In a world of mobile apps and cloud resources, it’s easier than ever to start a business around a simple application. Even in large companies, developers fighting for resources might need to prove an application’s popularity or find a way to boost its monetization. Sometimes, that might even mean injecting some data-processing right into an application.

But whatever the case, if your job revolves around writing code rather than data flows, you might need a little help. Here are 12 tools (listed alphabetically) that aim to help. As usual with this type of list, it’s very possible I left out some good options, so please note any omissions in the comments.

1. BitDeli

BitDeli, a startup that launched in November, lets programmers measure pretty much whatever application metrics they want using Python scripts. Co-founder and CEO Ville Tuulos told me at the time that scripts can be as simple or complex as necessary — even going so far as to incorporate machine learning. Compared with the heavyweight Hadoop, BitDeli thinks of itself as the lightweight Ruby on Rails for analytics.

2. Continuuity

The brainchild of former Yahoo Chief Cloud Architect Todd Papaioannou and Facebook HBase engineer Jonathan Gray, Continuuity wants to help all companies operate like its founders’ former employers. The team created a big data fabric that abstracts the complexities of connecting to Hadoop and HBase clusters and includes a full suite of developer tools. The goal is to make it easy to write big data applications serving either internal or external audiences.

3. Flurry

Flurry is like a one-stop mobile-app shop, and it’s generating nearly $100 million a year in revenue because it’s good at what it does. Not only does the company help developers build mobile apps, but helps them analyze all the data those apps are generating in order to make them even better. The data also underpins the company’s ad network that helps developers monetize their apps by putting the right advertisers in front of the right users.

4. Google Prediction API

Of all the tools in Google’s developer toolbox, the Google Prediction API might be the coolest. If you have good data to train a model, the Prediction API can bring machine learning to work on it in order to discern any number of pattern types and feed the answers into your application. Among the examples Google gives are spam detection, recommendation engines and sentiment analysis — and it gives step-by-step instructions for building those models.

“Mucho bueno” is probably Spanish.

5. Infochimps

Although Infochimps is trying hard to make itself an enterprise IT company (hey, that’s where the money is), the company’s eponymous platform also provides a real value for developers. Sitting atop its technologies for configuring and managing big data environments is Wukong, a framework for creating Hadoop jobs or streaming data flows using Ruby scripts. Infochimps also maintains a data marketplace full of API-accessible or downloadable datasets.

6. Keen IO

Keen IO won our Structure 2012 Launchpad competition with a message of delivering powerful analytics to mobile developers. With just a single line of code inserted that dictates what to track, the company claims developers can track pretty much whatever they want within their applications. At that point, it’s just a matter of creating a dashboard or query process in order to turn all that data into usable information.

7. Kontagent

Kontagent‘s bread-and-butter business is its analytics platform for mobile, social and web applications, but it’s all built atop a Hadoop infrastructure designed to handle really big data. Earlier this year, the company turned that infrastructure loose with a new product that lets users mine their application data using the SQL-like Hive query language for Hadoop. Instead of tracking predetermined variables, they can dig in however they choose.

8. Mortar Data

Mortar Data is Hadoop for developers, plain and simple. The company has offered its cloud service — which replaces MapReduce with a combination Pig and Python — for almost a year. In November, it released the open source Mortar framework in order to build a community around sharing datasets and making it easier to write Hadoop pipelines. Mortar Data runs atop Amazon Web Services and currently supports Amazon S3 and MongoDB (hosted on Amazon EC2) as data sources.

9. Placed Analytics

Placed does away with scripts, APIs and any other developer legwork and just delivers the results. In the case of Placed, those results are detailed information about where and when consumers are actually using mobile apps and web sites — right down to the name of the business. This type of info can be useful for attracting advertisers as well as informing app design (e.g., implementing voice controls if people are using an app while driving).

10. Precog

Precog might look like any other proprietary business intelligence service, but underneath its covers there’s a twist. The company offers a service called Labcoat, which is an interactive development environment for writing analytics jobs based on the open source Quirrel query language. The IDE includes a tutorial for learning the language, as well as some complex functions, and Precog COO Jeff Carr told me even non-technical people can learn it in hours.

11. Spring for Apache Hadoop

Hadoop is written in Java, but that doesn’t mean it’s easy for Java developers to learn or use. That’s why, in early 2012, SpringSource announced the Spring for Apache Hadoop project, which brings the ease of building Java applications with the Spring framework to Hadoop jobs. That means integration with other Spring apps, scripting using JVM-based languages and a generally easier way to develop applications that utilize Hadoop or related technologies such as Hive or HBase.

12. StatsMix

In the same vein as BitDeli and Keen IO, StatsMix wants to let developers start collecting and analyzing application data using the languages they already know. The service automatically tracks certain metrics, but developers can add their own using the StatsMix API and predefined code libraries. The results are delivered via a collection of dashboards that users can customize, share and use to mashup multiple data sources into a single view.

