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Cosmos DB Real-time Advanced Analytics Workshop
The workshop implements an innovative fraud detection solution as a PoC for a bank who provides payment processing services for commerce to their merchant customers all across the globe, helping them save costs by applying machine learning and advanced analytics to detect fraudulent transactions. Since their customers are around the world, the right solutions should 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. The workshop designs 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. Cosmos DB’s major advantage when operating at a global scale is its high concurrency with low latency and predictable results.
This combination is unique to Cosmos DB and ideal for the bank needs. The solution leverages the Cosmos DB change data feed in concert with the Azure Databricks Delta and Spark capabilities 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. https://github.com/Microsoft/MCW-Cosmos-DB-Real-Time-Advanced-Analytics Takeaway: How to leverage Azure Cosmos DB + Azure Databricks along with Spark ML for building innovative advanced analytics pipelines.
1 .WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
2 .Cosmos DB Real-time advanced analytics workshop Cosmos DB Real-time Advanced Analytics Workshop Sri Chintala, Microsoft #UnifiedDataAnalytics #SparkAISummit
3 .Today’s customer scenario Woodgrove Bank provides payment processing services for commerce. Want to build PoC of an innovative online fraud detection solution. Goal: Monitor fraud in real-time across millions of transactions to prevent financial loss and detect widespread attacks. #UnifiedDataAnalytics #SparkAISummit 3
4 .Part 1: Customer Scenario • Woodgrove Banks’ customers – end merchants – are all around the world. • The right solution would minimize any latencies experienced by using their service by distributing the solution as close as possible to the regions used by customers. 4
5 .Part 1: Customer scenario • Have decades-worth of historical transactional data, including transactions identified as fraudulent. • Data is in tabular format and can be exported to CSVs. • The analysts are very interested in the recent notebook-driven approach to data science & data engineering tasks. • They would prefer a solution that features notebooks to explore and prepare data, model, & define the logic for scheduled processing. 5
6 .Part 1: Customer needs • Provide fraud detection services to merchant customers, using incoming payment transaction data to provide early warning of fraudulent activity. • Schedule offline scoring of “suspicious activity” using trained model, and make globally available. • Store data from streaming sources into long-term storage without interfering with read jobs. • Use standard platform that supports near-term data pipeline needs and long- term standard for data science, data engineering, & development. 6
7 .Common scenarios
8 .Part 2: Design the solution (10 min) • Design a solution and prepare to present the solution to the target customer audience in a chalk-talk format. 8
9 .Part 3: Discuss preferred solution 9
10 .Preferred solution - overall
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13 .Preferred solution – Data Ingest Payment transactions can be ingested in real-time using Event Hubs or Azure Cosmos DB. Factors to consider are: rate of flow (how many transactions/second) data source and compatibility level of effort to implement long-term storage needs
14 .Preferred solution – Data Ingest Cosmos DB: Is optimized for high write throughput Provides streaming through its change feed. TTL (time to live) – automatic expiration & save in storage cost Event Hub: Data streams through, and can be persisted (Capture) in Blob or ADLS Both guarantee event ordering per-partition. It is important how you partition your data with either service.
15 .Preferred solution – Data Ingest Cosmos DB likely easier for Woodgrove to integrate because they are already writing payment transactions to a database. Cosmos DB multi-master accepts writes from any region (failover auto redirects to next available region) Event Hub requires multiple instances in different geographies (failover requires more planning) Recommend: Cosmos DB – think of as “persistent event store”
16 .Preferred solution – Data pipeline processing Azure Databricks: Managed Spark environment that can process streaming & batch data Enables data science, data engineering, and development needs. Features it provides on top of standard Apache Spark include: AAD integration and RBAC Collaborative features such as workspace and git integration Run scheduled jobs for automatic notebook/library execution Integrates with Azure Key Vault Train and evaluate machine learning models at scale
17 .Preferred solution – Data pipeline processing Azure Databricks can connect to both Event Hubs and Cosmos DB, using Spark connectors for both. Spark Structured Streaming to process real-time payment transactions into Databricks Delta tables. Be sure to set a checkpoint directory on your streams. This allows you to restart stream processing if the job is stopped at any point.
18 .Preferred solution – Data pipeline processing Store secrets such as account keys and connection strings centrally in Azure Key Vault Set Key Vault as the source for secret scopes in Azure Databricks. Secrets are [REDACTED].
19 .Preferred solution – Data pipeline processing Databricks Delta tables are Spark tables with built-in reliability and performance optimizations. Supports batch & streaming with additional features: ACID transactions: Multiple writers can simultaneously modify data, without interfering with jobs reading the data set. DELETES/UPDATES/UPSERTS: Automatic file management: Data access speeds up by organizing data into large files that can be read efficiently Statistics and data skipping: Reads are 10-100x faster when statistics are tracked about data in each file, avoiding irrelevant information
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24 .Preferred solution – Model training & deployment Azure Databricks supports machine learning training at scale. Train model using historical payment transaction data
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27 .Preferred solution – Model training & deployment Use Azure Machine Learning service (AML) to: Register the trained model Deploy it to Azure Kubernetes Service (AKS) cluster for easy web accessibility and high availability. For scheduled, batch scoring, Access model from notebook and write results to Cosmos via Cosmos DB Spark connector.
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