5 Important Factors for Pricing Data in the Information (Overload) Age. This guest post comes from Nick Ducoff, Co-founder and CEO of Infochimps, an Austin-based marketplace to find, share and build on data. He blogs at nick.vc. Do you remember what it was like to figure out what was going on in your city fifteen years ago? You probably called your friends. Less than 20% of U.S. households were online. People learned about local news and events from newspapers, TV and their friends. Fast-forward to today, and this is obviously no longer the case. Just because, we, the consumers, aren’t paying for that information, doesn’t mean nobody is. There are a lot of companies like Groupon that generate a lot of data, but whose primary business is selling something else.
At Infochimps, we are building a platform to help companies in the business of selling data, and also those that aren’t, to monetize their data assets. 1. Some customers need data in real time, and are willing to pay a premium for it. Lesson: Think about how scarce your data is. 2. 3. 4. 5. Dirty data, stored dirt cheap. June 4, 2011 A major driver of Hadoop adoption is the “big bit bucket” use case. Users take a whole lot of data, often machine-generated data in logs of different kinds, and dump it into one place, managed by Hadoop, at open-source pricing. Hadoop hardware doesn’t need to be that costly either.
And once you get that data into Hadoop, there are a whole lot of things you can do with it. Of course, there are various outfits who’d like to sell you not-so-cheap bit buckets. Contending technologies include Hadoop appliances (which I don’t believe in), Splunk (which in many use cases I do), and MarkLogic (ditto, but often the cases are different from Splunk’s). Cloudera and IBM, among other vendors, would also like to sell you some proprietary software to go with your standard Apache Hadoop code. So the question arises — why would you want to spend serious money to look after your low-value data? Comments. Hardware for Hadoop. June 4, 2011 After suggesting that there’s little point to Hadoop appliances, it occurred to me to look into what kinds of hardware actually are used with Hadoop. So far as I can tell: Hadoop nodes today tend to run on fairly standard boxes.Hadoop nodes in the past have tended to run on boxes that were light with respect to RAM.The number of spindles per core on Hadoop node boxes is going up even as disks get bigger.
A key input comes from Cloudera, who to my joy delegated the questions to Omer Trajman, who wrote: Most Hadoop deployments today use systems with dual socket and quad or hex cores (8 or 12 cores total, 16 or 24 hyper-threaded). Storage has increased as well with 6-8 spindles being common and some deployments going to 12 spindles. These are SATA disks with between 1TB and 2TB capacity. Bullet points from that year-ago link include: So basically we’re talking in the range of 2-3 GB of RAM per core — and 1 spindle per core, up from perhaps half a spindle per core a year ago. Stack Your Logs In One Place With Loggly. If you’re writing a serious application, on the web or otherwise, you’ve got to have good logging.
In the context of application development, reading logs is my personal favorite method for tracking bugs and rooting them out. A service like Loggly is perfect for programmers like me. It provides an easy to use web interface with searching capabilities that allows you to see what your application is logging at any time that you’d like to check in. Debugging with Loggly is nice, but it’s just the tip of the iceberg, thanks in part to its Loggly API. Imagine that you’re a web programmer implementing your own “passion project.” You start with one server and one application instance, but as the internet starts to show some traffic-love to your idea expansion becomes a pressing need. I recently spoke with Kord Campbell, Loggly’s CEO and founder about his vision for the company. The Loggly RESTful API serves up JSON responses and offers two modes: administration and dealing with logs.
CloudTalk: Voice instant messaging that works | Rafe's Radar. The most audacious start-up pitches are those that propose changing people's communications habits. No matter how clever a company's technology or gorgeous an app's interface, getting users to adopt new modalities of communication is perhaps the hardest job in tech. It's a social challenge as much as a technological one, which means that if you get it right, your technology spreads from person to person--virally, as the overused term calls it. Lately, social start-ups have been adopting strange, mutated viral models: Path is a social app that launched with a bizarrely limited way to join networks. Color opens you up to pop-up social networks based on physical proximity. A new app, CloudTalk (previously Pana.ma) is a person-to-person voice and video messaging app. As CloudTalk founder David Hayden (formerly of Magellan and Critical Path) says, as we discuss the younger generation's growing reliance on text messaging, "It's not that kids don't like to talk.
What is BPM | BPMBasics Learning Center | Appian BPM. Introduction to SMPP. Introduction SMPP stands for Short Message Peer to Peer Protocol SMPP is used to send and receive messages from and to GSM, UMTS, iDEN,CDMA and TDMA cell phones. The protocol is a level-7 TCP/IP protocol, which allows fast deliver of SMS messages. Using the SMPP protocol instead of sending messages using a GSM modem has the following advantages: The SMPP protocol is TCP/IP based, GSM hardware is not required; Users can send SMS to a simple shortcode, this is not possible when sending to a GSM phone; High throughput ( up to 200 msgs/second); Alphanumeric sender address can be assigned. Applications SMPP can be used for the following applications: Connections SMPP is used by clients to connected to a SMSC (Short Message Service Centre).
Messages Send to a SMSC are called MT (Mobile Terminated) messages, because they are sent to a mobile phone. SMPP PDU's The TCP packets between the ESME and the SMSC are called PDU's (Protocol Data Units). Session Management PDUs Message Submission PDUs submit_sm. eXtremeDB. History[edit] Later editions targeted the high performance non-embedded software market, including capital markets applications (algorithmic trading, order matching engines) and real-time caching for Web-based applications, including social networks and e-commerce. Features added to support this focus include a SQL ODBC and JDBC interfaces, 64-bit support, and multiversion concurrency control (MVCC) transaction management.[4] Product features[edit] Core eXtremeDB engine[edit] eXtremeDB supports the following features across its product family.[5] In-process architecture[edit] eXtremeDB runs in-process with an application, rather than as a database server that is separate from client processes.
Application programming interfaces[edit] Database indexes[edit] Concurrency mechanisms[edit] Supported data types[edit] Security[edit] Optional features[edit] Distributed database management abilities[edit] Hybrid storage[edit] Transaction logging[edit] SQL ODBC/JDBC[edit] Kernel mode deployment[edit] Pricing/licensing info? | VoltDB.