Real World Internet of Things at Scale

Jim Kaskade

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Real-time Big Data or Small Data?

I’ve been asked what I consider as “Big Data” versus “Small Data” in this domain. Here’s my view.

Have you heard of products like IBM’s InfoSphere Streams, Tibco’s Event Processing product, or Oracle’s CEP product? All good examples of commercially available stream processing technologies which help you process events in real-time.

I’ve been asked what I consider as “Big Data” versus “Small Data” in this domain. Here’s my view.

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Real-Time Analytics Small Data Big Data
Data Volume None None
Data Velocity 100K events / day (<<1K events / second) Billion+ events / day (>>1K events / second)
Data Variety 1-6 unstructured on sources AND 1 single destination (an output file, a SQL database, a BI tool) 6+ structured and 6+ unstructured for sources AND many destinations (a custom application, a BI tool, several SQL databases, NoSQL databases, Hadoop)
Data Models Used for “transport” mainly. Little to no ETL, in-stream analytics, or complex event processing performed. Transport is the foundation. However, distributed ETL, linearly scalable in-memory and in-stream analytics are applied, and complex event processing is the norm.
Business Functions One line of business (e.g. financial trading) Several lines of business – to – 360 view
Business Intelligence No queries are performed against the data in motion. This is simply a mechanism for transporting transaction or event from the source to a database.Transport times are <1 second.

 

 

Example: connect to desktop trading applications and transport trade events to an Oracle database.

ETL, sophisticated algorithms, complex business logic, and even queries can be applied to the stream of events as they are in motion.  Analytics span across all data sources and, thus, all business functions.Transport and analytics occur in < 1 second.

 

Example: connect to desktop trading applications, market data feeds, social media, and provide instantaneous trending reports. Allow traders to subscribe to information pertinent to their trades and have analytics applied in real-time for personalized reporting.

Want to see my view of Batch Analytics? Go Here.

Want to see my view of Ad Hoc Analytics? Go Here.

Here are a few other products in this space:

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Jim Kaskade currently leads Janrain, the category creator of Consumer Identity & Access Management (CIAM). We believe that your identity is the most important thing you own, and that your identity should not only be easy to use, but it should be safe to use when accessing your digital world. Janrain is an Identity Cloud servicing Global 3000 enterprises providing a consistent, seamless, and safe experience for end-users when they access their digital applications (web, mobile, or IoT).

Prior to Janrain, Jim was the VP & GM of Digital Applications at CSC. This line of business was over $1B in commercial revenue, including both consulting and delivery organizations and is focused on serving Fortune 1000 companies in the United States, Canada, Mexico, Peru, Chile, Argentina, and Brazil. Prior to this, Jim was the VP & GM of Big Data & Analytics at CSC. In his role, he led the fastest growing business at CSC, overseeing the development and implementation of innovative offerings that help clients convert data into revenue. Jim was also the CEO of Infochimps; Entrepreneur-in-Residence at PARC, a Xerox company; SVP, General Manager and Chief of Cloud at SIOS Technology; CEO at StackIQ; CEO of Eyespot; CEO of Integral Semi; and CEO of INCEP Technologies. Jim started his career at Teradata where he spent ten years in enterprise data warehousing, analytical applications, and business intelligence services designed to maximize the intrinsic value of data, servicing fortune 1000 companies in telecom, retail, and financial markets.