Ironfan: Your Foundation for Flexible Big Data Infrastructure

Ironfan is the foundation for your Big Data stack, bringing your system diagram to life. It makes it simple to configure, deploy, monitor, and modify your Big Data infrastructure.



Your Foundation for Flexible Big Data Infrastructure

Infochimps brings the power of Big Data infrastructure to your fingertips.
Traditional systems configuration is a time-consuming process, vulnerable to human error. Infochimps leverages the power and simplicity of Ironfan as its provisioning and deployment layer, allowing users to easily launch and orchestrate repeatable infrastructure.

Infochimps Cloud reduces cycle time to provision a server from days or weeks to minutes, enabling simple scaling and rapid system evolution while dramatically lowering the cost of starting new data analysis jobs. Infochimps even enables continual monitoring of your system through automated machine provisioning. Spend your time finding insights, not building infrastructure.

With Ironfan, you will get:

Reduced cycle time.
Provision servers in minutes not days.

Improved visibility.
Increased transparency means faster problem solving and sharing.

Lower support costs.
Experience fewer reactive support issues.

Lower network costs.
Only use the nodes you need for the job you are running.

Lower risk, more agility.
Deploy and manage a big data stack with minimal resources.

To see this visual image, download the White Paper now.

Why Infochimps Cloud?

Ironfan, Infochimps’ systems configuration tool, leverages three years of internal development and external contributions to its code base. This specialized experience helps organizations reduce the initial adoption cost and experimentation necessary to produce well-tuned clusters.

Infochimps’ tool development and Big Data expertise means our team understands and is equipped with the tools to successfully navigate and troubleshoot the entire Big Data ecosystem of an organization.

Flexible Cost.
Infochimps’ Ironfan lets you take advantage of IaaS (Infrastructure as a Service) providers such as Amazon Web Services. This allows for all infrastructure costs to be treated as operating expenses (use what you need) and not capital expenditures (pay whether you need it or not). Switching from CapEx to OpEx can dramatically lower the funding barrier to adopting Big Data internally in an enterprise.

Perhaps best of all, Infochimps Cloud, enabled by Ironfan, can be used to provide context to an enterprise’s internal data, whether through public opinion mining (via social networks), geo-located information, word corpus training for machine learning, and other commonly useful (but difficult to accumulate) data. All of these capabilities combine to make Infochimps a great choice for providing Big Data services to the budget and process-conscious enterprise customer.

Understanding the Tools

What is Chef?
Chef is a configuration management system, designed to be a general purpose tool for building repeatable infrastructure. It uses a Ruby DSL (Domain Specific Language) allowing you to write out specifications (as cookbooks, roles, etc.) for infrastructure that is fully composable.

Chef can be used in a number of ways, allowing it to fit into a variety of existing architectures. Its flexibility, however, means that it cannot as easily build higher-level abstractions on top of the architecture it provides.

What is Ironfan?
Ironfan, the foundation of Infochimps Cloud, is a systems provisioning and deployment tool. Ironfan automates not only machine configuration, but entire systems configuration to enable the entire Big Data stack, including tools for data ingestion, scraping, storage, computation, and monitoring.

Ironfan builds on Chef, but is opinionated about its architecture, which allows broader integration between components. It assumes a source repository, a central Chef Server, and a modern POSIX-compliant operating system for a base image. Currently, it works best with Git, Amazon Web Services and Ubuntu 11.04, with exploration into other virtualization platforms (Vagrant, etc.) and operating systems (Centos, FreeBSD, etc.) ongoing, both inside and outside of Infochimps.

Benefits for the Entire Team

For Systems Administrators, Ironfan removes the guesswork from building systems, because it reduces the cycle time to build a server from days or weeks to minutes. Instead of following long lists of manual processes, a system administrator makes changes to their Ironfan homebase, and then ushers those changes into the appropriate systems with the Chef knife and client programs. This enables rapid iterative development, a practice of Agile programming shops for years. Up until recently, this kind of fast-paced development was unavailable to the average systems administrator. Ironfan also enables repeatable architecture, another powerful tool. Now, replacing malfunctioning components with completely new ones, built from scratch and loaded with data from live exports or backups is a simple, reliable, and rapid process, instead of a last-ditch solution. Finally, Ironfan allows you to make infrastructure inevitable: you can write definitions, which automatically attach new servers to your existing architecture, instead of wiring into central services like monitoring, log ingestion, or orchestration manually, without the attendant risk of human error.

