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
Revolutionizing Data Storage and Analytics with MongoDB Atlas on Google Cloud and HCL
Every organization requires data they can trust—and access—regardless of its format, size, or location. The rapid pace of change in technology and the shift towards cloud computing is revolutionizing how companies handle, govern and manage their data by freeing them from the heavy operational burden of on-premise deployments. Enterprises are looking for a centralized, cost-effective solution that allows them to scale their storage and analytics so they can ingest data and perform artificial intelligence (AI) and machine learning (ML) operations, ultimately expanding their marketing horizon. This blog post explores why companies should partner with MongoDB Atlas on Google Cloud to begin their data revolution journey, and how HCL Technologies can support customers looking to migrate. MongoDB Atlas as the distributed data platform MongoDB Atlas is the leading database-as-a-service on the market for three main reasons: Unparalleled developer experience - allows organizations to bring new features to market at a high velocity Horizontal scalability - supports hundreds of terabytes of data with sub-second queries Flexibility - stores data to meet various regulatory, operational, and high availability requirements. The versatility offered by MongoDB’s document model makes it ideal for modern data-driven use cases that require support for structured, semi-structured, and unstructured content all within a single platform. Its flexible schema allows changes to support new application features without costly schema migrations typically required with relational databases. MongoDB Atlas extends the core database by offering services like Atlas Search and MongoDB Realm that are a necessity for modern applications. Atlas Search provides a powerful Apache Lucene-based full text search engine that automatically indexes data in your MongoDB database without the need for a separate dedicated search engine or error-prone replication processes. Realm provides edge-to-cloud sync and backend services to accelerate and simplify mobile and web development. Atlas’ distributed architecture supports horizontal scaling for data volume, query latency, and query throughput which offers the scalability benefits of distributed data storage alongside the rich functionality of a fully-featured general purpose database. MongoDB Atlas is unique in its ability to provide the most wanted database as a managed service and is relied on by the world’s largest companies for their mission-critical production applications. Innovation powered by collaboration with HCL Technologies MongoDB’s versatility as a general-purpose database, in addition to its massive scalability, makes it a perfect foundation for analytics, visualization, and AI/ML applications on Google Cloud. As an MSP partner for Google Cloud, HCL Technologies helps enterprises accelerate and risk-mitigate their digital agenda, powered by Google Cloud. We’ve successfully implemented applications leveraging MongoDB Atlas on Google Cloud, building upon MongoDB’s flexible JSON-like data model, rich querying and indexing, and elastic scalability in conjunction with Google Cloud’s class-leading cloud infrastructure, data analytics, and machine learning capabilities. HCL is working with some of the world’s largest enterprises in building secure, performant, and cost-effective solutions with MongoDB and Google. Possessing technical expertise in Google Cloud, MongoDB, machine learning, and data science, our dedicated team developed a reference architecture that ensures high performance and scalability. This is simplified by MongoDB Atlas’ support for Google Cloud services which allows it to essentially operate as a cloud-native solution. Highlighted features include: Integration with Google Cloud Key Management Service Use of Google Cloud’s native storage snapshot for fast backup and restore Ability to create read-only MongoDB nodes in Google Cloud to reduce latency with Google Cloud-native services regardless of where the primary node is located (even other public cloud providers!) Integrated billing with Google Cloud Ability to span a single MongoDB cluster across Google Cloud regions worldwide, and more As represented in Figure 1 below, MongoDB Atlas on Google Cloud can be used as a single database solution for transactional, operational, and analytical workloads across a variety of use cases. Figure 1: MongoDB's core characteristics and features The following architecture in Figure 2 demonstrates the ease of reading and writing data to MongoDB from Google Cloud services. Dataflow, Cloud Data Fusion, and Dataproc can be leveraged to build data pipelines to migrate data from heterogeneous databases to MongoDB and to feed data to create interactive dashboards using Looker. These data pipelines support both batch and real-time ingestion workloads and can be automated and orchestrated using Google Cloud - native services.. Figure 2: MongoDB Atlas' integration with core Google Cloud services A data platform built using MongoDB Atlas and Google Cloud offers an integrated suite of services for storage, analysis, and visualization. Address your business challenges with HCL: Industry use cases Data-driven solutions built with MongoDB Atlas on Google Cloud have multiple applications across industries such as financial services, media and entertainment, healthcare, oil and gas, energy, manufacturing, retail, and the public sector. Every industry can benefit from this highly integrated storage and analytical solution. Use Cases and Benefits Data lake modernization with low cost and high availability for media and entertainment customers: Maintaining high availability and a low-cost data lake is an obstacle for any online entertainment platform that builds mobile or web ticketing applications. However, building on Google App Engine with MongoDB Atlas Clusters in the backend allows for a high-availability, low-cost data platform that seamlessly feeds data to downstream analytics platforms in real time. Unified data platform for retail customers: The retail business frequently requests an agile environment in order to encourage innovation among its engineers. With its agility in scaling and resource management, seamless multi-region clusters, and premium monitoring, running MongoDB Atlas on Google Cloud is a fantastic choice for building a single data platform. This simplifies the management of different data platforms and allows developers to focus on new ideas. High-speed real-time data platform of supply chain system for manufacturing units: By having real-time visibility and distributed data services, supply chain data can become a competitive advantage. MongoDB Atlas on Google Cloud provides a solid foundation for creating distributed data services with a unified, easy-to-maintain architecture. The unrivaled speed of MongoDB Atlas simplifies supply chain operations with real-time data analytics. The way forward Even in just the past decade, organizations have been forced to adapt to the extremely fast pace of innovation in the data analytics landscape: moving from batch to real-time, on-premise to cloud, gigabytes to petabytes, and the increased accessibility of advanced AI/ML models thanks to providers like Google Cloud. With our track record of success in this domain, HCL Technologies is uniquely positioned to help organizations realize the joint benefits of building data analytics applications with best-of-breed solutions from Google Cloud and MongoDB. Visit us to learn more about the HCL Google Ecosystem Business Unit and how we can help you harness the power of MongoDB Atlas and Google Cloud Platform to change the way you store and analyze your data through these solutions.