Since we approaching (in USA that is) a Thanksgiving Day for 2013 and shopping is not a sin for few days, multiple blog visitors asked me what hardware advise I can share for their Data Science and Visualization Lab(s). First of all I wish you will get a good Turkey for Thanksgiving (below is what I got last year):
I cannot answer DV Lab questions individually – everybody has own needs, specifics and budget, but I can share my shopping thoughts about needs for Data Visualization Lab (DV Lab). I think DV Lab needs many different types of devices: smartphones, tablets, projector (at least 1), may be a couple of Large Touchscreen Monitors (or LED TVs connectable to PCs), multiple mobile workstations (depends on size of DV Lab team), at least one or two super-workstation/server(S) residing within DV Lab etc.
Smartphones and Tablets
I use Samsung Galaxy S4 as of now, but for DV Lab needs I will consider either Sony Xperia Z Ultra or Nokia 1520 with hope that Samsung Galaxy S5 will be released soon (and may be it will be the most appropriate for DV Lab):
My preference for Tablet will be upcoming Google Nexus 10 (2013 or 2014 edition – it is not clear, because Google is very secritive about it) and in certain cases Google Nexus 7 (2013 edition). Until Nexus 10 ( next generation) will be released, I guess that two leading choices will be ASUS Transformer Pad TF701T
and Samsung Galaxy Note 10.1 2014 edition (below is a relative comparison of the size of these 2 excellent tablets):
Projectors, Monitors and may be Cameras.
Next piece of hardware in my mind is a projector with support for full HD resolution and large screens. I think there are many good choices here, but my preference will be BENQ W1080ST for $920 (please advise if you have a better projector in mind in the same price range):
So far you cannot find too many Touchscreen Monitors for reasonable price, so may be these two 27″ touchscreen monitors (DELL P2714T for $620 or Acer T272HL bmidz for $560) are good choices for now:
I also think that a good digital camera can help to Data Visualization Lab and considering something like this (can be bought for $300): Panasonic Lumix DMC FZ72 with 60X optical zoom and ability to do a Motion Picture Recording as HD Video in 1,920 x 1,080 pixels – for myself:
Mobile and Stationary Workstations and Servers.
If you need to choose CPU, I suggest to start with Intel’s Processor Feature Filter here: http://ark.intel.com/search/advanced . In terms of mobile workstations you can get quad-core notebook (like Dell 4700 for $2400 or Dell Precison 4800 or HP ZBook 15 for $3500) with 32 GB RAM and decent configuration with multiple ports, see sample here:
If you are OK with 16GB of RAM for your workstation, you may prefer Dell M3800 with excellent touchscreen monitor (3200×1800 resolution) and only 2 kg of weight. For a stationary workstation (or rather server) good choices are Dell Precision T7600 or T7610 or HP Z820 workstation. Either of these workstations (it will cost you!) can support up to 256GB RAM, up to 16 or even 24 cores in case of HP Z820), multiple high-capacity hard disks and SSD, excellent Video Controllers and multiple monitors (4 or even 6!) Here is an example of backplane for HP Z820 workstation:
I wish to visitors of this blog a Happy Holidays and good luck with their DV Lab Shopping!
I started recently the new Data Visualization Google+ page as the extension of this blog here:
Internet has a lot of articles, pages, blogs, data, demos, vendors, sites, dashboards, charts, tools and other materials related to Data Visualization and this Google+ page will try to point to most relevant items and sometimes to comment on most interesting of them.
What was unexpected is a fast success of this Google+ page – in a very short time it got many followers and that number keeps growing!
Some of visitors to this blog after reading of my recent post about $300K/employee/year as a KPI (Key Performance Indicator) suggested to me another Indicator of the health of Data Visualization vendors: a number of job openings and specifically a number and percentage of software development openings (I include software testers and software managers into this category) and use it also as a predictor of the future. Fortunately it is a public data and below is what I got today from respective websites:
56(!) positions at Tableau, 14 them of are developers;
46 openings at Qliktech, 4 of them are developers;
21 positions at Spotfire, 3 of them are developers;
3 positions at Visokio, 2 of them are developers.
Considering that Tableau is 4 times less in terms of sales then Qlikview and 3-4 times less (then Qliktech) in terms of workforce, this is an amazing indicator. If Tableau can sustain this speed of growth, we can witness soon the change of Data Visualization landscape, unless Qliktech can find the way to defend its dominant position (50% of DV market).
For comparison, you can use Microstrategy’s number of openings. While Microstrategy is not a Data Visualization vendor, it is close enough (as BI vendor) for benchmarking purposes: it has 281 openings, 38 of them are developers and current Microstrategy’s workforce is about 3069, basically 3 times more then Qliktech’s workforce…
In light of recent releases of Qlikview 11 and Spotfire 4.0 it makes (soon to be released) Tableau 7.0 is very interesting to compare… Stay tuned!
7 months ago I published a poll on LinkedIn and got a lot of responses, 1340 votes (in average 1 vote per hour) and comments. People asked me many times to repeat this poll from time to time. I guess it is time to re-Poll. I added 2 more choices (LinkedIn allows maximum 5 choices in their polls and it is clear not enough for this poll), based on a feedback I got: Omniscope and Visual Insight/Microstrategy. I also got some angry voters complaining that certain vendors are funding this poll. This is completely FALSE, I am unaffiliated with any of vendors, mentioned in this poll and I am working for completely independent (from those vendors) software company, see the About page of this Blog.
