parameter, she also had to remember to update the script call to pass the new parameter. She needed a mechanism to just pass all parameters to a bash script.

As a proof of concept, I just wanted to find a way to get the parameters to be saved on a Linux VM. For this, I did some looking at the various ARM template functions and noticed this one: deployment(). deployment() allows access to the ARM template and the parameters which differ in value from the parameter’s defaultValue.

To use the value, I first captured the values of interest in the ARM template variables:

"variables": {
  "uniqueParams": "[deployment().properties.parameters]",
  "defaultParams": "[deployment().properties.template.parameters]",
  "userParams": "[replace(string(variables('uniqueParams')), '\"', '\\\"')]",
  "originalParams": "[replace(string(variables('defaultParams')), '\"', '\\\"')]"

Please note that the above variables section was trimmed to just show the capture of the parameters. The string() function turns the object into JSON, replace() does simple string substitution. Once captured, my proof of concept then just wanted to show that the values could be passed along. To do this, I added a custom extension which would emit the data to a well known location.

    "type": "Microsoft.Compute/virtualMachines/extensions",
    "apiVersion": "2020-06-01",
    "name": "[concat(variables('vmName'),'/', 'RunScripts')]",
    "location": "[parameters('location')]",
    "dependsOn": [
    "properties": {
        "publisher": "Microsoft.Azure.Extensions",
        "type": "CustomScript",
        "typeHandlerVersion": "2.1",
        "settings": {
            "commandToExecute": "[concat('echo \"', variables('userParams'), '\" > /var/userParams.txt')]"

Once done, the following was emitted to userParams.txt:

  "vmNamePrefix": {
    "value": "scseely"
  "vmSize": {
    "value": "Standard_DS2_v2"
  "pwd": {
    "value": "p@ssw0rd"
  "dnsName": {
    "value": "scseely"
  "publicIPAddressName": {
    "value": "sycompscs"

Likewise, if you need the default params as well, the file looks like this:

  "vmNamePrefix": {
    "type": "String",
    "metadata": {
      "description": "Assign a prefix for the VM name"
  "location": {
    "defaultValue": "[resourceGroup().location]",
    "type": "String",
    "metadata": {
      "description": "Select the Azure region for the resources"
  "vmSize": {
    "type": "String",
    "metadata": {
      "description": "Select the vm size"
  "userName": {
    "defaultValue": "azureadmin",
    "type": "String",
    "metadata": {
      "description": "Specify the OS username"
  "pwd": {
    "type": "SecureString",
    "metadata": {
      "description": "If Windows, specify the password for the OS username"
  "dnsName": {
    "type": "String",
    "metadata": {
      "description": "Specify the DNS name for the managed web app"
  "publicIPAddressName": {
    "type": "String",
    "metadata": {
      "description": "Assign a name for the public IP address"

Now, to read the file back, I can use a tool like jq to load and manipulate the JSON file in my scripts. Because the commandToExecute is just a bash command, I can stitch the emission of the JSON with scripts using ‘&&’.

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Connect Application Insights to your Azure Functions App in Terraform

This goes into the “notes for Scott” category, where I post things to my blog for me. I hope this is somewhat useful for you too!

I’m in the process of writing Terraform automation for an Azure Functions application I’ve built. When the deployment completed and I went to the Azure Functions application in the Azure portal (, I got a message stating that Application Insights wasn’t connected to the Functions App:

Application Insights is not configured. Configure Application Insights to capture function logs.

The fix isn’t well documented, yet. After deploying a functions app via the portal, I found the link and it’s pretty simple: Azure Functions uses an app setting named APPINSIGHTS_INSTRUMENTATIONKEY. Just add that with the right value and things work.

To put it all together, you will deploy an app service plan, Application Insights, and an Azure Function App:

resource "azurerm_app_service_plan" "app_service_plan" {
  name                = "${var.base_name}appserv"
  location            = "${azurerm_resource_group.rg.location}"
  resource_group_name = "${}"
  kind                = "Linux"
  reserved            = true
  sku {
    tier = "Basic"
    size = "B1"
resource "azurerm_application_insights" "ai" {
  name                = "${var.base_name}ai"
  location            = "${azurerm_resource_group.rg.location}"
  resource_group_name = "${}"
  application_type    = "web"
Once created, the azurerm_application_insights resource has a value called instrumentation_key. Connect that to the APPINSIGHTS_INSTRUMENTATIONKEY app setting in your azurerm_function_app to connect AppInsights to your Azure Functions.
resource "azurerm_function_app" "apis" {
  name                      = "${var.base_name}func"
  location                  = "${azurerm_resource_group.rg.location}"
  resource_group_name       = "${}"
  app_service_plan_id       = "${}"
  storage_connection_string = "${azurerm_storage_account.az_backend.primary_connection_string}"
  https_only                = true
  version                   = "~2"

  app_settings = {

  site_config {
    cors { 
      allowed_origins = ["https://www.${var.domain_name}"]

