FICO is more than just the FICO credit score. Founded in 1956, FICO also offers analytics applications for customer acquisition, service, and security, plus tools for decision management.
One of those applications is the Falcon Assurance Navigator (FAN), a fraud detection system that monitors purchasing and expenses through the full procurement to pay cycle. Consider an expense report: the entities involved include the reporter, the approver, the vendor, the department or business unit, the expense line items, and more. A single report has multiple line items, where each line may be broken into different expense codes, different budget sources, and so on. This translates into a complicated data model that can be nested 6 or 7 layers deep – a great match for MongoDB’s document model, but quite hard to represent in the tabular model of relational databases.
FAN Architecture Overview
The fraud detection engine consists of a series of microservices that operate on transactions in queues that are persisted in MongoDB:
- Each transaction arrives in a receiver service, which places it into a queue.
- An attachment processor service checks for an attachment; if one exists, it sends it to an OCR service and stores the transaction enriched with the OCR data.
- A context creator service analyzes it and associates it with any past transactions that are related to it.
- A decision execution engine runs the rules that have been set up by the client and identifies violations.
- One or more analytics engines review transactions and flag outliers.
- Now decorated with a score, the transaction goes to a case manager service, which decides whether to create a case for human follow-up based on any identified issues.
- At the same time, a notification manager passes updates on the processing of each transaction back to the client’s expense/procurement system.
To learn more, watch FICO’s presentation at MongoDB World 2018.
How Planable Uses MongoDB Atlas to Help Social Media Teams Move Their Creative Processes Forward
In 2015, Nicolae Gudumac was working at a social media agency managing hundreds of campaigns for clients. However, with each campaign requiring multiple rounds of review, he needed a way to streamline the disjointed feedback loop and bring everyone onto the same page. Along with his co-founders, Xenia Muntean and Vlad Calus, he began building a platform that would streamline this time-consuming process for social media managers, agencies, and their clients. The team created Planable , a platform that simplifies planning, visualizing, and approving social media posts. The tool feels like a live mock-up of the social feed, making it easy for teams to collaborate and give real-time feedback in a familiar format. Prioritizing Developer Velocity From the Beginning As the newly minted CTO, Nicolae decided to use MongoDB’s document data model to help his team innovate quickly to stay ahead of the ever-changing social media landscape. “With social media content, requirements are constantly changing as platforms evolve and introduce new formats. Having a flexible data model has been a boon to our productivity.” He also needed to build a foundation that could scale with them as the business grew. Since Planable is a collaboration tool that needs to keep users synced in real-time, Nicolae built the tool on top of Node.js and websockets. The team harnessed MongoDB’s oplog tailing functionality to send real-time updates to all connected users when relevant data changed and recently started to leverage MongoDB change streams to make this process more simple and scalable. To easily scale their app servers at peak times, Nicolae chose to run Planable on AWS container service. Migrating to MongoDB Atlas For managing their MongoDB deployment, the team started off using Compose.com but were having issues with restoring backups and only had access to a very limited set of configuration options. Compose also charged a premium for upgrading their storage engine to WiredTiger. MongoDB Atlas, with its queryable backup snapshots, automated upgrades, and configuration flexibility — especially the ease at which clusters can be scaled horizontally — looked very appealing. We needed to be able to provide our clients with a platform that is as reliable as we are. Atlas’ native cloud first and scale-out architecture aligned well with our increasing performance demands and usage growth. Nicolae Gudumac, CTO, Planable When Planable was accepted into the MongoDB Startup Accelerator , the timing was right to make the move to MongoDB Atlas. The team had customers all over the world and couldn’t afford any downtime with the migration. They used the Atlas Live Migration Service to move over their data from Compose with no downtime. Currently, the team of 6 is split between engineering and business. With a small engineering team, they’re hyper-focused on product improvements and new features that will drive the business forward. Atlas features such as the Real-Time Performance panel and the Performance Advisor, which monitors clusters for slow queries and automatically suggests indexes to improve performance, allow the team to dedicate more of their attention to application improvements. According to Nicolae, “The Performance Advisor has made indexing the database and optimizing queries a no-brainer." Queryable backups have also helped the team quickly address customer questions and as Nicolae recalls, “It’s saved us numerous times when someone accidentally dropped/updated a few documents and they needed to be restored. We’ve managed to quickly inspect and restore data in just a few clicks.” The team is looking forward to the upcoming release of MongoDB Charts which is currently available in beta. “Charts will enable our marketing and business team to gain insights from our database, without resorting to sophisticated and expensive BI tools” says Nicolae. Planable is bringing thousands onto their platform each month and are well on their way to becoming the default tool for social media collaboration. MongoDB Atlas has helped this fast-growing startup focus on helping social media teams move their process forward and allows Nicolae to give valuable time back to his team. “There’s no doubt in my mind that moving to MongoDB Atlas has increased our team’s productivity.” MongoDB Atlas is the easiest and fastest way to get started with MongoDB. Deploy a free cluster in minutes.
