In the Internet of things no device is an island. And while Raspberry Pi devices are pretty cool on their own adding an API makes them a lot more interesting. We have been playing around with Raspberry for a while now and have a small distribution of Mule, called ‘Anypoint Edge’ that happily runs on small embeddable devices like the Raspberry Pi. These ARM-based devices are taking the world by storm since they are lower powered, low cost and can be embedded into small hubs to control other things like lightbulbs, or be used inside anything from PoS kiosks to gas pumps to cars to medical devices.
You may have read about our mountain trek, new release cycle, and the increased pace of delivering. We’ve climbed our first mountain and we’re happy to announce the availability of our first release in our trek: Andes! This delivers major usability improvements around our platform, new connectivity to applications such as Marketo and ZenDesk, and expanded API management capabilities. We’ll summarize what’s new for you here and we’ll be doing deeper dives over the coming days for you to learn more.
After creating a basic Mule App, you might be wondering how to automate the process of deploying a Mule App to CloudHub. In this post, we are introducing a Maven plugin that enables that use case. As a result a Mule App will be deployed automatically to CloudHub after a Maven build. This is achieved using the goal cloudhub-deploy from the Mule AppKit Maven Plugin.
In a ideal development workflow, each time the project builds the Mule Application will be deployed to the cloud providing a cutting edge instance that can be used for QA of the latest snapshot. Both Bamboo or Jenkins can be configured in order to run Maven and deploy the Mule App to CloudHub.
It’s pretty common to hear and read about how everything in the IT business is going “as a service…”. So you start hearing about Software as a Service (SaaS), Platform as a Serivce (PaaS) and even Integration Platform as a Service (iPaaS, which is where our very own CloudHub platform plays on). But what about data?
Lack of Connectivity Limits Marketing and Sales Engagement with Customers
As more companies adopt SaaS sales and marketing applications, SaaS providers are under the gun to create and offer functionality that supports the business process and automation requirements of these individual and sometimes silo teams. In any given organization, sales and marketing use upwards of 10 – 15 applications to engage, onboard and maintain customer interactions. Believe it or not, here at MuleSoft our marketing and sales teams use over 30 different applications. Yes 30, and we have less than 30 people in our marketing organization! Sample applications include, HootSuite, Google Apps, Confluence, Yammer, Salesforce, SurveyMonkey, WebEx Events, Eventbrite, Cloud9 Analytics, KISSmetrics, Google Adwords, GetSatisfaction and the list goes on. Each of these applications are used to engage the customer in a different stage of the buying process:
Calling all Salesforce.com users! Looking for a fast and easy way to move data in and out of Salesforce.com? Look no further – follow these best practices from the dataloader.io community to quickly become a data loading pro:
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At MuleSoft, we’re constantly looking for better ways to solve the integration needs of enterprises. Software as a service is creating new integration requirements and Gartner expects the SaaS market to continue growing at a blistering pace of 17.9% through the end of 2013. With the adoption of SaaS in Europe and Asia Pacific accelerating, we talk to customers every day looking to integrate in the cloud across geographies, and isolate certain data to comply with data protection laws.
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As an avid observer of the Salesforce.com data loading community, I thought it would be interesting to share some of the trends we see within our dataloader.io user base heading into the summer. How do these data loading trends compare to the activities within your organization?
SaaS is one size fits all (kind of)
Even though Salesforce.com is a SaaS based application, there is quite a bit of customization that takes place inside account, opportunity and contact records. While the majority of datalaoder.io users have standard objects, roughly ⅓ are routinely updating, upserting or inserting (importing) custom objects into their salesforce.com orgs.
Mule has a very extensive support for NoSQL data stores, which covers pretty much the whole spectrum of what’s available out there, from key/value stores to document-oriented databases. The only piece that was missing in the puzzle was connectivity to a graph database: with the introduction of the Neo4j connector, the gap is now closed.
Popularized by the advent of social media, the need for efficiently storing, indexing, traversing and querying graphs of objects has become prominent in less than a decade. During this time, Neo4j has risen to the number one graph database on the market, with successful deployments across all types of industries and a strong commitment to open source.
The new connector, presented in this blog, allows Mule users to leverage the incredibly rich API that Neo4j offers with convenient configuration elements. Read on to discover a simple example built with this connector.