For Data Scientists or Business Intelligence Teams, Ironfan can currently build a Hadoop cluster from scratch in less than an hour with just a small handful of commands, and expand it in minutes with a single command. Other large scale cluster technologies (HBase, ElasticSearch, Redis, Flume, etc.) are just as simple to build. This dramatically reduces the cost of starting new data analysis jobs, allowing for greater experimentation. Because the underlying architecture is rented by machine-hour, jobs with predictable costs in machine-hours can be optimized for rapid execution without large increases in cost. Should the

For Systems Architects or Core Infrastructure Team, Ironfan allows you to build the repeatable architecture recommended by ITIL (Information Technology Infrastructure Library) for reliable IT infrastructure. It becomes simpler to scale or evolve systems rapidly. Ironfan takes the grunt-work out of distributing those changes, allowing architects to spend more of their focus on design details, instead of implementation details. Since everything is stored in source control, both architects and administrators can make changes to the infrastructure, confident that they are not obliterating important history. Also, the same code can be used to create development, staging, and production environments, the usual barriers to deployment caused by differences in the underlying architectures and deployment mechanisms are significantly reduced. Because starting new instances with Ironfan is trivial, and paid for by the hour, capacity can be managed as OpEx rather than CapEx. This also means that problems with huge capacity spikes can be considered; turning up a thousand nodes for three days, then turning them off again, is no longer a laughable fantasy. Migrations also become significantly easier, as new infrastructure can be spun up in parallel with the old, without a long term increase in expense.

What is TrstRank?

TrstRank is an Infochimps developed dataset and API that provides Twitter influence metrics. This API provides Twitter influence metrics with the click of a button! TrstRank measures Twitter user reputation, importance and influence in a far more robust way than counting the number of followers. It is a sophisticated measure of a user’s relative importance within the entire Twitter network.

Case Study:
How Infochimps Uses Ironfan to Create TrstRank

Since the launch of Twitter, people have clamored for ways to access and “slice and dice” its data. One of the most common ways people use the Twitter data corpus is to measure a person’s importance and influence. Klout is an example of one product that specializes in this kind of “influencer” data.

A few years ago, we created our own special version of Klout, one that took advantage of our vast historical record of the relationships to create an accurate number describing how influential a Twitter user is. It’s called TrstRank and it ranks a user on a scale of 1-10, with 10 being the most influential you can get.

To see this visual image, download the White Paper now.

Coming up with such a number like TrstRank is no small task. Setting aside the issues of getting the data, there are some very real Big Data problems surrounding the product that require special tools for getting it done efficiently. And when you’re a bootstrapped startup, like we were at the time, you have to be resourceful if you are going to get by.

The biggest issue with pursuing a new data product like TrstRank is the same one any company faces when they decide to venture into new territory - the high risks of wasting time and money.

Wasting Time
One of the first problems you run into as a small team trying your hand at data science is the excess time spent on server and machine configuration, instead of focusing on modeling, algorithms, and manipulating the data. Ramp-up time for even the first phase of a project like TrstRank can be a whole day or more of engineering time.

Wasting Money
From our earliest days Infochimps has been based on Amazon Web Services’ (AWS) cloud, taking advantage of the flexibility and scalability it provides. With AWS, you pay for what you use, so you are always inclined to eliminate waste. In our early days we even created decision trees for when to shut down a cluster or not, depending on how many hours it was to be up but not used.

This can set conflicting goals for the data scientist who would prefer to leave a cluster up overnight, even if it’s unused, so they don’t have to deal with setting everything up again the next day!

Enter Ironfan
We created Ironfan to solve our own problems of how to save time and money during our data science operations in the cloud. When we came up with the idea for TrstRank, it was a simple operation to spin up a cluster for early analysis and experimentation. We could validate some of our algorithms and ideas on a simple cluster before moving to something more heavyweight.

Ironfan and TrstRank, Now
Ironfan has continued as a key tool for our monthly TrstRank operation. We continue to scrape Twitter for follower information, and with the updated data every month we crunch the TrstRank numbers again. With Ironfan, we’re able to run a multiple step operation on 8 billion tweets on clusters of 30 m1.xlarge EC2 machines, while only running the resources we need when they’re needed. TrstRank takes 72 hours to complete, with resources being paid for commensurately. Without Ironfan, we’d be looking at 2-3x the costs in time and money!

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See Infochimps Cloud for Big Data.

Infochimps Cloud is a suite of enterprise-ready cloud services that make it simpler, faster and far less complicated to develop and deploy Big Data applications in public, virtual private and private clouds.

Our cloud services provide a comprehensive analytics platform, including data streaming, storage, queries and administration. With Infochimps, you focus on the analytics that drive your business insights, not building and managing a complex infrastructure.