Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
    pexels pavel danilyuk 8112119
    Data Analytics Is Revolutionizing Medical Credentialing
    8 Min Read
    data and seo
    Maximize SEO Success with Powerful Data Analytics Insights
    8 Min Read
    data analytics for trademark registration
    Optimizing Trademark Registration with Data Analytics
    6 Min Read
    data analytics for finding zip codes
    Unlocking Zip Code Insights with Data Analytics
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Spotlight on SiSense: BI Without the Bandwidth
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > IT > Cloud Computing > Spotlight on SiSense: BI Without the Bandwidth
AnalyticsBusiness IntelligenceCloud ComputingData MiningData QualityData VisualizationData WarehousingDecision ManagementExclusiveHadoopMapReduceMarket ResearchOpen SourceSocial DataSQLUnstructured Data

Spotlight on SiSense: BI Without the Bandwidth

Shawn Gordon
Shawn Gordon
6 Min Read
Image
SHARE

I was at DataWeek/API World in mid-September 2014 (last week at the time of this writing) and saw some interesting things, almost entirely around Big Data. The two items that stood out for me, were the Graph DataBase system Neo4j (which I wish I had time and a reason to dig into more), and SiSense, who absolutely blew my mind.

I was at DataWeek/API World in mid-September 2014 (last week at the time of this writing) and saw some interesting things, almost entirely around Big Data. The two items that stood out for me, were the Graph DataBase system Neo4j (which I wish I had time and a reason to dig into more), and SiSense, who absolutely blew my mind.

Ever since I first heard of Hadoop and researched it, it seemed like a very poor solution. Way too much work, detached data, not real time, reliant on IT to put their queries together, etc.. SiSense saw this issue as well, but they addressed it in a totally different, two pronged approach. This isn’t a product review, but rather an overview of the technology and possibilities. SiSense is a provider of Business Intelligence (BI) technology, that includes a back-end powered by “in-chip” technology that easily enables non-techies to access and analyze large data sets from multiple sources, and a front-end for creating dashboards and reports that will display on any device, including mobile. I’m going to focus on the former.

Image

More Read

Improvement Project for Services; Remember You’re Never Really Done
SAS commits $70 million to Cloud Computing
Aiding Architecture & Engineering Firms with Data-Driven Learning
Taking the question out of questionable claims
6 Tips to Improve the Accuracy and Efficiency of Sales Planning

BI applications typically process and extraordinarily large amount of data to provide useful feedback in an easily digestible fashion. Because of that, they tend to be fairly slow as there is a constant stream of data from disk to memory to the CPU. Many vendors process as much in RAM as possible to speed things up, but this requires lots and lots of RAM. To make more efficient use of the RAM, pretty much all the BI vendors are using columnar databases as the staging area, this is about as efficient as you can get with your result set as you drop the data you don’t need.

This is where SiSense diverges from the crowd and how they can declare they can process 100 times the data at 10 times the speed of the competition. SiSense leaves that data on the disk instead of trying to jam it into RAM, then compresses the heck out of it.

Now here is where their secret sriracha sauce comes in: They do the decompression in the CPU cache.

As you can see from our graphic, even the L3 cache of the CPU is orders of magnitude faster than even doing it in RAM, and because the data is all highly compressed, it is moving off disk and through RAM at a significantly faster pace than could normally be achieved.

The application doing all this work is Prism, and it doesn’t stop there. Prism is holding a memory map of the current location of all data. When it process data or does any type of calculation, it is applying vector algebra to the data, thus enabling Prism to take advantage of the x86 in-chip Single Instruction Multiple Data (SIMD) vector instructions. This allows short arrays of data to be processed by a single instruction. As a result, the CPU cores are able to process data much faster and in parallel.

ImagePrism is designed to keep the CPU as active as possible. It is the fastest piece of hardware in your box and is often under utilized, but Prism changes all that. The learning algorithm that is built in to Prism also means that over time it will even get faster and it more intelligently optimizes and pre-fetches data. This also means, that counter intuitively, it can get faster with more people using it because pre-loading optimizations improve as more queries are performed.

ImageBack to my opening statement about Hadoop. SiSense and Prism will work with Hadoop and mapreduce data. However, the whole distributed and difficult nature of Hadoop evolved around having to manage insanely large amounts of data and no realistic way to do it in real time. With SiSense, you are able to crunch through terrabytes of data with a large number of concurrent users, on a single commodity server without having a team of IT guys constantly managing it.

CPUs just get more and more powerful, with more cores and presumably, more cache. The technology that SiSense has created is about as durable as it gets in terms of future proofing. Nearly three quintillion bytes of data are created every day; 80 percent of it is unstructured, and only 20 percent of it is available to be processed.

 

Image

  

The need for tools to effectively dig through all that data and present useful results is clear, and there are many vendors providing them, but SiSense, in my opinion, is genuinely addressing it the way it needs to be addressed.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ESG reporting software
Data Shows How ESG Reporting Software Helps Companies Achieve Sustainability Goals
Big Data Infographic
ai in marketing
AI Helps Businesses Develop Better Marketing Strategies
Artificial Intelligence Exclusive
agenic ai
How Businesses Are Using AI to Make Smarter, Faster Decisions
Artificial Intelligence Exclusive
accountant using ai
AI Improves Integrity in Corporate Accounting
Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

In-database analytics – a white paper

3 Min Read
smart manufacturing
Predictive Analytics

Big Data and the Evolution of Supply Chain Planning

5 Min Read

Tips for Starting Your Dashboard Layout

7 Min Read

Staring at the Lights: Your Data Warehouse Isn’t a Commodity

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?