Dwight Merriman Named EY Entrepreneur Of The Year™ 2014 Award finalist in New York
May 27, 2014 | Updated: March 7, 2016
EY recently announced that Co-founder and Chairman Dwight Merriman of MongoDB, Inc. is a finalist for the EY Entrepreneur Of The Year™ 2014 Award in the New York region. The awards program recognizes entrepreneurs who demonstrate excellence and extraordinary success in such areas as innovation, financial performance and personal commitment to their businesses and communities. Dwight Merriman was selected as a finalist by a panel of independent judges. Award winners will be announced at a special gala event on Tuesday, June 17th at the Marriott Marquis.
Now in its 28th year, the program has expanded to recognize business leaders in more than 145 cities in more than 60 countries throughout the world.
Regional award winners are eligible for consideration for the EY Entrepreneur Of The Year National program. Award winners in several national categories, as well as the EY Entrepreneur Of The Year National Overall Award winner, will be announced at the annual awards gala in Palm Springs, California, on November 15, 2014. The awards are the culminating event of the EY Strategic Growth Forum®, the nation’s most prestigious gathering of high-growth, market-leading companies.
About EY Entrepreneur Of The Year™
EY Entrepreneur Of The Year™ is the world’s most prestigious business award for entrepreneurs. The unique award makes a difference through the way it encourages entrepreneurial activity among those with potential and recognizes the contribution of people who inspire others with their vision, leadership and achievement. As the first and only truly global award of its kind, Entrepreneur Of The Year celebrates those who are building and leading successful, growing and dynamic businesses, recognizing them through regional, national and global awards programs in more than 145 cities in more than 60 countries.
MongoDB Backup Strategies Compared
In a recent webinar , MongoDB VP of Education & Cloud Services, Andrew Erlichson, delivered a presentation comparing and contrasting different MongoDB backup strategies. We thought we’d continue on that topic by comparing these options in-depth on the MMS blog. There are three main approaches for backing up MongoDB: mongodump, a utility bundled with the MongoDB database Filesystem snapshots, such as those provided by Linux LVM or AWS EBS MongoDB Management Service (MMS) , a fully managed, cloud service that provides continuous backup for MongoDB (also available as on-prem software with a MongoDB subscription ) Let’s look at each of these strategies in further detail. Backup Strategy #1: mongodump mongodump is a tool bundled with MongoDB that performs a backup of the data in MongoDB. mongodump may be used to dump an entire database, collection, or result of a query. mongodump can produce a consistent snapshot of the data by dumping the oplog. You can then use the mongorestore utility to restore the data to a new or existing database. mongorestore will import content from the BSON database dumps produced by mongodump and replay the oplog. mongodump is a straightforward approach and has the benefit of producing backups that can be filtered based on your specific needs. While mongodump is sufficient for small deployments, it is not appropriate for larger systems. mongodump exerts too much load to be a truly scalable solution. It is not an incremental approach, so it requires a complete dump at each snapshot point, which is resource-intensive. As your system grows, you should evaluate lower impact solutions such as filesystem snapshots or MMS. In addition, while the complexity of deploying mongodump for small configurations is fairly low, the complexity of deploying mongodump in large sharded systems can be significant. Backup Strategy #2: Copying the Underlying Files You can back up MongoDB by copying the underlying files that the database processes uses to store data. To obtain a consistent snapshot of the database, you must either stop all writes to the database and use standard file system copy tools, or create a snapshot of the entire file system, if your volume manager supports it. For example, Linux LVM quickly and efficiently creates a consistent snapshot of the file system that can be copied for backup and restore purposes. To ensure that the snapshot is logically consistent, you must have journaling enabled within MongoDB. Because backups are taken at the storage level, filesystem snapshots can be a more efficient approach than mongodump for taking full backups and restoring them. However, unlike mongodump, it is a more coarse approach in that you don’t have the flexibility to target specific databases or collections in your backup. This may result in large backup files, which in turn may result in long-running backup operations. To implement filesystem snapshots requires ongoing maintenance as your system evolves and becomes more complex. To coordinate backups across multiple replica sets, particularly in a sharded system, requires devops expertise to ensure consistency across the various components. Backup Strategy #3: MongoDB Management Service (MMS) MongoDB Management Service provides continuous, online backup for MongoDB as a fully managed service. You install the Backup Agent in your environment, which conducts an initial sync to MongoDB’s secure and redundant datacenters. After the initial sync, MMS streams encrypted and compressed MongoDB oplog data to MMS so that you have a continuous backup. By default, MMS takes snapshots every 6 hours and oplog data is retained for 24 hours. The snapshot schedule and retention policy can be configured to meet your requirements. You also have the flexibility to exclude non-mission critical databases and collections. For replica sets, a custom, point-in-time snapshot can be restored for any moment in the last 24 hours. For sharded system, MMS produces a consistent snapshot of the cluster every 6 hours. You also have the option of using checkpoints for more granular restores of sharded clusters. Because MMS reads only the oplog, the ongoing performance impact is minimal, similar to that of adding an additional node to the replica set. In addition to the cloud-based service, MMS is available as on-prem software as part of a MongoDB Standard or Enterprise Subscription . Learn More: To get a more in-depth review of MongoDB backup strategies, read Backup and its Role in Disaster Recovery .
