Category: CloudHub

Lack of Connectivity Limits Marketing and Sales Engagement with Customers

As more companies adopt SaaS sales and marketing applications, 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, HootSuiteGoogle 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 .com? Look no further – follow these best practices from the dataloader.io 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 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.

Dataloader.io, MuleSoft’s #1 data loading app on the Salesforce AppExchange, just reached the 1 billion records processed mark!

As a special thanks to all of our dataloader.io pros, we’ve created a nifty for you to get the most out of your dataloader.io experience:

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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 .com is a 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 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.

Janet Revell on Monday, June 10, 2013

10 Little Mule Studio Gems

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Every so often, while using , I come across clever little gems that our team thoughtfully inserted into the product to improve usability. These gems don’t get a lot of fanfare, nor do they often warrant much attention on their own, but put together, they make for a smoother, intuitive user experience. Nearly invisible, they have become nearly indispensable to me.

 

#1 Wrap in and Extract to

At MuleSoft, we’ve been saying for years that point-to-point integration is evil. With time to market measured in minutes or hours, point-to-point integration projects measured in man-years are headed the way of the Dodo. And the no-software no-hardware model of iPaaS promises to shrink time to market even more.

But how fast can you deploy an enterprise-grade integration from scratch? We’re setting out to break preconceived notions of time to market with the 15-minute integration. Like the 4-minute mile before Roger Bannister, the 15-minute integration sounds like a myth. So is it for real?

Apache Cassandra is a column-based, distributed database.  Until recently the only way to interact with databases from Mule was to reuse one of the existing Java clients, like Hector or Astyanax, in a component.  Mule’s Cassandra DB Module now provides message processors to insert, update, query and delete data in Cassandra.

To show off some of the features of the Cassandra module I’ll show how to implement a simple account management API.  This API will allow clients to perform CRUD operations on accounts, behaving similarly to something like an LDAP directory.

Picture an architecture where production data gets painstakingly replicated to a very expensive secondary database, where, eventually, yesterday’s information gets analyzed. What’s the name for this “pattern”? If you answered “Traditional Business Intelligence (BI)”, you’ve won a rubber Mule and a warm handshake at the next Mule Summit!

As the volume of data to analyze kept increasing and the need to react in real-time became more pressing, new approaches to BI came to life: the so-called Big Data problem was recognized and a range of tools to deal with it started to emerge.

Apache Hadoop is one of these tools. It’s “an open-source software framework that supports data-intensive distributed applications. It supports the running of applications on large clusters of commodity hardware. Hadoop was derived from Google’s MapReduce and Google File System (GFS) papers” (Wikipedia). So how do you feed real-time data into Hadoop? There are different ways but one consists in writing directly to its primary data store named HDFS (aka Hadoop Distributed File System). Thanks to its Java client, this is very easily done in simple scenarios. If you start throwing concurrent writes and the need to organize data in specific directory hierarchies, its a good time to bring Mule into the equation.

In this post we will look at how Mule’s HDFS Connector can help you write time series data in HDFS, ready to be map-reduced to your heart’s content.