Comparison of DV Tools is the most popular page (and post) of this site, visited by many thousands of people. Some of them keep asking to append this comparison with different additional features, one of them is a comparison of requirements of leading DV tools for file and memory footprint and also for reading and saving time.
I took mid-sized dataset (428999 rows and 135 columns), exported it into CSV and compressed it to ZIP format, because all native DV formats (QVW by Qlikview, DXP by Spotfire, TWBX by Tableau and XLSX by Excel and PowerPivot) are compressed one way or another. My starting filesize (of ZIPped dataset) was 56 MB. Here is what I got, see for yourself:
One comment is that numbers above are all relative to configuration of hardware used for tests and also depend on other software I ran during tests, because that software also requires RAM, CPU cycles, disk I/O and even on speed of repainting applications windows on screen, especially for Excel. I probably will add more comments to this post/page, but my first impression from this comparison is that new Tableau’s Data Engine (released in version 6.0 and soon will be updated in 6.1) made Tableau more competitive. Please keep in mind, that comparison of in-memory footprint was much less significant in above test, because Qlikview, Excel and PowerPivot putting all dataset into RAM, while Tableau and Spotfire can leave some (unneeded for visualization) data on disk, treating it as “virtual memory”. Also Tableau using 2 executables (not just one EXE as others): tableau.exe (or tabreader.exe) and tdserver64.exe
Since Tableau is the only DV Leading software, capable to read from SSAS Cubes and from PowerPivot (local SSAS) Cubes, I also took large SSAS Cube and for testing purposes I selected SSAS Sub-Cube with 3 Dimensions, 2 Measures and 156439 “rows”, measured the Time and Footprint, needed for Tableau to read Sub-Cube, Refresh it in Memory, Save to local application file, and also measurted “Cubical” Footprint of it in Memory and on Disk and then compared all results with the same tests while running Excel 2010 alone and Excel 2010 with PowerPivot:
While Tableau’s ability to read and visualize Cubes is cool, performance-wise Tableau is far behind of Excel and PowerPivot, especially in Reading department and memory footprint. In Saving department and File footprint Tableau is doing nothing because it is not saving cube locally in its local application TWBX file (and it keeps data in SSAS cube outside of Tableau) so Tableau’s file footprint for SSAS Cubes is not an indicator but for PowerPivot-based local Cubes Tableau does better job (saving data into local application file) then both Excel and PowerPivot!
For many years, Gartner keeps annoying me every January by publishing so called “Magic Quadrant for Business Intelligence Platforms” (MQ4BI for short) and most vendors (mentioned in it; this is funny, even Donald Farmer quotes MQ4BI) almost immediately re-published it either on so-called reprint (e.g. here – for a few months) area of Gartner website or on own website; some of them also making this “report” available to web visitors in exchange for contact info – for free. To channel my feeling toward Gartner to a something constructive, I decided to produce my own “Quadrant” for Data Visualization Platforms (DV “Quadrant” or Q4DV for short) – it is below and is a work in-progress and will be modified and republished overtime:
3 DV Leaders (green dots in upper right corner of Q4DV above) compared with each other and with Microsoft BI stack on this blog, as well as voted in DV Poll on LinkedIn. MQ4BI report actually contains a lot of useful info and it deserved to be used as a one of possible data sources for my new post, which has more specific target – Data Visualization Platforms. As I said above, I will call it Quadrant too: Q4DV. But before I will do that, I have to comment on Gartner’s annual MQ4BI.
MQ4BI customer survey included vendor-provided references, as well as survey responses from BI users in Gartner’s BI summit and inquiry lists. There were 1,225 survey responses (funny enough, almost the same number of responces as on my DV Poll on LinkedIn), with 247 (20%) from non-vendor-supplied reference lists. Magic Quadrant Customer Survey’s results the Gartner promised to publish in 1Q11. The Gartner has a somewhat reasonable “Inclusion and Exclusion Criteria” (for Data Visualization Q4DV I excluded some vendors from Gartner List and included a few too), almost tolerable but a fuzzy BI Market Definition (based on 13 loosely pre-defined capabilities organized into 3 categories of functionality: integration, information delivery and analysis).
I also partially agree with the definition and the usage of “Ability to Execute” as one (Y axis) of 2 dimensions for bubble Chart above (called the same way as entire report “Magic Quadrant for Business Intelligence Platforms”). However I disagree with Gartner’s order of vendors in their ability to execute and for DV purposes I had to completely change order of DV Vendors on X axis (“Completeness of Vision”).
For Q4DV purposes I am reusing Gartner’s MQ as a template, I also excluded almost all vendors, classified by Gartner as niche players with lower ability to execute (bottom-left quarter of MQ4BI), except Panorama Software (Gartner put Panorama to a last place, which is unfair) and will add the following vendors: Panopticon, Visokio, Pagos and may be some others after further testing.
I am going to update this DV “Quadrant”, using the method suggested by Jon Peltier: http://peltiertech.com/WordPress/excel-chart-with-colored-quadrant-background/ – Thank you Jon! I hope I will have time before end of 2011 for it…