How DataSwitch And MongoDB Atlas Can Help Modernize Your Legacy Workloads
Data modernization is here to stay, and DataSwitch and MongoDB are leading the way forward. Research strongly indicates that the future of the Database Management System (DBMS) market is in the cloud, and the ideal way to shift from an outdated, legacy DBMS to a modern, cloud-friendly data warehouse is through data modernization. There are a few key factors driving this shift. Increasingly, companies need to store and manage unstructured data in a cloud-enabled system, as opposed to a legacy DBMS which is only designed for structured data. Moreover, the amount of data generated by a business is increasing at a rate of 55% to 65% every year and the majority of it is unstructured. A modernized database that can improve data quality and availability provides tremendous benefits in performance, scalability, and cost optimization. It also provides a foundation for improving business value through informed decision-making. Additionally, cloud-enabled databases support greater agility so you can upgrade current applications and build new ones faster to meet customer demand. Gartner predicts that by 2022, 75% of all databases will be on the cloud – either by direct deployment or through data migration and modernization. But research shows that over 40% of migration projects fail. This is due to challenges such as: Inadequate knowledge of legacy applications and their data design Complexity of code and design from different legacy applications Lack of automation tools for transforming from legacy data processing to cloud-friendly data and processes It is essential to harness a strategic approach and choose the right partner for your data modernization journey. We’re here to help you do just that. Why MongoDB? MongoDB is built for modern application developers and for the cloud era. As a general purpose, document-based, distributed database, it facilitates high productivity and can handle huge volumes of data. The document database stores data in JSON-like documents and is built on a scale-out architecture that is optimal for any kind of developer who builds scalable applications through agile methodologies. Ultimately, MongoDB fosters business agility, scalability and innovation. Key MongoDB advantages include: Rich JSON Documents Powerful query language Multi-cloud data distribution Security of sensitive data Quick storage and retrieval of data Capacity for huge volumes of data and traffic Design supports greater developer productivity Extremely reliable for mission-critical workloads Architected for optimal performance and efficiency Key advantages of MongoDB Atlas , MongoDB’s hosted database as a service, include: Multi-cloud data distribution Secure for sensitive data Designed for developer productivity Reliable for mission critical workloads Built for optimal performance Managed for operational efficiency To be clear, JSON documents are the most productive way to work with data as they support nested objects and arrays as values. They also support schemas that are flexible and dynamic. MongoDB’s powerful query language enables sorting and filtering of any field, regardless of how nested it is in a document. Moreover, it provides support for aggregations as well as modern use cases including graph search, geo-based search and text search. Queries are in JSON and are easy to compose. MongoDB provides support for joins in queries. MongoDB supports two types of relationships with the ability to reference and embed. It has all the power of a relational database and much, much more. Companies of all sizes can use MongoDB as it successfully operates on a large and mature platform ecosystem. Developers enjoy a great user experience with the ability to provision MongoDB Atlas clusters and commence coding instantly. A global community of developers and consultants makes it easy to get the help you need, if and when you need it. In addition, MongoDB supports all major languages and provides enterprise-grade support. Why DataSwitch as a partner for MongoDB? Automated schema re-design, data migration & code conversion DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a modern database platform. Our no-code and low-code solutions along with cloud data expertise and unique, automated schema generation accelerates time to market. We provide end-to-end