Upon running this, the error message went away and Azure Functions showed I had connected everything correctly.

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Using AzureAD PowerShell on *nix machines (Mac, Linux)

The Azure Active Directory team has a lot of great command line tooling. This is available in the Azure Cloud Shell (from the portal) as well as via the AzureAD PowerShell package. The .NET Core version of the PowerShell package is still in development, but is available for us to use as needed. I’ve had to show a few folks how to do this on my team, so I’m recording the steps here as my “notes”. Run all these from an elevated PowerShell session (sudo PowerShell).

  1. Add the PowerShell Test Gallery. This gallery is a test site and may go down for any reason. The command to make it available is:

Register-PackageSource -Trusted -ProviderName 'PowerShellGet' -Name 'Posh Test Gallery' -Location'

  1. Import the package using this command.

Install-Module AzureAD.Standard.Preview
For what it’s worth, I installed PowerShell onto my Ubuntu box using the information over here.

If you need the Azure PowerShell module too, run
Install-Module Az

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Public Notes: Running Python in Azure Batch on Windows using the Azure Portal

TLDR; This post explains how to setup an Azure Batch node which uses Windows using installers. It also explains how to use application packages to preload Azure Batch nodes with utilities so that tasks can just be command lines. The explanation use case is “run a Python script”, but should apply more broadly to “install tools, distribute stuff, and run command lines.”

PDF Version of this article (which has a bit better formatting)

When I start experimenting with something, I do not start out with writing code to automate everything. Sometimes, I try to use a GUI to bootstrap my process, then automate when setup is correct. Why? A lot of environments, like Azure, will allow for fast cycles from the UI tools. My latest adventure took a bit of time, so I’m documenting what I did.

Here’s the context: I am developing a mid-sized example project for scalability. If everything goes to plan, the demo will show how to solve the same problem with Azure Batch and the Azure Kubernetes Service. The demo is targeting a special kind of data scientist: an actuary. Actuaries frequently write code in one of three languages: APL, Python, and R. I’m focusing on Python.

My goals:

  1. Configure everything through the Azure portal.
  2. Install Python on the batch node at startup.
  3. Use the application packages feature to deliver the Python virtual environment prior to any tasks running.
  4. Run a command line without resources to make sure I can run a script in the Python virtual environment.

What follows is essentially a lab for you to follow and do the same things I did. As time marches forward, this lab’s correctness will degrade. Hit me up on LinkedIn if you catch this and I may go back and update the details.

For all the Azure pieces, try to keep things in the same region. I’ll be using East US. This isn’t necessary, but is helpful for the parts that transfer files. Staying in the same region gives better speed.

1         Create a new project in PyCharm

  1. Open up your editor; I’m using PyCharm. Details for other editors will differ. Set the location to wherever you like. I’m naming the project BatchDemo.
  2. Setup a New environment using Virtualenv. The location will be in the venv directory of your project. For the base interpreter, use the one installed on your machine already.

For me, the dialog looks like this:


  1. Click on Create.
  2. Add a new file,, to the project in the BatchDemo directory.
  3. Add the library to use table storage.
    1. Select File–>Settings–>Project: BatchDemo–>Project Interpreter.
    2. In the list of packages, you’ll see a plus sign ‘+’. Click on that.
    3. Select azure-cosmosdb-table. Click on Install Package.
    4. Close the Available Packages window once done. My machine looked like this before I closed the window:


  1. Click OK on the Settings Window. You should now have a number of packages installed.
  2. Add the following code to the file. The code is fairly “Hello, world!”-ish: add a row to a Table Storage table (using libraries from the azure.cosmosdb namespace, but interacts with Storage, so no, not intuitive). The script is simple and adds one row to a table named tasktable:
from azure.cosmosdb.table.tableservice import TableService
import datetime

def main():
    table_name = 'tasktable'
    table_service = TableService(
        account_name="<your Azure Storage Account Name>",
        account_key="<your Azure Storage Account Key>")

    if not (table_service.exists(table_name)):
    task = \
            'PartitionKey': 'tasks',
            'RowKey': str(datetime.datetime.utcnow()),
            'description': 'Do some task'
    table_service.insert_entity(table_name, task)   

if __name__ == "__main__":

For the highlighted code, take the name of one of your Azure Storage Accounts and a corresponding key, then plug in the proper values. If you need to create a storage account, instructions are here. To get the keys, look in the same doc (or click here) and follow the instructions. If you create a new storage account, use a new resource group and name the resource group BatchPython. We’ll use that group name later too.

One last comment here: for a production app, you really should use Key Vault. The credentials are being handled this way to keep the concept count reasonably low.

Test the code by running it. You should be able to look at a table named tasktable in your storage account and see the new row. The RowKey is the current timestamp, so in our case it should provide for a unique enough key.

Once you have all this working and tested, let’s look at how to run this simple thing in Azure Batch. Again, this is for learning how to do some simple stuff via a Hello, World.

2         Create a batch account

In this step, we are going to create a batch account which I’ll refer to as batch_account in here; your name will be different. Just know to substitute a proper string where needed.

  1. In the Azure portal, click on Create a resource.
  2. Search for Batch Service. Click on Batch Service, published by Microsoft.
  3. Click on Create.
  • Account name: For the account name, enter in batch_account [remember, this is a string you need to make up, then reuse. You’re picking something unique. I used scseelybatch]
  • Resource Group: Select BatchPython. If you didn’t create this earlier, select Create New.
  • Select a storage account to use with batch. You can use the same one you created to test the table insertion.
  • Leave the other defaults as is.
  • Click on Create.

3         Upload the Python Installer

Upload the Python installer which you want to use. I used the Windows x86-64 executable installer from here.

  1. In your storage account, create a container named installers.
    1. In the Azure Portal, navigate to your Storage Account.
    2. Select Blob ServiceàBrowse Blobs
    3. Click on + Container.
    4. Set the Name to installers.
    5. Click on OK.
  2. Once created, click on the installers container.
  3. Upload the Python installer from your machine.
    1. Click on Upload.
    2. In the Upload blob screen, point to your installer and click on Upload.
    3. Wait until the upload completes.
  4. Get a SAS URL for the installer.
    1. Right click on the uploaded file.
    2. Select Generate SAS.
    3. Set the expiration of the token to some time in the future. I went for 2 years in the future.
    4. Click on Generate blob SAS token and URL


Copy the Blob SAS URL. Store that in a scratch area. You’ll need it in a bit.

4         Create a Batch Application

  1. Going back to your machine, go to the BatchDemo directory which contains your file along with the virtual environment. Zip up BatchDemo and everything else inside into a file called [Mine is about 12MB in size]
  2. Open up your list of Resource Groups in the portal. Click on BatchPython.
  3. Click on your Batch account.
  4. Select FeaturesàApplications
  5. Click on Add.
    1. Application id: BatchPython
    2. Version: 1.0
    3. Application package: Select
    4. Click on OK.

The file will upload. When complete, you’ll have 1/20 applications installed.

  1. Click on BatchPython.
  2. Set Default Version to 1.0.
  3. Click on Save.

5         Create a Batch Pool

  1. Open up your list of Resource Groups in the portal. Click on BatchPython.
  2. Click on your Batch account.
  3. Select on FeaturesàPools
  4. Click on Add.
    • Pool ID: Python
    • Publisher: MicrosoftWindowsServer
    • Offer: WindowsServer
    • Sku: 2016-Datacenter
    • Node pricing tier: Standard D11_v2 [Editorial: When experimenting, I prefer to pick nodes with at least 2 cores. 1 for the OS to do its thing, 1 for my code. I’ll do one core for simple production stuff once I have things working. This is particularly important to allow for effective remote desktop/SSH. The extra core keeps the connection happy.]
    • Target dedicated nodes: 1
    • Start Task/Start task: Enabled
    • Start Task/Command Line: We want this installed for all users, available on the Path environment variable, and we do not want a UI.

python-3.6.6-amd64.exe /quiet InstallAllUsers=1 PrependPath=1 Include_test=0

  • Start Task/User identity: Task autouser, Admin
  • Start Task/Wait for success: True
  • Start Task/Resource files:
    • Blob Source: Same as the URL you saved from the piece labeled “Upload the Python Installer”. The SAS token is necessary.
    • File Path: python-3.6.6-amd64.exe
    • Click on Select
  • Optional Settings/Application packages
    • Click on Application Packages.
    • Application: BatchPython
    • Version: Use default version
    • Click on Select
  • Click on OK.

