Channel 4, one of the UK's most popular television networks, recently won an award in the "Best Technical Innovation" category at the Online Media awards. The website awarded called Scrapbook is a place to tag and collect content from the Channel 4's TV shows, was built with MongoDB in just 19 weeks.
Read the full writeup on CBR Online
Big Data Is The New Normal
Now Big Data has even won a Gartner Seal of Approval, so to speak, with the publication of a new report that says Big Data is on the fast track to maturity and by 2016 it will just be data. The huge idea here is that as information goes cross-platform and vaults up in volume, very shortly Big Data will become another norm of how business gets done. By no means will all data be Big Data - that would make no sense because there of course are key values found in certain, specific data warehouses that are quite small and structured. But there also is the fact that, suddenly, business realizes it is awash in data and it knows that if only it can harness the insights that can be derived from the information already on hand or soon to be, great value will ensue. Big data transforming bricks + mortar retailing Think about the recent New York Times story about how pioneering retailers are tracking customer behavior by monitoring cellphone signals -- WiFi in particular. The story triggered significant teeth gnashing by privacy proponents, and these concerns are understandable. But a reality is that e-commerce players already have enormous tracking data on their customers and it stands to reason that, finally, bricks and mortar retailers would want to level the playing field. Big data plus smartphones is an equation that works. Put the privacy debate aside. Think simply about the information flow, its volume and the insights it would give retailers. That is very big data indeed and the goal would be mashing it up and then reinventing store layout, product placement, and in effect making it easier for consumers to find and buy what they came into the store for. Really knowing the customer Probably a gold standard for pursuing BIg Data is Netflix, which is well known for gathering information about what their customers watch but also how they watch it - where do they fast forward, where do they rewind, when do they simply turn off a film and never return? But then Netflix goes farther with its data, per reporting in SiliconANGLE : “[Netflix] actually [is] putting that data to use. Netflix has begun to produce its own original TV shows, and to do so its leveraging all of its data to do it. Netflix used its data to decide that the BBC’s ‘House of Cards’ was the best fit for a remake, and its data also correlated fans of the original to fans of actor Kevin Spacey and director David Fincher, which in turn was what led to them being hired.” Think about the power there: Big data is driving complex decisions and, apparently, it is helping get closer to what consumers really want. The maturation of Big Data A safe bet is that such stories - revolutionary as they sound in 2013 - will seem commonplace within a very few years because, right now, the ingredients are all coming together for a flowering of Big Data into an everyday business tool and probably 2016, as Gartner predicts, is as good a guess as any. Certainly it will become more commonplace in many more businesses very soon, about now in fact as the first generation pioneers - with their massive data stores and new analytical tools for rapidly making sense out of them -- start to enjoy differentially superior results. They are demonstrating that Big Data works, period. It’s no longer a computer science project, it’s becoming just plain business. The irony is that when Big Data becomes humdrum - when it loses its buzzword status - that is when it genuinely will have solidified a role as a transformational information utility. By, say, 2020 we will look back and be puzzled at how organizations arrived at their marketing and design decisions without Big Data. It will seem every bit as puzzling as, say, how organizations maintained customer data befor CRM (can you say 3” x 5” card?). From where we sit in 2013, that future seems distant indeed. But it also is closer than we think.
