Cloud as a Story - Vunvulea Radu

The last posts covered blueprints of a money laundering solution for Microsoft Azure and AWS. You can check the two posts if you want to find more about the proposed solutions below: Blueprint of a transaction monitoring solution on top of AWS and custom ML algorithm for money laundering Blueprint of a transaction monitoring solution on top of Azure and custom ML algorithm for money laundering Comparison of a money laundering solution build on top of AWS and Azure The blueprints were designed, having in mind two key elements. 1.      Reduce the operation and management cost at minimal 2.     Reduce at minimum the configuration and development costs by using out-of-the-box cloud services as much as possible In this post, we will make a comparison between what services we used for each layer, trying to identify the key differential factors between each of them. Ingest For this purpose, we used Azure Event Hub and AWS Kinesis Data Stream. Both services are cap

Office Graph API it's a great mechanism to gather information related to users that you have inside the organization. It is easily integrated with other application that needs to fetch user information and user activity. The security and management layer that it is built on top of it allows us to have a more granular control related to what data we share with different applications or users in comparison with offering access to AD or similar systems. In this post, we will talk about employeeId that represents the unique ID that each user has inside the organization. It is used to identify a unique user and can be used to fetch data from other systems. The property is part of User resource type  and in theory, can be accessed easilty using a call like the one below. https://graph.microsoft.com/v1.0/me Unfortunately, you will discover that you cannot find the employeeId in the result. The cause is not related to our rights/access level that you have. The cause is that this f

In the last post , I presented a possible solution on top of Microsoft Azure that can be used for real-time analytics on bank accounts using out of the box services using a custom Machine Learning algorithm. In this post, we take a look at a similar approach that we can have using AWS Services. One of the key factors that we took into consideration when we decided what AWS service to use was to involve minimal configuration and to be out of the box services. If you already read the post where I offered an Azure the solution, you should jump to “AWS Approach” section. Related posts: Blueprint of a transaction monitoring solution on top of AWS and custom ML algorithm for money laundering Blueprint of a transaction monitoring solution on top of Azure and custom ML algorithm for money laundering Comparison of a money laundering solution build on top of AWS and Azure Business scenario Imagine that you are working for a bank that has subsidiaries in multiple regions arou

In this post, we talk about how we can use the cloud to enable us to do real-time analytics of bank account activities using our own custom Machine Learning algorithm. Related posts: Blueprint of a transaction monitoring solution on top of AWS and custom ML algorithm for money laundering Blueprint of a transaction monitoring solution on top of Azure and custom ML algorithm for money laundering Comparison of a money laundering solution build on top of AWS and Azure Business scenario Imagine that you are working for a bank that has subsidiaries in multiple regions around the world. You want to develop a system that can monitor in real time the bank activities that are happening cross subsidiaries and identify suspect any suspect transactions or accounts. Machine Learning Algorithm The bank already develops a custom ML algorithm that can detect and mark any account or transactions that looks, suspect. The solutions can detect suspect transactions so good, that you

I ’m proud and honoured to have been re-awarded as Microsoft Azure MVP for another year. It is my 7 year in a row when I receive this nomination. It keeps me motivated to do that extra mile to support the community and share my knowledge and experience with the others. For the next 12 months, I have a topic list that I want to approach that I think will help the Azure and cloud community. You can find the list below: Define migration and recommendation best practices when you want to migrate from AWS to Azure Migration and best practices paths for cloud and Azure Sharing my Azure experience with community and support Microsoft initiatives Support group of people that wants to learn and grow their cloud competencies Lesson learned from me or my teams related to cloud and Azure especially or me   Thanks all folks for the support, let’s go back to our communities and support them to achieve the clouds.