Webinar on MongoDB on Oct 30th
We’re doing a webinar on MongoDB on Oct 30, 2009 noon EST. It’ll be an overview of MongoDB & will also have Ian White from Business Insider talking about how they are using MongoDB in production:
Details & register at: http://mongodb1.eventbrite.com/
(The webinar is FREE)
We’ve been speaking about MongoDB at physical events like conferences and meetups. But since there’s interest in MongoDB from many different geographical locations, we thought we’d also do a webinar. This will be an interactive live web event. Look forward to seeing you there!
If you have questions on the webinar or have ideas for other such webinars shoot us an email at firstname.lastname@example.org.
Databases Should be Dynamically Typed
Software developers often debate the pros and cons of static versus dynamic typing in programming languages. Yet what about databases? Of course, static typing is traditional for databases. In a relational database we usual declare our columns and the datatype of each column’s values. However, we now see in the nosql space what are known as “schemaless” databases. Technically these products are often have some schema: for example in MongoDB we define collections and indexes. However, we do not predefine the structure of objects within those collections – they may all be different, or all the same. The typing is dynamic. Dynamically typed databases are a good fit with dynamically typed programming languages. It certainly feels like it would be a win to have a dynamically typed db when using a dynamically typed programming language (Ruby, PHP, Python, Erlang, …) How suboptimal it would be to have all the flexibility of dynamic typing in our code, and then hit a “brick wall” when we go to persist the data and have to statically spec everything out! There is synergy to be had between the dynamically typed programming language and the dynamically typed database. Dynamically typed databases can be a good thing when using statically typed programming languages. The best thing about static typing with compilers is that errors are reported at compile/development time. This is a big win for statically typed languages such as Java and C++. However, even with a statically typed database, type matching errors storing data are only reported at runtime! (That is, our java compiler doesn’t check our MySQL schema.) Thus some of the power of static typing in programming is lost at the storage layer. We still retain some benefits: assurance of some consistency to the data stored. But any failure to honor such a contract is only reported at runtime. Thus, it is more than worth considering using a “schemaless” database with say, Java, and getting out of the business of writing data migration scripts with each release. (Yes, some of that work stays but we can eliminate the majority.) Relational databases could be dynamically typed. While existing RDBMSes are statically typed, this is not an inherent limitation of the relational model. One could imagine a relational database with tables where one can dynamically insert a row with an extra column value at any time, and where values of cells in the same column of a table may have different types.
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