4 Critical Features for a Modern Payments System
The business systems of many traditional banks rely on solutions that are decades old. These systems, which are built on outdated, inflexible relational databases, prevent traditional banks from competing with industry disruptors and those already adopting more modern approaches. Such outdated systems are ill-equipped to handle one of the core offerings that customers expect from banks today — instantaneous, cashless, digital payments . The relational database management systems (RDBMSes) at the core of these applications require breaking data structures into a complex web of tables. Originally, this tabular approach was necessary to minimize memory and storage footprints. But as hardware has become cheaper and more powerful, these advantages have also become less relevant. Instead, the complexity of this model results in data management and programmatic access issues. In this article, we’ll look at how a document database can simplify complexity and provide the scalability, performance, and other features required in modern business applications. Document model To stay competitive, many financial institutions will need to update their foundational data architecture and introduce a data platform that enables a flexible, real-time, and enriched customer experience. Without this, new apps and other services won’t be able to deliver significant value to the business. A document model eliminates the need for an intricate web of related tables. Adding new data to a document is relatively easy and quick since it can be done without the usually lengthy reorganization that RDBMSes require. What makes a document database different from a relational database? Intuitive data model simplifies and accelerates development work. Flexible schema allows modification of fields at any time, without disruptive migrations. Expressive query language and rich indexing enhance query flexibility. Universal JSON standard lets you structure data to meet application requirements. Distributed approach improves resiliency and enables global scalability. With a document database, there is no need for complicated multi-level joins for business objects, such as a bill or even a complex financial derivative, which often require object-relational mapping with complex stored procedures. Such stored procedures, which are written in custom languages, not only increase the cognitive load on developers but also are fiendishly hard to test. Missing automated tests present a major impediment to the adoption of agile software development methods. Required features Let’s look at four critical features that modern applications require for a successful overhaul of payment systems and how MongoDB can help address those needs. 1. Scalability Modern applications must operate at scales that were unthinkable just a few years ago, in relation to both transaction volume and to the number of development and test environments needed to support rapid development. Evolving consumer trends have also put higher demands on payment systems. Not only has the number of transactions increased, but the responsive experiences that customers expect have increased the query load, and data volumes are growing super-linear. The fully transactional RDBMS model is ill suited to support this level of performance and scale. Consequently, most organizations have created a plethora of caching layers, data warehouses, and aggregation and consolidation layers that create complexity, consume valuable developer time and cognitive load, and increase costs. To work efficiently, developers also need to be able to quickly create and tear down development and test environments, and this is only possible by leveraging the cloud. Traditional RDBMSes, however, are ill suited for cloud deployment. They are very sensitive to network latency, as business objects spread across multiple tables can only be retrieved through multiple sequential queries. MongoDB provides the scalability and performance that modern applications require. MongoDB’s developer data platform also ensures that the same data is available for use with other frequent consumption patterns like time series and full-text search . Thus, there is no need for custom replication code between the operational and analytical datastore. 2. Resiliency Many existing payment platforms were designed and architected when networking was expensive and slow. They depend on high-quality hardware with low redundancy for resilience. Not only is this approach very expensive, but the resiliency of a distributed system can never be reached through redundancy. At the core of MongoDB’s developer data platform is MongoDB Atlas , the most advanced cloud database service on the market. MongoDB Atlas can run in any cloud, or even across multiple clouds, and offers 99.995% uptime. This downtime is far less than typically expected to apply necessary security updates to a monolithic legacy database system. 3. Locality and global coverage Modern computing demands are at once ubiquitous and highly localized. Customers expect to be able to view their cash balances wherever they are, but client secrecy and data availability rules set strict guardrails on where data can be hosted and processed. The combination of geo-sharding, replication, and edge data addresses these problems. MongoDB Atlas in combination with MongoDB for Mobile brings these powerful tools to the developer. During the global pandemic, more consumers than ever have begun using their smartphones as payment terminals. To enable these rich functions, data must be held at the edge. Developing the synchronization of the data is difficult, however, and not a differentiator for financial institutions. MongoDB for Mobile, in addition with MongoDB’s geo-sharding capability on Atlas cloud, offloads this complexity from the developer. 4. Diverse workloads and workload isolation As more services and opportunities are developed, the demand to use the same data for multiple purposes is growing. Although legacy systems are well suited to support functions such as double entry accounting, when the same information has to be served up to a customer portal, the central credit engine, or an AI/ML algorithm, the limits of the relational databases become obvious. These limitations have resulted in developers following what is often called “best-of-breed” practices. Under this approach, data is replicated from the transactional core to a secondary, read-only datastore based on technology that is better suited to the particular workload. Typical examples are transactional data stores being copied nightly into data lakes to be available for AI/ML modelers. The additional hardware and licensing cost for this replication are not prohibitive, but the complexity of the replication, synchronization, and the complicated semantics introduced by batch dumps slows down development and increases both development and maintenance costs. Often, three or more different technologies are necessary to facilitate the usage patterns. With its developer data platform, MongoDB has integrated this replication, eliminating all the complexity for the developers. When a document is updated in the transactional datastore, MongoDB will automatically make it available for full-text search and time series analytics. The pace of change in the payments industry shows no signs of slowing. To stay competitive, it’s vital that you reassess your technology architecture. MongoDB Atlas is emerging as the technology of choice for many financial services firms that want to free their data, empower developers, and embrace disruption. Replacing legacy relational databases with a modern document database is a key step toward enhancing agility, controlling costs, better addressing consumer expectations, and achieving compliance with new regulations. Learn more by downloading our white paper “Modernize Your Payment Systems."