Used by top brands like Panasonic, Verizon Wireless, Audi, and more, Adobe Experience Manager (AEM) is one of the leading enterprise content management systems on the market.
AEM 6.0 is the next step forward in enabling marketing teams to create, manage, and optimize digital customer experiences across channels. It features support for MongoDB as a new persistence layer, which introduces capabilities for AEM customers such as:
- Unlimited scalability and increased flexibility - Scale both publish and author instances beyond the limits of a single server. With MongoDB, the AEM and persistence layers can be scaled independently of one another.
- Support for globally distributed teams - A single shared MongoDB deployment can be deployed across sites.
- Automatic failover - With auto-healing replica sets, MongoDB reduces the need for manual intervention in the event of an outage.
- Increased control over data placement - MongoDB allows fine-grained control over data placement within the cluster. With location-aware partitioning, content can be distributed to region-specific nodes to reduce latency, or split based on custom rules.
- And more…
Adobe Experience Manager has been certified on MongoDB Enterprise Advanced, a finely-tuned package of advanced software, support, certifications, and other services designed for running mission-critical applications.
Next week, we will on site at Adobe Summit 2015 to talk more about how MongoDB can benefit AEM customers. If you’re attending, be sure to come visit us at booth 1014.
We are also hosting a webinar at the end of the month on how clustering Adobe Experience Manager is made easy with MongoDB. The session will be a more in-depth look at how you can use MongoDB to deploy AEM systems at scale. Sign up here.
Interested in talking to us now about using MongoDB for your AEM deployment? Click below to get in touch with someone who can help you immediately:
Automate operational management of MongoDB with HP Operations Bridge
This is a guest blog post by Manoj Mohanan, Technical Marketing Specialist at HP. Over the past few years, non-relational databases have seen tremendous growth and acceptance across industries, with MongoDB leading the way. The database has now made considerable inroads into enterprise companies, with over a third of the Fortune 100 utilizing it to power their next-generation and mission-critical applications. This wide adoption makes MongoDB monitoring a necessary feature in many infrastructure and application monitoring systems. Due to an increasingly dynamic and complex IT landscape, operations personnel are often tasked with becoming proficient in monitoring and managing newly introduced technologies. Perhaps just as important, they also face the need to bring their newly acquired knowledge and processes into their existing end-to-end operations; without a tight integration, it may be difficult to understand how the behavior of one of their new technologies impacts the wider business. For organizations looking to drive innovation with new technologies, these operational challenges are significant, especially given the sheer breadth of tools now available to developers. A more modern approach to IT Operations management can drastically reduce the number of events presented to operators and automate the discovery of business service dependencies. These capabilities help filter and correlate events to remove noise and get to the root cause of each issue. The result is faster mean time to repair, with some customers reaching as much as 75 percent reduction in event volumes and 90 percent improvements in MTTR. The HP Operations Bridge solution uniquely addresses these challenges by providing the means to sense the status and analyze the state of service delivery. It dynamically and automatically discovers and correlates—even as the environment changes—three sets of data: Event data that indicates infrastructure- or service-impacting issues Topology data that ties the IT infrastructure to the business services that rely on it Metrics that describe the availability and performance of the business service and its dependencies HP Operations Bridge has just been improved with the announcement of OMi version 10, a core product in the Operations Bridge solution. With over 100+ integrations, it covers many of the technologies that are being used in today’s IT ecosystem to fuel various business apps, e.g. MongoDB, Vertica, Docker, VMWare, HortonWorks and many more. Learn more in the HP blog, Our Connected World Seen Through the Spanish Looking Glass . While MongoDB does offer domain-specific monitoring via MMS and Ops Manager, some operations teams may require monitoring to be available through a consolidated console. Using HP Operations Manager i and the newly released OMi Management Pack for MongoDB , operations teams can now monitor the health of MongoDB and all their other IT components using a single Operations Bridge Console. This first release of new OMi Management Pack for MongoDB provides out of the box capabilities to collect, monitor and visualize the critical availability and performance metrics of MongoDB instances. Collect and store critical availability and performance metrics Monitor the health and generate alerts Visualize the MongoDB topology, its dependencies, and its associated health Analytics With this and other management packs, HP Operations Bridge v10.00 expands IT capabilities, providing the innovative power to analyze the state of IT resources. HP Operations Bridge v10.00 provides predictive analytics out of the box. The metrics you capture from MongoDB monitoring and all other monitoring is brought to the single pane of glass for IT Operations Management. The metrics collected by the MongoDB management pack can then be fed to HP’s patented Predictive Analytics. Customers using this integrated capability gain predictive alerts, which may save them hundreds of thousands of dollars by giving their operations teams an early warning alert before a service disruption impacts business users and revenues. MongoDB monitoring data can also be analyzed in real time along with log files to uncover unpredicted and unknown events that otherwise would not have been tracked. HP Operations Analytics adds the lightning fast capabilities of Big Data analytics to enhance monitoring capabilities. Operators add the power of a “time machine” approach to their event driven analytics to search across the complex array of data sets monitoring provides. Some IT teams have gained as much as 72x improvement on MTTR, maximizing the ROI on their OMi Management packs. Takeaway HP Operations Bridge with the OMi Management Pack for MongoDB allows you to monitor your entire MongoDB deployment (and your entire IT ecosystem) from a single pane of glass. This integration between MongoDB and HP Operations Bridge will be further strengthened by integration with the Ops Manager API, which will offer additional comprehensive monitoring metrics and automation capabilities in the future. Learn more and download the OMi Management Pack for MongoDB here . Visit and explore other integrations with HP Operations Bridge here . Interested in learning more about MongoDB Operations Best Practices? Read our white paper: Download Ops Best Practices
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."