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README.md

Cosmos DB real-time advanced analytics

Woodgrove Bank, who provides payment processing services for commerce, is looking to design and implement a proof-of-concept (PoC) of an innovative fraud detection solution. They want to provide new services to their merchant customers, helping them save costs by applying machine learning and advanced analytics to detect fraudulent transactions. Their customers are around the world, and the right solutions for them would minimize any latencies experienced using their service by distributing as much of the solution as possible, as closely as possible, to the regions in which their customers use the service.

October 2019

Target audience

  • Application developer
  • AI developer
  • Data scientist

Abstracts

Workshop

In this workshop, you will learn to design a data pipeline solution that leverages Cosmos DB for both the scalable ingest of streaming data, and the globally distributed serving of both pre-scored data and machine learning models. The solution leverages the Cosmos DB change data feed in concert with the Azure Databricks Delta to enable a modern data warehouse solution that can be used to create risk reduction solutions for scoring transactions for fraud in an offline, batch approach and in a near real-time, request/response approach.

At the end of this workshop, you will be better able to design and implement solutions that leverage the strengths of Cosmos DB in support of advanced analytics solutions that require high throughput ingest, low latency serving and global scale in combination with scalable machine learning, big data and real-time processing capabilities.

Whiteboard design session

Woodgrove Bank, who provides payment processing services for commerce, is looking to design and implement a PoC of an innovative fraud detection solution. They want to provide new services to their merchant customers, helping them save costs by applying machine learning and advanced analytics to detect fraudulent transactions. Their customers are around the world, and the right solutions for them would minimize any latencies experienced using their service by distributing as much of the solution as possible, as closely as possible, to the regions in which their customers use the service.

In this whiteboard design session, you will work in a group to design the data pipeline PoC that could support the needs of Woodgrove Bank.

At the end of this workshop, you will be better able to design solutions that leverage the strengths of Cosmos DB in support of advanced analytics solutions that require high throughput ingest, low latency serving and global scale in combination with scalable machine learning, big data and real-time processing capabilities.

Hands-on lab

Woodgrove Bank, who provides payment processing services for commerce, is looking to design and implement a PoC of an innovative fraud detection solution. They want to provide new services to their merchant customers, helping them save costs by applying machine learning and advanced analytics to detect fraudulent transactions. Their customers are around the world, and the right solutions for them would minimize any latencies experienced using their service by distributing as much of the solution as possible, as closely as possible, to the regions in which their customers use the service.

In this hands-on lab session, you will implement a PoC of the data pipeline that could support the needs of Woodgrove Bank.

At the end of this workshop, you will be better able to implement solutions that leverage the strengths of Cosmos DB in support of advanced analytics solutions that require high throughput ingest, low latency serving and global scale in combination with scalable machine learning, big data and real-time processing capabilities.

Azure services and related products

  • Azure Cosmos DB
  • Azure Databricks
  • Azure Data Lake Store
  • Azure Event Hubs
  • Azure Kubernetes Service
  • Azure Machine Learning
  • Power BI

Azure solutions

Globally Distributed Data

Related references

Help & Support

We welcome feedback and comments from Microsoft SMEs & learning partners who deliver MCWs.

Having trouble?

  • First, verify you have followed all written lab instructions (including the Before the Hands-on lab document).
  • Next, submit an issue with a detailed description of the problem.
  • Do not submit pull requests. Our content authors will make all changes and submit pull requests for approval.

If you are planning to present a workshop, review and test the materials early! We recommend at least two weeks prior.

Please allow 5 - 10 business days for review and resolution of issues.

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