The pool will be created. In my experience, creating the pool and getting the node ready can take a few minutes. Wait until the node appears as Idle before continuing.


6         Run a job

  1. Open up your list of Resource Groups in the portal. Click on BatchPython.
  2. Click on your Batch account.
  3. Select on FeaturesàJobs
  4. Click on Add
    1. Job ID: AddARow
    2. Pool: Python
    3. Job manager, preparation, and release tasks:
      1. Mode: Custom
      2. Job Manager Task:
        1. Task ID: AddARowTask
        2. Command line:


Note on the environment variable: Application packages are zip files. Batch puts the location of the unzipped application package into an environment variables in one of two ways, depending on if you select the default version or a specific version.



  • Click on Select
  • Click on OK

The job should start running immediately. Because it’s a short task, it’ll finish quickly too. Click on Refresh and you’ll probably see that the AddARowTask has completed.


You can then verify the output by opening up the table and looking at the rows. A new one should be present. I’ll expect a row that completed near 21:33 on July 19; the time will be recorded as UTC, and I’m in Redmond, WA, USA, which is 7 hours behind UTC time.


That view is courtesy of the Azure tools present in Visual Studio 2017.

7         So, what next?

Now that you’ve done all this, what does it mean? For your batch pools, you could preload them with a common toolset. The resource files you pass in to a job can be files to operate on, independent of binaries. Your tasks start times can be greatly reduced by loading the prerequisites early. Could you do this with custom VMs? Sure, but then you need to keep the VMs patched. This mechanism allows you to use a patched VM and just install your few items.

This is definitely a toy example, meant to show how to do the initial setup in the portal. Here’s what you want to do for automation purposes:

  1. Script all of this.
  2. For the Python piece, add a mechanism to create the zip file after you have a successful build and test.
  3. Script the management of the binaries, creating the Batch Service, and configuring the pools and application package(s).
  4. Add an integration test to validate that the pool is ready to run.
  5. Minimize the number of secrets in the code to 0 secrets. Use Key Vault to manage that stuff.

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Thoughts on Day 3 of Money 2020, Europe

Walking the show floor at Money 2020, one sees lots of payment providers. Its no shock then that today, I saw a lot of talks on how this works. This is in keeping with the theme of the show: the future of money. The previous two days, I did hear a fair amount of talk about moving away from paper money, what to do about fiat currency vs. other currency, and difficulty in managing payments. Today, we heard about what is possible with payments.

The facts keep stacking up around us about people preferring to pay electronically instead of with plastic or currency. Those electronic payments are happening in apps:

The first three are as phone OS capabilities, the last two are via apps on the phones. Electronic payments make a number of things better for consumers, retailers, and credit institutions (banks, credit card companies, etc.). For consumers, they get convenience. For retailers, they find less friction at checkout. For credit institutions, they get reduced fraud. So far, so good, right? Well, what I learned after this was a lot more interesting: if you are just reducing payment friction, you are leaving a lot of opportunity on the table. Opportunity for:

  • Learning about the customer. You are collecting their buying habits. Imagine what you could do if you did more to help, what else could you learn?
  • Getting retailers to use your payment system. You know a lot about your customers and what they like to buy. You can now refer them to other retailers, services, and so on. Use that to convince retailers that if they use your system, you will drive more interested customers their way.
  • Delight your customers in novel ways. WeChat has delighted their Chinese users by making it easier to send Hong Bao (a monetary gift in a red envelope) to others. Their users love this feature. Here’s what the growth has looked like year over year (numbers are from a presentation by Ashley Guo of WeChat):


You can also look at Ant Financial/Alipay. Despite their name, they are not a payment company; they are a marketing company. You use them to schedule doctor appointments, figure out how to travel on public transportation or taxi, manage vacations, discover information about products, and so on. And yes, when the service is performed or goods are purchased, they also make sure that the vendor is compensated by you. But, they make it all seamless. The product has been successful in China, turning their tier 1 and tier 2 cities into cashless areas. The services are so popular with their Chinese user base that the apps are used around the world at high end retailers down to businesses like Burger King.