Dissecting Open Banking with MongoDB: Technical Challenges and Solutions
Thank you to Ainhoa Múgica for her contributions to this post. Unleashing a disruptive wave in the banking industry, open banking (or open finance), as the term indicates, has compelled financial institutions (banks, insurers, fintechs, corporates, and even government bodies) to embrace a new era of transparency, collaboration, and innovation. This paradigm shift requires banks to openly share customer data with third-party providers (TPPs), driving enhanced customer experiences and fostering the development of innovative fintech solutions by combining ‘best-of-breed’ products and services. As of 2020, 24.7 million individuals worldwide used open banking services, a number that is forecast to reach 132.2 million by 2024. This rising trend fuels competition, spurs innovation, and fosters partnerships between traditional banks and agile fintech companies. In this transformative landscape, MongoDB, a leading developer data platform, plays a vital role in supporting open banking by providing a secure, scalable, and flexible infrastructure for managing and protecting shared customer data. By harnessing the power of MongoDB's technology, financial institutions can lower costs, improve customer experiences, and mitigate the potential risks associated with the widespread sharing of customer data through strict regulatory compliance. Figure 1: An Example Open Banking Architecture The essence of open banking/finance is about leveraging common data exchange protocols to share financial data and services with 3rd parties. In this blog, we will dive into the technical challenges and solutions of open banking from a data and data services perspective and explore how MongoDB empowers financial institutions to overcome these obstacles and unlock the full potential of this open ecosystem. Dynamic environments and standards As open banking standards continue to evolve, financial institutions must remain adaptable to meet changing regulations and industry demands. Traditional relational databases often struggle to keep pace with the dynamic requirements of open banking due to their rigid schemas that are difficult to change and manage over time. In countries without standardized open banking frameworks, banks and third-party providers face the challenge of developing multiple versions of APIs to integrate with different institutions, creating complexity and hindering interoperability. Fortunately, open banking standards or guidelines (eg. Europe, Singapore, Indonesia, Hong Kong, Australia, etc) have generally required or recommended that the open APIs be RESTful and support JSON data format, which creates a basis for common data exchange. MongoDB addresses these challenges by offering a flexible developer data platform that natively supports JSON data format, simplifies data modeling, and enables flexible schema changes for developers. With features like the MongoDB Data API and GraphQL API , developers can reduce development and maintenance efforts by easily exposing data in a low-code manner. The Stable API feature ensures compatibility during database upgrades, preventing code breaks and providing a seamless transition. Additionally, MongoDB provides productivity-boosting features like full-text search , data visualization , data federation , mobile database synchronization , and other app services enabling developers to accelerate time-to-market. With MongoDB's capabilities, financial institutions and third-party providers can navigate the changing open banking landscape more effectively, foster collaboration, and deliver innovative solutions to customers. An example of a client who leverages MongoDB’s native JSON data management and flexibility is Natwest. Natwest is a major retail and commercial bank in the United Kingdom based in London, England. The bank has moved from zero to 900 million API calls per month within years, as open banking uptake grows and is expected to grow 10 times in coming years. At a MongoDB event on 15 Nov 2022, Jonathan Haggarty, Natwest’s Head of “Bank of APIs” Technology – an API ecosystem that brings the retail bank’s services to partners – shared in his presentation titled Driving Customer Value using API Data that Natwest’s growing API ecosystem lets it “push a bunch of JSON data into MongoDB [which makes it] “easy to go from simple to quite complex information" and also makes it easier to obfuscate user details through data masking for customer privacy. Natwest is enabled to surface customer data insights for partners via its API ecosystem, for example “where customers are on the e-commerce spectrum”, the “best time [for retailers] to push discounts” as well insights on “most valuable customers” – with data being used for problem-solving; analytics and insight; and reporting. Performance In the dynamic landscape of open banking, meeting the unpredictable demands for performance, scalability, and availability is crucial. The efficiency of applications and the overall customer experience heavily rely on the responsiveness of APIs. However, building an open banking platform becomes intricate when accommodating third-party providers with undisclosed business and technical requirements. Without careful