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The company calls its product the AppFabric, and it’s essentially a software development kit and a set of high-level APIs sitting above a fabric layer that connects to all of a company’s various Hadoop clusters. Co-founder and CEO Todd Papaioannou, former entrepreneur in residence at Battery Ventures and VP and chief cloud architect at Yahoo, says Continuuity wants to make it easy for developers at fast-follower companies to build big data applications without having to learn Hadoop’s low-level APIs and the general complexity of the distributed framework. Very few people, he said, really want to be part of the ”home-brew computing club experience.”

In some ways, Continuuity is trying to do for big data what Heroku and other PaaS providers have done for web applications. There’s a drag-and-drop user interface, an integrated developer environment to make development easy on a laptop before pushing an application into production, and resources scale elastically.  Papaioannou said developers get a UI for DevOps, as well, and can connect Continuuity apps with server-side web apps wherever they’re hosted, even on other PaaS offerings.

Presently, Continuuity is available in private beta for its single-node developer version and private cloud version. It expects a public cloud version will be available at some point in 2013. The platform currently supports Java applications only — a natural first step given that Hadoop is written in Java — but Papaioannou said Continuuity plans to support other languages going forward.

Papaioannou’s fellow co-founder, Jonathan Gray, who was integral in building Facebook’s Messaging and Puma analytics applications on top of the Hadoop-based HBase database, says even early big data adopters might be interested in what Continuuity is selling. For example, he explained, a company like Facebook will have separate clusters for data warehousing, for HBase and for caching. A product like AppFabric would abstract the differences between those various systems and allow developers to easily write applications connecting to any or all of them.

Even if they’ve made it easy for non-engineer developers to write batch-processing jobs (like Facebook has with Hive for Hadoop), both Gray and Papaioannou say large companies can have hard time giving developers even more functionality. AppFabric might let developers create streaming or transactional apps, too, without requiring a whole new set of internally developed tools.

Really, Papaioannou says, the idea behind a PaaS for big data is no different than that behind platforms as a service, generally. The key question is: “Can a programmer sit down in an afternoon and develop something he can take to his boss?” Any company that can really make this happen for Hadoop — be it Continuuity, Infochimps, Mortar Data or whomever — should be in a good position to grow. Hadoop skills are in high demand but short supply, and there are plenty of developers waiting to get their hands on all that data-processing power.

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The company has described its cloud platform, which is hosted on can run the Rackspace Cloud, as Heroku for Hadoop — although that characterization is becoming antiquated. While Hadoop is certainly an important part of the Infochimps stack, it’s actually not the focal point. “Usually, people come to us because they have a big data problem and heard they should look at Hadoop,” Infochimps CEO Joe Kelly told me, but they end up accomplishing a lot before they ever turn to Hadoop.

What often happens, Infochimps Chief Strategy Officer Dhruv Bansal said, is customers use the platform to build applications that can ingest, process and analyze data, only turning to Hadoop when they get to the point where they actually need to batch analyze large volumes of data. It’s this experience, he said, that led to the focus on the real-time features in the new release.

The new streaming analytics engine, called Data Delivery Service, is based on Apache Flume and lets Infochimps users process data as it flows into their systems. Using Wukong, a Ruby-based command line interface, developers can write big data applications that take advantage of Data Delivery Service, or Hadoop, using a simple grammar that doesn’t involve learning MapReduce or how to work with Flume.

Using Wukong to abstract development

Although, the platform does support other high-level Hadoop languages such as Hive and Pig. “Wukong is one way to interact,” Bansal said, “it’s not the only way.”

With the new version of the platform, developers also have a feature called Deploy Pack, which lets them write and test code locally and then easily push it to the cloud production environment with a single command. Thanks to Ironfan, Infochimps’ infrastructure automation tool, databases, Hadoop clusters and whatever else an application needs will launch with minimal developer effort.

Infochimps can run on users’ cloud infrastructure of choice, and has partnered with Rackspace to run on its OpenStack-based cloud computing platform.

However, the Heroku for Hadoop tag is also a little premature because the Infochimps Platform is still a relatively high-touch service. Although the development and deployment experience is relatively simple, users can’t yet get up and running with only a credit card; they still need to engage with the company on setting up their applications. Part of the reason for this, Kelly explained, is because of users’ skill levels — data scientists might need to hone their coding skills, while developers might need to learn how to write better data flows.

“We’re walking the bridge over the valley [between a do-it-yourself platform and a wholly managed service],” Kelly said. When it crosses the chasm and allows for immediate credit-card access, he added, it will probably be centered on prepackaged solutions based on what’s popular among users. The first 90 percent will be written for users, and they’ll tune the last 10 percent to their specific needs.

Feature image courtesy of Shutterstock user voyager624.

