How Our Improved Modernization Toolkit Can Help You Navigate Your Data Center Exit

Prasad Pillalamarri
September 30, 2020 | Updated: April 26, 2021
#Technology#partners

Leaving your data center and moving to the cloud is a big change — and for many organizations, it’s a change that desperately needs to be made. To help you assess your data center exit plans, MongoDB has enhanced the Modernization Toolkit. The scorecard criteria now cover three main categories: considerations for data center exit and entry to the cloud; assessment of cloud data platforms; and finally, individual application-level qualification matrices. You can download the latest modernization toolkit here.

How to use the Modernization Scorecard

The current MongoDB Modernization Scorecard helps evaluate the suitability of various databases for new applications and for the modernization of legacy applications. It scores MongoDB and other databases against multiple application requirements, such as:

  • Data modeling
  • Query requirements
  • Performance & scalability
  • Availability & disaster recovery
  • Operational management
  • Deployment model & TCO

Now, the scorecard has been enhanced to help assess fully managed offerings such as MongoDB Atlas and other cloud-native database services. Some of our criteria include:

  • Freedom from platform lock-in
  • Automated capacity management
  • Support for multi-document ACID transactions across distributed environments
  • Support for graph traversals
  • Full-text search functionality
  • Multi-region deployment flexibility
  • Data partitioning options
  • Operational parameters concerning monitoring, backup, and data security.

There is also a new section covering data center exit criteria. Here we’ll help you look at considerations such as:

  • Migration of large volumes of data to support modern applications
  • Data type and query complexity of applications
  • Resiliency and uptime
  • Security and redaction strategies in the cloud
  • Cost comparisons: cloud-based, modern databases versus relational databases running on a mainframe in a private data center.

MongoDB Atlas and Cloud Migration

Your choices of data management platform will be a key factor in a successful migration from your data center to the cloud. The MongoDB Cloud section of the architecture guide, available here, discusses the unique value MongoDB can bring to organizations making the transition to cloud.

The key components of the MongoDB cloud platform are:

MongoDB Cloud platform key components image

  • At its core is MongoDB, the general purpose operational database for modern applications. Nearly every application needs a fast database that can deliver single digit millisecond response times. And with its flexible document data model, transactional guarantees, rich and expressive query language, and native support for both vertical and horizontal scaling, MongoDB can be used for practically any use case, reducing the need for specialized databases even as your requirements change.

  • We deliver MongoDB as a fully managed cloud service with MongoDB Atlas. Atlas is available on 70+ regions across AWS, GCP, and Azure. Best-in-class infrastructure and database automation, and proven practices guarantee continuous availability, elastic scalability, and compliance with the most demanding data security and privacy standards. Advanced features like auto-scaling and intelligent guidance to help optimize your deployments ensure you find the right balance between performance and efficiency.

  • Atlas Search is built into MongoDB Atlas, making it easy to build fast full-text search capabilities on top of your MongoDB data with no need to learn a different API or deploy a separate search technology.

  • Atlas Data Lake is an on-demand, serverless query service that allows you to perform analysis on data in S3 in-place using the MongoDB query language. Support for federated queries allows you to combine and analyze data across your cloud object storage and your Atlas database clusters together with a single query.

  • Realm Mobile Database extends this data foundation to the edge. Realm is a lightweight database embedded on the client. Realm helps solve the unique challenges of building for mobile, making it simple to store data on-device and enabling data access even when offline. Realm Sync is seamlessly integrated and keeps data up-to-date across devices and users by automatically syncing data between the client and a backend Atlas cluster.

  • MongoDB Realm goes further, simplifying the code required to stand up both mobile and web applications. Realm’s SDKs give developers the tools needed to access data stored in MongoDB Atlas directly from the client, and interact with the platform’s application services.

  • MongoDB Charts gives you the fastest and easiest way to create visualizations of MongoDB data. Build charts in minutes, then assemble and share dashboards. With the Charts SDK, it’s easy to embed charts into applications, enriching them with live data visualizations.

Download the enhanced modernization toolkit today.
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Accelerating to T+1 - Have You Got the Speed and Agility Required to Meet the Deadline?

