- October 30, 2012
At Infochimps, we are committed to embracing cutting edge technology, while ensuring that the latest Big Data innovations are enterprise-ready. Today, we are proud to deliver on that promise once again by announcing the integration of Storm and Kafka into the Cloud::Streams component of the Infochimps Cloud.
Cloud::Streams provides solutions for challenges involving:
- Large-scale data collection – clickstream web data, social media and online monitoring, financial market data, machine-to-machine data, sensors, business transactions, listening to or polling application APIs and databases, etc.
- Real-time stream processing – real-time alerting, tagging and filtering, real-time applications, fast analytical processing like fraud detection or sentiment analysis, data cleansing and transformation, real-time queries, distribution to multiple clients, etc.
- Analytics system ETL – providing normalized/de-normalized data using customer-defined business logic for various analytics data stores and file systems including Hadoop HDFS, HBase, Elasticsearch, Cassandra, MongoDB, PostgreSQL, MySQL, etc.
Storm and Kafka
“With Storm and Kafka, you can conduct stream processing at linear scale, assured that every message gets processed in real-time, reliably. In tandem, Storm and Kafka can handle data velocities of tens of thousands of messages every second.”
Ultimately, Storm and Kafka form the best enterprise-grade real-time ETL and streaming analytics solution on the market today. Our goal is to put the same technology that Twitter uses to process over 400 million tweets per day — in your hands. Other companies that have adopted Storm in production include Groupon, Alibaba, The Weather Channel, FullContact, and many others.
Nathan Marz, Storm creator and senior Twitter engineer, comments on Storm’s rapid growth:
“Storm has gained an enormous amount of traction in the past year due to its simplicity, robustness, and high performance. Storm’s tight integration with the queuing and database technologies that companies already use have made it easy to adopt for their stream computing needs.”
Storm solves a broad set of use cases, including “processing messages and updating databases (stream processing), doing a continuous query on data streams and streaming the results into clients (continuous computation), parallelizing an intense query like a search query on the fly (distributed RPC), and more.”
Cloud::Streams is fault-tolerant and linearly scalable, and performs enterprise data collection, transport, and complex in-stream processing. In much the same way that Hadoop provides batch ETL and large-scale batch analytical processing, Cloud::Streams provides real-time ETL and large-scale real-time analytical processing — the perfect complement to Hadoop (or in some cases, what you needed instead of Hadoop).
Cloud::Streams adds important enterprise-class enhancements to Storm and Kafka, including:
- Integration Connectors to your existing tech environment for collecting required data from a huge variety of data sources in a way that is robust yet as non-invasive as possible
- Optimizations for highly scalable, reliable data import and distributed ETL (extract, transform, load), fulfilling data transport needs
- Developer Toolkit for rapid development of decorators, which perform the real-time stream processing
- Guaranteed delivery framework and data failover snapshots to send processed data to analytics systems, databases, file systems, and applications with extreme reliability
- Rapid solution development and deployment, along with our expert Big Data methodology and best practices
Infochimps has extensive experience implementing Cloud::Streams, both for clients and for our internal data flows including large-scale clickstream web data flows, massive Twitter scrapes, the Foursquare firehose, customer purchase data, product pricing data, and more.
Obviously, data failover and optimizations are key to enterprise readiness. Above and beyond that though, Cloud::Streams is a joy to work with because of its flexible Integration Connectors and the Developer Toolkit. No matter where your data is, you can access and ingest it with a variety of input methods. No matter what kind of work you need to perform (parse, transform, augment, split, fork, merge, analyze/process, …) you can quickly develop that processor unit, test it, and deploy it as a Cloud::Streams decorator.
One of our most recent customers was able to build an entire production application flow for large-scale social media data analysis using the Infochimps Cloud development framework in just 30 days with only 3 developers. That is both unheard of from an enterprise timeline perspective, as well as an amazing case of business ROI. Big Data is too important to spend months and months developing. Your business needs results now, and the Infochimps Cloud leverages the talent you have today for fast project success.
How much is it worth to you to launch your own revenue generating applications for your customers? Or for your internal stakeholders as part of a Big Data business intelligence initiative? How much value would launching 12 months sooner provide your organization? These are questions which we’re trying to make the answer to obvious.
“Storm and Kafka are excellent platforms for scalable real-time data processing. We are very pleased that Infochimps has embraced Storm and Kafka for Cloud::Streams. This new offering gives us the opportunity to supplement our listening and analytics products with Infochimps’ data sources, to integrate capabilities seamlessly with our partners who also use Storm, and to retain Infochimps’ unique technical team to support and optimize our data pipelines.”
Lastly, check out our previous product announcements! In February, we launched the Infochimps Platform. In April we launched Dashpot as well as our support of OpenStack. In August, we announced the Platform’s newest release.