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In September, I profiled six companies doing big data in the cloud, and here are nine more. They’re not providing cloud-based applications (e.g., anti-malware services or site-optimization) that happen to use big data techniques on the backend; these companies providing access to big data resources or analytics engines from which customers can draw their own conclusions.

Cetas: Cetas (see disclosure) is a stealth-mode startup focused on providing an entire analytics stack in the cloud (or on-premise, if a customer prefers). The driving theory is to let companies running web applications get the types of user analytics that Facebook and Google are able to get, only without the teams of expensive engineers and data scientists, Cetas VP of Products Karthik Kannan told me. While most of that functionality is prepackaged now into core capabilities, Kannan said Cetas plans to let power-users build their own custom models and tie Cetas into existing analytic platforms.

DNAnexus: DNAnexus stores mountains of genomic data in the Amazon Web Services and Google clouds so that researchers, doctors and others interested in DNA have a centralized place to access and analyze that data. With genome sequencing getting cheaper by the year but still producing the same amount of data per genome, we’re facing a possible deluge of DNA data. Whereas the high-end research facilities might have access to high-performance computing and storage necessary to perform DNA sequencing, the hospitals that will now be doing those analyses on a regular basis certainly will not, DNAnexus CEO Andreas Sundquist told me in October.

Google: Google has a multi-pronged strategy on cloud-based big data services, but the two services that stand out most are BigQuery and the Google Prediction API.  Google describes BigQuery, available in limited preview now, as a service that “allows you to run SQL-like queries against very large datasets, with potentially billions of rows. … BigQuery works best for interactive analysis of very large datasets, typically using a small number of very large, append-only tables.” The Google Prediction API is just what it sounds like, a service that puts machine-learning and pattern-detection capabilities in developers’ hands so they can analyze application data for things such as sentiment analysis, system analytics and recommendation engines.

Infochimps: Once a startup focused on its data marketplace, Infochimps has morphed into a provider of big data infrastructure as a service that provides its marketplace data as a value-added feature. Describing the new Infochimps Platform in February, I wrote, “The platform is hosted in the AWS cloud and supports Hadoop, various analytical tools on top of that — including Apache Pig and Infochimps’ own Wukong (a Ruby framework for Hadoop) — and a variety of relational and NoSQL databases.” But the key is the platform’s automated nature, which CEO Joe Kelly hopes “will help answer the question of ‘what does a Heroku for big data look like?’”

Kognitio: Kognitio is an analytic database vendor that offers a wholly cloud-based version of its flagship WX2 database, called Kognitio Cloud. It has attracted customers such as Orbitz with its in-memory technology that, as I explained in October, is popular because it “takes less time to process information stored in a system’s memory than it does information stored on a hard disk. That means companies can get closer to real-time analysis as data streams in, or far faster results when running less-timely queries.”

Medio: Medio offers an end-to-end suite of cloud-based capabilities, under the inGenius platform banner for analyzing user information. It’s inSight product lets developers tune applications to capture certain data from users, but inTeract is the predictive analytics engine. With inTeract, Medio says, “you can compare and evaluate the effectiveness of specific targeted messages, personalized content and tailored offers and quickly calculate the impact each will have on customer ROI, engagement and loyalty.” Its inCent service automates the process of acting upon whatever insights customers have discovered. Under the covers of Medio, of course, is Hadoop.

Metamarkets: Metamarkets (see disclosure) is a cloud-based analytics engine designed to help users understand behavior on their websites. Here’s how I described it in a January post: “The Metamarkets product is a cloud-based big data application previously tuned for helping online media companies analyze the streams of data they generate everyday as customers click their way through the sites. The company uses a specialized version of Hadoop for parallel processing, but it goes much further by adding a custom-built in-memory database for real-time queries and by providing visualization and predictive modeling capabilities.”

Microsoft: Microsoft was late to the Hadoop game, but has been making up for lost time since October. I recently described its progress on the Hadoop on Windows Azure offering: “The company opened a preview of the service to 400 developers in December and on [March 6], … opened it up to 2,000 developers. According to Doug Leland, GM of product management for SQL Server, … Microsoft is trying ‘to provide a service that is very easy to consume for customers of any size,’ which means an intuitive interface and methods for analyzing data. Already, he said, Webtrends and the University of Dundee are among the early testers of Hadoop on Windows Azure, with the latter using it for genome analysis.”

Xignite: Xignite offers market data to financial services clients, all from a centralized store in the AWS cloud. As my colleague Stacey Higginbotham described its API-based service when covering the company’s $10 million investment round in September, “Xignite stores ‘petabytes of data’ on Amazon’s cloud, and the goal is to keep that data up there so folks can analyze it on cloud-based servers and just get the information they need in the format they want when they ask for it.”

Disclosure: Cetas and Metamarkets are portfolio companies of True Ventures, which is also an investor in GigaOM. Om Malik is also a venture partner at True.

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