Both WeChat and Alipay emphasized that they use the data to better market to users. The users like the targeting in their lives. When traveling, they discover attractions and restaurants that appeal to them because the app knows them so well. The businesses are happy to participate because they acquire customers who may not have found them otherwise.

What I saw today was a lot of companies thinking about how to make transactions easier by working with banks and credit card companies to remove plastic from your life. This is great. I look forward to the day when my wallet no longer bulges because of all the cards I need to carry.

I also saw something wonderful and scary: a world where things will generally improve for me if I let artificial intelligence and machine learning see all the things I do. By knowing what I eat, where I go, and so on maybe the algorithms can warn me to start doing some things (walk more) and stop doing other things (keep it to two coffees a day). Scary, because I worry what would happen if all that data was combined in some nefarious way. For example, if the algorithm senses that I get out of depression by spending money, maybe the algorithm seizes on this by getting my spending up using knowledge sales people only wish they knew. Or, me being denied a job because the data leaks that I buy [something the employer wants to look out for: alcohol, cigarettes, etc.].

In all, a very interesting day around payments.

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Thoughts on Day 2 of Money 2020, Europe

The conference at Money2020 has many tracks. Given the amount of questions I have seen from customers around distributed ledger technology (DLT) (Blockchain, Hyperledger Fabric, Ethereum, Corda, etc.), I attended that track. In the track, there were a fair number of panels staffed by either users of DLT, consortiums looking to make their use cases more ubiquitous, and implementers of DLT. While all groups had different lenses on what is going on, they tended to agree on what problems DLT solves and how to use the technology.

For finance, DLT helps eliminate a lot of verification/validation work with happens in the middle and back office.

This has been framed as the “Do you see what I see?” (DYSWIS diss-wiss) problem. DYSWIS solution attempts from the past have involved things like cryptographic signatures where two parties compare signatures of the data. Those solutions fail for the main reason crypto comparison always fails: normalization of data prior to signing. DLT solutions solve this in different ways, but they all have ways to sign facts and achieve consensus about the facts.

Anyhow, back to the main point about saving time confirming that what I see matches what you see. The finance industry has used a strong central authority for smaller, contract-less transactions in the form of Visa, Mastercard, and others. Then, we get to more complex transactions. How complex? Consider this scenario:

A European importer purchases some goods from an East African exporter. The importer prefers to pay when goods arrive. The exporter prefers to be paid when goods are shipped. Neither gets their preferred mode because of risk. The bank for the importer needs to issue a letter of credit. The exporter also insures the goods until delivery. The shipper, sitting between importer and exporter, will orchestrate the movement of the goods through several partners, who in turn may use other partners. Finally, the exporter will insure the goods in case of loss. It is normal for the shipment to pass through around 30 entities and have around 200 transactions [info from a presentation by].

How does DLT help here? Using DLT, the importer, exporter, bank, shipper, and insurers can all see what is happening in real time (within minutes). Because facts are attested to and sent digitally, human transcription errors disappear. This means that humans may only to verify that the numbers and such look “right” before allowing their end of a transaction to proceed. This frees up human capital to do more valuable tasks.

So, one question you might ask yourself is “which DLT is right for me?” More than a few of the C-level folks on panels said a variant of “I don’t care. I just want something that works.” For those of you that care about the details and optimal choices, understand this: if you are joining a DLT consortium and it doesn’t use what you consider to be best, you need to just build something that works with the choice. If you complicate things by creating translation layers between something like Corda and Ethereum, expect to be looking for a new job tomorrow (because you’ve been fired).

The great news here is that the businesses now understand how to apply DLT. They have found that their normal transaction volumes of 200 TPS are already handled by most enterprise DLT solutions. They also understand the difference between on-chain and off-chain data, so don’t put PII and other GDPR prohibited data on the chain.

Over and over again, I heard the C-level folks say “I want DLT for the use cases where I spend a lot of time verifying that data was input correctly because that work costs too much time and slows down the business.” Then, using those facts, they want to drive cost savings elsewhere. The instant verification of the truth reduces financial risk. The reduced financial risk means the business can now make decisions sooner to further improve their ability to move money, settle accounts, and so on.