management, this can lead to unforeseen performance issues and increased costs. Open banking demands high performance of the APIs under all kinds of workload volumes. OBIE recommends an average TTLB (time to last byte) of 750 ms per endpoint response for all payment invitations (except file payments) and account information APIs. Compliance with regulatory service level agreements (SLAs) in certain jurisdictions further adds to the complexity. Legacy architectures and databases often struggle to meet these demanding criteria, necessitating extensive changes to ensure scalability and optimal performance. That's where MongoDB comes into play. MongoDB is purpose-built to deliver exceptional performance with its WiredTiger storage engine and its compression capabilities. Additionally, MongoDB Atlas improves the performance following its intelligent index and schema suggestions, automatic data tiering, and workload isolation for analytics. One prime illustration of its capabilities is demonstrated by Temenos, a renowned financial services application provider, achieving remarkable transaction volume processing performance and efficiency by leveraging MongoDB Atlas. They recently ran a benchmark with MongoDB Atlas and Microsoft Azure and successfully processed an astounding 200 million embedded finance loans and 100 million retail accounts at a record-breaking 150,000 transactions per second . This showcases the power and scalability of MongoDB with unparalleled performance to empower financial institutions to effectively tackle the challenges posed by open banking. MongoDB ensures outstanding performance, scalability, and availability to meet the ever-evolving demands of the industry. Scalability Building a platform to serve TPPs, who may not disclose their business usages and technical/performance requirements, can introduce unpredictable performance and cost issues if not managed carefully. For instance, a bank in Singapore faced an issue where their Open APIs experienced peak loads and crashes every Wednesday. After investigation, they discovered that one of the TPPs ran a promotional campaign every Wednesday, resulting in a surge of API calls that overwhelmed the bank's infrastructure. A scalable solution that can perform under unpredictable workloads is critical, besides meeting the performance requirements of a certain known volume of transactions. MongoDB's flexible architecture and scalability features address these concerns effectively. With its distributed document-based data model, MongoDB allows for seamless scaling both vertically and horizontally. By leveraging sharding , data can be distributed across multiple nodes, ensuring efficient resource utilization and enabling the system to handle high transaction volumes without compromising performance. MongoDB's auto-sharding capability enables dynamic scaling as the workload grows, providing financial institutions with the flexibility to adapt to changing demands and ensuring a smooth and scalable open banking infrastructure. Availability In the realm of open banking, availability becomes a critical challenge. With increased reliance on banking services by third-party providers (TPPs), ensuring consistent availability becomes more complex. Previously, banks could bring down certain services during off-peak hours for maintenance. However, with TPPs offering 24x7 experiences, any downtime is unacceptable. This places greater pressure on banks to maintain constant availability for Open API services, even during planned maintenance windows or unforeseen events. MongoDB Atlas, the fully managed global cloud database service, addresses these availability challenges effectively. With its multi-node cluster and multi-cloud DBaaS capabilities, MongoDB Atlas ensures high availability and fault tolerance. It offers the flexibility to run on multiple leading cloud providers, allowing banks to minimize concentration risk and achieve higher availability through a distributed cluster across different cloud platforms. The robust replication and failover mechanisms provided by MongoDB Atlas guarantee uninterrupted service and enable financial institutions to provide reliable and always-available open banking APIs to their customers and TPPs. Security and privacy Data security and consent management are paramount concerns for banks participating in open banking. The exposure of authentication and authorization mechanisms to third-party providers raises security concerns and introduces technical complexities regarding data protection. Banks require fine-grained access control and encryption mechanisms to safeguard shared data, including managing data-sharing consent at a granular level. Furthermore, banks must navigate the landscape of data privacy laws like the General Data Protection Regulation (GDPR), which impose strict requirements distinct from traditional banking regulations. MongoDB offers a range of solutions to address these security and privacy challenges effectively. Queryable Encryption provides a mechanism for managing encrypted data within MongoDB, ensuring sensitive information remains secure even when shared with third-party providers. MongoDB's comprehensive encryption features cover data-at-rest and data-in-transit, protecting data throughout