On May 28, 2024, the Securities and Exchange Commission (SEC) will implement a move to a T+1 settlement for standard securities trades , shortening the settlement period from 2 business days after the trade date to one business day. The change aims to address market volatility and reduce credit and settlement risk. The shortened T+1 settlement cycle can potentially decrease market risks, but most firms' current back-office operations cannot handle this change. This is due to several challenges with existing systems, including: Manual processes will be under pressure due to the shortened settlement cycle Batch data processing will not be feasible To prepare for T+1, firms should take urgent action to address these challenges: Automate manual processes to streamline them and improve operational efficiency Event-based real-time processing should replace batch processing for faster settlement In this blog, we will explore how MongoDB can be leveraged to accelerate manual process automation and replace batch processes to enable faster settlement. What is a T+1 and T+2 settlement? T+1 settlement refers to the practice of settling transactions executed before 4:30pm on the following trading day. For example, if a transaction is executed on Monday before 4:30 pm, the settlement will occur on Tuesday. This settlement process involves the transfer of securities and/or funds from the seller's account to the buyer's account. This contrasts with the T+2 settlement, where trades are settled two trading days after the trade date. According to SEC Chair Gary Gensler , “T+1 is designed to benefit investors and reduce the credit, market, and liquidity risks in securities transactions faced by market participants.” Overcoming T+1 transition challenges with MongoDB: Two unique solutions 1. The multi-cloud developer data platform accelerates manual process automation Legacy settlement systems may involve manual intervention for various tasks, including manual matching of trades, manual input of settlement instructions, allocation emails to brokers, reconciliation of trade and settlement details, and manual processing of paper-based documents. These manual processes can be time-consuming and prone to errors. MongoDB (Figure 1 below) can help accelerate developer productivity in several ways: Easy to use: MongoDB is designed to be easy to use, which can reduce the learning curve for developers who are new to the database. Flexible data model: Allows developers to store data in a way that makes sense for their application. This can help accelerate development by reducing the need for complex data transformations or ORM mapping. Scalability: MongoDB is highly scalable , which means it can handle large volumes of trade data and support high levels of concurrency. Rich query language: Allows developers to perform complex queries without writing much code. MongoDB's Apache Lucene-based search can also help screen large volumes of data against sanctions and watch lists in real-time. Figure 1: MongoDB's developer data platform Discover the developer productivity calculator . Developers spend 42% of their work week on maintenance and technical debt. How much does this cost your organization? Calculate how much you can save by working with MongoDB. 2. An operational trade store to replace slow batch processing Back-office technology teams face numerous challenges when consolidating transaction data due to the complexity of legacy batch ETL and integration jobs. Legacy databases have long been the industry standard but are not optimal for post-trade management due to limitations such as rigid schema, difficulty in horizontal scaling, and slow performance. For T+1 settlement, it is crucial to have real-time availability of consolidated positions across assets, geographies, and business lines. It is important to note that the end of the batch cycle will not meet this requirement. As a solution, MongoDB customers use an operational trade data store (ODS) to overcome these challenges for real-time data sharing. By using an ODS, financial firms can improve their operational efficiency by consolidating transaction data in real-time. This allows them to streamline their back-office operations, reduce the complexity of ETL and integration processes, and avoid the limitations of relational databases. As a result, firms can make faster, more informed decisions and gain a competitive edge in the market. Using MongoDB (Figure 2 below), trade desk data is copied into an ODS in real-time through change data capture (CDC), creating a centralized trade store that acts as a live source for downstream trade settlement and compliance systems. This enables faster settlement times, improves data quality and accuracy, and supports full transactionality. As the ODS evolves, it becomes a "system of record/golden source" for many back office and middle office applications, and powers AI/ML-based real-time fraud prevention applications and settlement risk failure systems. Figure 2: Centralized Trade Data Store (ODS) Managing trade settlement risk failure is critical in driving efficiency across the entire securities market ecosystem. Luckily, MongoDB integration capabilities (Figure 3 below) with modern AI and ML platforms enable banks to develop AI/ML models that make managing potential trade settlement fails much more efficient from a cost, time, and quality perspective. Additionally, predictive analytics allow firms to project availability and demand and optimize inventories for lending and borrowing. Figure 3: Event-driven application for real time monitoring Summary Financial institutions face significant challenges in reducing settlement duration from two business days (T+2) to one (T+1), particularly when it comes to addressing the existing back-office issues. However, it's crucial for them to achieve this goal within a year as required by the SEC. This blog highlights how MongoDB's developer data platform can help financial institutions automate manual processes and adopt a best practice approach to replace batch processes with a real-time data store repository (ODS). With the help of MongoDB's developer data platform and best practices, financial institutions can achieve operational excellence and meet the SEC's T+1 settlement deadline on May 28, 2024. In the event of T+0 settlement cycles becoming a reality, institutions with the most flexible data platform will be better equipped to adjust. Top banks in the industry are already adopting MongoDB's developer data platform to modernize their infrastructure, leading to reduced time-to-market, lower total cost of ownership, and improved developer productivity. Looking to learn more about how you can modernize or what MongoDB can do for you? Zero downtime migrations using MongoDB’s flexible schema Accelerate your digital transformation with these 5 Phases of Banking Modernization Reduce time-to-market for your customer lifecycle management applications MongoDB’s financial services hub

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