Event Sourcing is a powerful way to think about domain objects and transaction processing. Rather than persisting an object in its current state, event sourcing instead writes an immutable log of deltas (domain events) to the database. From this set of events, an object's state is derived, at any point in the past, simply by replaying the event history sequentially.
Additionally, event sourcing is a deceptively radical idea, which challenges our contemporary notions about transaction processing, while also being a mature pattern with a long history. Watch the video to see how event processing is used across a spectrum of use cases, including database engines and financial systems.
Receive alerts over Flowdock and Slack
Cloud Manager’s Alerts are a key feature, useful for keeping tabs on your deployment and alerting you when things have gone awry. We support a large number of notification services, including email, SMS, HipChat, and even Webhooks . The Cloud Manager team is proud to announce the addition of Flowdock and Slack to the long list of supported alert channels. Flowdock Here’s how to receive your alerts via Flowdock: 1) Get an Personal API key from your flowdock profile. 2) Either create a new alert or add a Flowdock endpoint to an existing alert 3) Then enter your organization name, flow name (specifically, the prefix of the flow’s email address), Flowdock API key, and choose your frequency options 4) Click “Save” and you are good to go Slack And here’s how to add Slack endpoints: 1) Head to https://api.slack.com/web to get a Web API token or https://api.slack.com/slackbot to get a bot API token 2) Either create a new alert or add a Slack endpoint to an existing alert 3) Choose the channel name and notification frequencies you want 4) Click “Save” and you are good to go
How Accenture’s Data Modernizer Tool Accelerates the Modernizing of Legacy and Mainframe Apps to MongoDB
As companies scale their MongoDB estates and apply them to more and more use cases, opportunities for deeper insights arise—but first come issues around data modeling. The Challenge It’s one thing to start modeling for a green field application where you have the ability to apply modeling patterns . But it’s a different matter entirely to start from an existing relational estate and take into account multiple data sources and their business requirements in an effort to create the ideal MongoDB data model based on explicit schema (table and relations) as well as implicit schema (access patterns and queries). Unfortunately, this is a fairly common obstacle. Perhaps an application has been around for a while and documentation hasn’t kept up with changes. Or maybe a company has lost talent and expertise in some areas of a monolithic app. These are just a couple of examples that demonstrate how time consuming and error-prone this effort can be. The Solution MongoDB and Accenture are excited to announce a modernization tool which enables faster MongoDB adoption by accelerating data modeling. As part of its mainframe offload and cloud modernization initiatives, Accenture’s Java-based data modernizer helps companies arrive at a recommended MongoDB document data model more quickly and efficiently. Using this tool results in a faster operational data layer for offloading mainframes and faster implementation of MongoDB Atlas for migrating applications to the cloud while also transforming them. Accenture and MongoDB are long-standing strategic partners, and this investment is another addition to our joint customers’ toolkits. “This modernizer is the right tool for teams who are focused on changing business requirements as they rethink their applications for the cloud. With on average 50% faster data modeling and 80% data model accuracy straight out of the tool, developers can work on modernizing instead of spending weeks figuring out the right data model to simply meet existing business requirements that have not been explicit.” (Francesco De Cassai) How it Works The modernizer analyzes access logs and relational schema from Oracle and DB2 databases, whether they’re powered by mainframes or not. This allows teams to obtain critical insights before kicking off development activities, thereby better informing the process. Additionally, the modernizer combines the implementation of modeling patterns , development best practices such as document sizing (best, average, worst case), and naming conventions, while also providing confidence levels to predict the accuracy of the model it’s suggested. The tool combines Accenture’s deep expertise and lessons learned from numerous successful projects. Our customers are investing in a new approach to managing data: Data as a Service & Data Decoupling. This strategic initiative focuses on consolidating and organizing enterprise data in one place, most often on the cloud, and then making it available to serve digital projects across the enterprise. MongoDB Atlas unlocks data from legacy systems to drive new applications and digital systems, without the need to disrupt existing backends as companies modernize and migrate to the cloud. With this tool and MongoDB capabilities, MongoDB and Accenture allow organizations to get the most out of their data. Find out more about the MongoDB & Accenture partnership here . Learn more about MongoDB’s Modernization Initiative .