its lifecycle. MongoDB's flexible schema allows financial institutions to capture diverse data requirements for managing data sharing consent and unify user consent from different countries into a single data store, simplifying compliance with complex data privacy laws. Additionally, MongoDB's geo-sharding capabilities enable compliance with data residency laws by ensuring relevant data and consent information remain in the closest cloud data center while providing optimal response times for accessing data. To enhance data privacy further, MongoDB offers field-level encryption techniques, enabling symmetric encryption at the field level to protect sensitive data (e.g., personally identifiable information) even when shared with TPPs. The random encryption of fields adds an additional layer of security and enables query operations on the encrypted data. MongoDB's Queryable Encryption technique further strengthens security and defends against cryptanalysis, ensuring that customer data remains protected and confidential within the open banking ecosystem. Activity monitoring With numerous APIs offered by banks in the open banking ecosystem, activity monitoring and troubleshooting become critical aspects of maintaining a robust and secure infrastructure. MongoDB simplifies activity monitoring through its monitoring tools and auditing capabilities. Administrators and users can track system activity at a granular level, monitoring database system and application events. MongoDB Atlas has Administration APIs , which one can use to programmatically manage the Atlas service. For example, one can use the Atlas Administration API to create database deployments, add users to those deployments, monitor those deployments, and more. These APIs can help with the automation of CI/CD pipelines as well as monitoring the activities on the data platform enabling developers and administrators to be freed of this mundane effort and focus on generating more business value. Performance monitoring tools, including the performance advisor, help gauge and optimize system performance, ensuring that APIs deliver exceptional user experiences. Figure 2: Activity Monitoring on MongoDB Atlas MongoDB Atlas Charts , an integrated feature of MongoDB Atlas, offers analytics and visualization capabilities. Financial institutions can create business intelligence dashboards using MongoDB Atlas Charts. This eliminates the need for expensive licensing associated with traditional business intelligence tools, making it cost-effective as more TPPs utilize the APIs. With MongoDB Atlas Charts, financial institutions can offer comprehensive business telemetry data to TPPs, such as the number of insurance quotations, policy transactions, API call volumes, and performance metrics. These insights empower financial institutions to make data-driven decisions, improve operational efficiency, and optimize the customer experience in the open banking ecosystem. Figure 3: Atlas Charts Sample Dashboard Real-Timeliness Open banking introduces new challenges for financial institutions as they strive to serve and scale amidst unpredictable workloads from TPPs. While static content poses fewer difficulties, APIs requiring real-time updates or continuous streaming, such as dynamic account balances or ESG-adjusted credit scores, demand capabilities for near-real-time data delivery. To enable applications to immediately react to real-time changes or changes as they occur, organizations can leverage MongoDB Change Streams that are based on its aggregation framework to react to data changes in a single collection, a database, or even an entire deployment. This capability further enhances MongoDB’s real-time data and event processing and analytics capabilities. MongoDB offers multiple mechanisms to support data streaming, including a Kafka connector for event-driven architecture and a Spark connector for streaming with Spark. These solutions empower financial institutions to meet the real-time data needs of their open banking partners effectively, enabling seamless integration and real-time data delivery for enhanced customer experiences. Conclusion MongoDB's technical capabilities position it as a key enabler for financial institutions embarking on their open banking journey. From managing dynamic environments and accommodating unpredictable workloads to ensuring scalability, availability, security, and privacy, MongoDB provides a comprehensive set of tools and features to address the challenges of open banking effectively. With MongoDB as the underlying infrastructure, financial institutions can navigate the ever-evolving open banking landscape with confidence, delivering innovative solutions, and driving the future of banking. Embracing MongoDB empowers financial institutions to unlock the full potential of open banking and provide exceptional customer experiences in this era of collaboration and digital transformation. If you would like to learn more about how you can leverage MongoDB for your open banking infrastructure, take a look at the below resources: Open banking panel discussion: future-proof your bank in a world of changing data and API standards with MongoDB, Celent, Icon Solutions, and AWS How a data mesh facilitates open banking Financial services hub