Introducing Keynote Speakers for MongoDB World: Part 2
June 2, 2017 | Updated: July 7, 2017
At this year’s MongoDB World, our all-star lineup of keynotes is so packed that we couldn’t fit them all in one blog post. In addition to comprehensive technical sessions, one-on-one consultations, and networking with industry professionals, join us at MongoDB World to hear from these tech industry leaders.
Meet some of our featured keynote speakers:
Saška Mojsilović, IBM Fellow & Scientist, IBM T. J. Watson Research CenterSaška Mojsilović manages IBM’s Data Science department and is the founder of IBM Science for Social Good. She’s one of the pioneers of business analytics, has authored 100+ publications, and holds 16 patents.
Claudio Gosiker, Director, Capability Planning, Controllership, Enterprise Architecture, & Test Automation, Florida BlueClaudio Gosiker has 25 years of experience spanning the insurance, technology, and retail industries. He is an experienced leader, trusted technical advisor, and mentor.
Matt Parker, Standup MathematicianPossibly the only person to hold the prestigious title of London Mathematical Society Popular Lecturer while having a sold-out comedy show, Matt is always keen to mix his passions of mathematics and stand-up. He is also a keen rubik’s cube solver, ranking 63,327th in the world.
Dev Ittycheria, President & CEO, MongoDBDev Ittycheria has a unique mix of entrepreneurial, operational, and investing experience. He also serves as an advisor and board member to next-generation software companies.
After being inspired by our keynote speakers, strengthen your skills at any of the 80+ sessions. Join us at MongoDB World to learn best practices for using MongoDB, directly from the experts.
Bond & MongoDB: Delivering Thoughtfulness at Scale Using MongoDB Atlas & AWS
On the third floor of a pre-war building in Manhattan’s Chelsea neighborhood, you might not expect to stumble upon a fleet of hundreds of handwriting robots. However, in the offices of Bond , that’s exactly what you’ll find. Bond began in 2013 as a gifting company, adorning each of their gifts with a handwritten note. It soon became clear that the note (and not the gift) would be the kickstart to Bond’s success. Bond’s notes are generated with proprietary machine learning algorithms that mimic the way we write letters. The team examines the way different letters of the alphabet relate to each other and recreate that effect using NodeJS and their purpose-built robotic fleet. It’s one of the few companies where you’ll find calligraphers sitting alongside software engineers. Selecting MongoDB over MySQL While novelty may be part of the reason Bond’s notes catch the attention of millions of senders and recipients across the world, the company’s mission is more elegant: to equip anyone with the technology to be more thoughtful to the important people in their lives. This mission resonated with thousands of new Bond customers , who quickly pushed the limits of Bond’s existing technical infrastructure. Originally built on MySQL running in Amazon Relational Database Service (RDS), the platform through which customers create and order notes was seeing upwards of 1,000 read operations per second. This read workload came at the expense of write consistency. The business was scaling exponentially but their database wasn’t keeping pace. Before long, the engineering team was spending more cycles troubleshooting issues with the datastore rather than building out the core product offering. Bond’s CTO began evaluating other options with a particular focus on NoSQL databases for their horizontal scalability. However, the team quickly realized that most NoSQL databases weren’t ready for primetime—they either lacked the required querying capabilities or were too infrastructure-intensive for their rapidly-growing requirements. MongoDB was ultimately selected for its robust ecosystem, expressive query language, and scalability. Migrating to MongoDB Initially, Bond chose to continue to route write operations to MySQL and pass them to a hosted MongoDB instance where the data could be read at a much higher frequency. However, the team has since migrated completely to MongoDB as their database of record. Ensuring a more stable IOPS load enabled the platform to scale, and therefore allowed Bond to process more orders. In the 6 months after migrating to MongoDB, Bond fulfilled twice as many orders than in the previous 2 years on MySQL. Throughout the process, the team also transitioned from working with PHP to building predominantly in Node with Python for machine learning. Having used a managed service on AWS for MySQL, Bond's team was eager to hand over the day-to-day management of the database so they turned to Compose.io, a third party MongoDB service provider. While offloading their MongoDB management to a Compose-hosted deployment on AWS enabled the team to return focus to the consumer-facing portions of their app, it became apparent that the lack of encryption and features in the most recent releases of MongoDB were becoming a security and operational hurdle. Finding MongoDB Atlas Prompted by their need for end-to-end encryption and the upcoming support for the Decimal 128 data type in MongoDB 3.4 , Bond began migrating their data from Compose to MongoDB Atlas shortly after its debut in the summer of 2016. MongoDB Atlas exposed all of the latest functionality of the underlying database, allowing Bond’s technology to not only keep pace with their rapidly-growing business, but to also accelerate to the point where innovation is now driving their business growth. The team has since built a machine data analytics platform to understand and optimize the performance of their robotic fleet, allowing them to fulfill more orders with the same proprietary infrastructure. Using the Connector for Apache Spark , Bond is also using machine learning to extract usage data from MongoDB to anticipate the needs of their many types of customers. To see Bond in action, watch our video with Chief Product Officer, Sam Broe:
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