data, schema and process migration with automated replatforming and refactoring, thereby delivering: 50% faster time to market 60% reduction in total cost of delivery Assured quality with built-in best practices, guidelines and accuracy Data modernization: How “DataSwitch Migrate” helps you migrate from RDBMS to MongoDB DataSwitch Migrate (“DS Migrate”) is a no-code and low-code toolkit that leverages advanced automation to provide intuitive, predictive and self-serviceable schema redesign from a traditional RDBMS model to MongoDB’s Document Model with built-in best practices. Based on data volume, performance, and criticality, DS Migrate automatically recommends the appropriate ETTL (Extract, Transfer, Transform & Load) data migration process. DataSwitch delivers data engineering solutions and transformations in half the timeframe of the existing typical data modernization solutions. Consider these key areas: Schema redesign – construct a new framework for data management. DS Migrate provides automated data migration and transformation based on your redesigned schema, as well as no-touch code conversion from legacy data scripts to MongoDB Atlas APIs. Users can simply drag and drop the schema for redesign and the platform converts it to a document-based JSON structure by applying MongoDB modeling best practices. The platform then automatically migrates data to the new, re-designed JSON structure. It also converts the legacy database script for MongoDB. This automated, user-friendly data migration is faster than anything you’ve ever seen. Here’s a look at how the schema designer works. Refactoring – change the data structure to match the new schema. DS Migrate handles this through auto code generation for migrating the data. This is far beyond a mere lift and shift. DataSwitch takes care of refactoring and replatforming (moving from the legacy platform to MongoDB) automatically. It is a game-changing unique capability to perform all these tasks within a single platform. Security – mask and tokenize data while moving the data from on-premise to the cloud. As the data is moving to a potentially public cloud, you must keep it secure. DataSwitch’s tool has the capability to configure and apply security measures automatically while migrating the data. Data Quality – ensure that data is clean, complete, trustworthy, consistent. DataSwitch allows you to configure your own quality rules and automatically apply them during data migration. In summary: first, the DataSwitch tool automatically extracts the data from an existing database, like Oracle. It then exports the data and stores it locally before zipping and transferring it to the cloud. Next, DataSwitch transforms the data by altering the data structure to match the re-designed schema, and applying data security measures during the transform step. Lastly, DS Migrate loads the data and processes it into MongoDB in its entirety. Process Conversion Process conversion, where scripts and process logic are migrated from legacy DBMS to a modern DBMS, is made easier thanks to a high degree of automation. Minimal coding and manual intervention are required and the journey is accelerated. It involves: DML – Data Manipulation Language CRUD – typical application functionality (Create, Read, Update & Delete) Converting to the equivalent of MongoDB Atlas API Degree of automation DataSwitch provides during Migration Schema Migration Activities DS Automation Capabilities Application Data Usage Analysis 70% 3NF to NoSQL Schema Recommendation 60% Schema Re-Design Self Services 50% Predictive Data Mapping 60% Process Migration Activities DS Automation Capabilities CRUD based SQL conversion (Oracle, MySQL, SQLServer, Teradata, DB2) to MongoDB API 70% Data Migration Activities DS Automation Capabilities Migration Script Creation 90% Historical Data Migration 90% 2 Catch Load 90% DataSwitch Legacy Modernization as a Service (LMaas): Our consulting expertise combined with the DS Migrate tool allows us to harness the power of the cloud for data transformation of RDBMS legacy data systems to MongoDB. Our solution delivers legacy transformation in half the time frame through pay-per-usage. Key strengths include: ● Data Architecture Consulting ● Data Modernization Assessment and Migration Strategy ● Specialized Modernization Services DS Migrate Architecture Diagram Contact us to learn more.