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- About Us
Leverage Big Data
And put it to work for your organization. Leverage Social Media, Historical, and Real-time to see the Emerging Trends.
Benefits Who is using or talking about your product.Hundreds of Millions of consumers are online everyday. Are they talking about your company?
Seasonality, multi-year trends, health trends, sales or spending trends become recognizable and useful.
Take the guess work out .. what-if that works.
Collection Social Media, eCommerce, the blogshere ?We reduce a trillion terrabytes of raw data into meaningful information that your teams can leverage.
Manage your Marketing $ Where are your markets emerging?Where would spending make most sense - on search engine ads, specific social media sites, where are your customers?
FP500 Organizations manage Petabytes of transactions, archives, product literature & databases.
Add context and see the value in your data
Cyber Security What does your Security Framework see?Threat Detection & Remediation, Reporting and Remediation, Compliance & Corporate Governance.
Do you see the trends?
Proactive, pre-emptive or predictive decision support are all deliverables of a well thoughout Big Data Framework solution for your company. Marketing, Product Development, Sales and Service Delivery will all need to understand what are your clients saying - in their data.
- What are your customers saying - where are they shopping.
- Does a sale guarantee a followup purchase?
- We workshop dozens of Big Data trends and measures.
- Our security monitoring and reporting center maintain the highest service levels
Big Data, BI Architect, Data Scientist I
Business Analyst and Project Coordinator extraordinaire.
Once we received our first standing ovation from Hewlett-Packard’s executive team, we were hooked. DashFlows’ Dashboard gave context to a previously mundane Service Delivery report that their $1.5 billion annual customer, CIBC Mellon Bank, could not do without. Now there were drill-downs, impact and exception explanations. We were WoWed !!
Business Process & Program Management
Big Data Tools
Big Data is on every CIO’s mind this quarter, and for good reason. Companies will have spent $4.3 billion on Big Data technologies by the end of 2012. But here’s where it gets interesting. Those initial investments will in turn trigger a domino effect of upgrades and new initiatives that are valued at $34 billion for 2013, per Gartner. Over a 5 year period, spend is estimated at $232 billion. What you’re seeing right now is only the tip of a gigantic iceberg. Big Data is presently synonymous with technologies like Hadoop, and the “NoSQL” class of databases including Mongo (document stores) and Cassandra (key-values). Today it’s possible to stream real-time analytics with ease. Spinning clusters up and down is a (relative) cinch, accomplished in 20 minutes or less. We have table stakes. But there are new, untapped advantages and non-trivially large opportunities beyond these usual suspects.
Storm and Kafka Storm and Kafka are the future of stream processing, and they are already in use at a number of high-profile companies including Groupon, Alibaba, and The Weather Channel. Born inside of Twitter, Storm is a “distributed real-time computation system”. Storm does for real-time processing what Hadoop did for batch processing. Kafka for its part is a messaging system developed at LinkedIn to serve as the foundation for their activity stream and the data processing pipeline behind it. When paired together, you get the stream, you get it in-real time, and you get it at linear scale.
Drill and Dremel
Why should you care? Drill and Dremel compare favorably to Hadoop for anything ad-hoc. Hadoop is all about batch processing workflows, which creates certain disadvantages. The Hadoop ecosystem worked very hard to make MapReduce an approachable tool for ad hoc analyses. From Sawzall to Pig and Hive, many interface layers have been built on top of Hadoop to make it more friendly, and business-accessible. Yet, for all of the SQL-like familiarity, these abstraction layers ignore one fundamental reality – MapReduce (and thereby Hadoop) is purpose-built for organized data processing (read: running jobs, or “workflows”). What if you’re not worried about running jobs? What if you’re more concerned with asking questions and getting answers — slicing and dicing, looking for insights?
That’s “ad hoc exploration” in a nutshell — if you assume data that’s been processed already, how can you optimize for speed? You shouldn’t have to run a new job and wait, sometimes for considerable lengths of time, every time you want to ask a new question. In stark contrast to workflow-based methodology, most business-driven BI and analytics queries are fundamentally ad hoc, interactive, low-latency analyses. Writing Map Reduce workflows is prohibitive for many business analysts. Waiting minutes for jobs to start and hours for workflows to complete is not conducive to an interactive experience of data, the comparing and contrasting, and the zooming in and out that ultimately creates fundamentally new insights. Some data scientists even speculate that Drill and Dremel may actually be better than Hadoop in the wider sense, and a potential replacement, even. That’s a little too edgy a stance to embrace right now, but there is merit in an approach to analytics that is more query-oriented and low latency. At Infochimps we like the Elasticsearch full-text search engine and database for doing high-level data exploration, but for truly capable Big Data querying at the (relative) seat level, we think that Drill will become the de facto solution.
R & SAP Hana
R is an open source statistical programming language. It is incredibly powerful. Over two million (and counting) analysts use R. It’s been around since 1997 if you can believe it. It is a modern version of the S language for statistical computing that originally came out of the Bell Labs. Today, R is quickly becoming the new standard for statistics. R performs complex data science at a much smaller price (both literally and figuratively). R is making serious headway in ousting SAS and SPSS from their thrones, and has become the tool of choice for the world’s best statisticians (and data scientists, and analysts too).
SAP Hana is an in-memory analytics platform that includes an in-memory database and a suite of tools and software for creating analytical processes and moving data in and out, in the right formats. SAP is going against the grain of most entrenched enterprise mega-players by providing a very powerful product, free for development use. And it’s not only that — SAP is also creating meaningful incentives for startups to embrace Hana as well. They are authentically fostering community involvement and there is uniformly positive sentiment around Hana as a result. Hana highly benefits any applications with unusually fast processing needs, such as financial modeling and decision support, website personalization, and fraud detection, among many other use cases. The biggest drawback of Hana is that “in-memory” means that it by definition leverages access to solid state memory, which has clear advantages, but is much more expensive than conventional disk storage. For organizations that don’t mind the added operational cost, Hana means incredible speed for very-low latency big data processing..
Learn more about SAP Hana
SAP HANA has completely transformed the database industry by combining database, data processing, and application platform capabilities in a single in-memory platform. The platform also provides libraries for predictive, planning, text processing, spatial, and business analytics -- all on the same architecture.
This makes it possible for applications and analytics to be rethought without information processing latency, and sense-and-response solutions can work on massive quantities of real-time data for immediate answers without building pre-aggregates. Simply put -- this makes SAP HANA the platform for building and deploying next-generation, real-time applications and analytics.