In 2018 you will hear a number of implementations of DLT in a number of markets. At this time, it seems prudent to be familiar with the leading contenders in the space. At the moment, these seem to be (in no particular order):

This will be an exciting year for DLT.

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Thoughts on Day 1 of Money2020, Europe

I just finished day 1 of Money 20/20 Europe. I stuck mainly to the large sessions and to the show floor. What I saw was a repeated vision of what this group in finance sees as the next set of important things to be tackled. Everything they are doing revolves around the customer and making things better for customers. Depending on where you are in the financial ecosystem determines which pieces you are building and which pieces you are integrating.

From the banking side, we heard from many folks. I took the most notes from the talks by Ralph Hamers (CEO of ING Group) and Andy Maguire (Group COO at HSBC). After these two, the themes repeated which only solidified that they weren’t unique in their visions. Because banks already have the balance sheets and other nuts and bolts of building a banking business, their vision is to provide a banking platform that other businesses can plug into. Any workable platform must be open: competitors need to be able to plug into it just as easily as partners. This will allow the bank to stay good at what it knows while letting other partners fill the gaps with the wide variety of expertise that the bank does not have so that it can participate in new opportunities more easily. For example, many banks are finding success by going into geographies where their customers only interact with them over a digital experience: no human to human interaction over 99% of the time. To do this, they craft their platform and their onboarding experience to be as easy to use as possible. Several banks talked of doing work to reduce the integration times with their platforms from months down to weeks. These efforts are paying off to allow the banks to find ways to interact with more customers in more countries.

From the FinTech side of the house (which for this conference so far is the “everyone else” even though I know this leaves out personal finance folks), I saw a lot of interesting technology. A lot of the technology focused on a few areas, all with interesting takes on how to accomplish the goals. I saw a lot of distributed ledger technology (aka blockchain) with implementations that have already gone live. It wasn’t clear to me how blockchain is being leveraged, but tomorrow promises to have a number of talks around the “what” and “how”. The show also has a number of folks presenting different ways to present your identity. Many of these still focus around the two factors for authenticating and many are avoiding passwords, PIN codes, and the like. The primary mechanism here is:

  1. Some biometric. Two most commonly cited are fingerprint and face.
  2. Smart phone.

So, yes, the argument that goes “What about people from [some part of world that they think doesn’t have Android or Apple phones]?” is not under consideration. In the countries where the banks operate, they know that most of their customers have smart phones.

The final thing I noticed is that AI came up a bunch and it was all nebulous to the speakers. Asking some of the AI firms on the floor, the sales folks know that they have data scientists and those people build and maintain their models. AI/ML is being applied to Know Your Customer/Anti-money Laundering work as well as fraud detection. Given the sales process, my guess here is that the people who need the tech will talk to those who make it and then have their engineers have the nitty gritty discussions of integration. I’m definitely looking forward to learning more there.

I also spent a bit of time on the show floor. Because it’s banking, a lot of the vendors create solutions that run in the client data center OR the cloud. For those folks, I’d like to let you know that you should look at joining the Azure Marketplace. This can give you ease of deployment for your customers who run in Azure and is fairly handy for VM only deployments. Contact me and I can help you get on board.

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Copying files from a Docker container onto local machine

This past week, I’ve spent time wiping away my ignorance of containers. To do this, I started in my usual way:

  1. Buy a bunch of books. Probably too many.
  2. Work through books, doing exercises as I go.

The first book I’m running through is Using Docker: Developing and Deploying Software with Containers by Adrian Mouat. I’m posting this bit now to hopefully help others.

When working through the exercise to backup the redis database in Chapter 3, I ran the command to backup the database:

docker run --rm --volumes-from myredis -v $PWD/backup:/backup debian cp /data/dump.rdb /backup/

This then emits the error message:

C:\Program Files\Docker\Docker\Resources\bin\docker.exe: Error response from daemon: Drive has not been shared.
 See 'C:\Program Files\Docker\Docker\Resources\bin\docker.exe run --help'.

This is happening because I never shared the C-Drive with Docker. To do this, right click on the Docker icon sitting in your toolbar and select Settings… . Then, select Shared Drives and check the drive(s) on your system which you want to be able to use. DockerSettingsSharedDrive

Upon clicking Apply, enter your credentials. The command should now work.

One other note: I found that the command did not work right in cmd.exe or some bash shells. It did work just fine from a powershell window. So, that’s another note…


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