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  • Excel Conditional Formatting Multiple Data Bars and Data Icons in one cell

    - by wbeard52
    I am using Excel 2007 on a windows machine. I am attempting to place one data bar and one data icon into a cell under the conditional formatting. The issue is that I don't really want to have data icons or data bars for cells that have dates in the future and I only want to have data icons for dates in the at least one month in the past. This is what I have: This is what I want: I am using the EOMONTH function to determine the last day of the month for the conditional formatting calculations. For the data bar the formula is =EOMONTH(Now(), 4) and =EOMONTH(Now(), -1). The data icons formulas are =EOMONTH(Now(), -1) and =EOMONTH(Now(), -2) Is there a way in Excel 2007 to get rid of the data icons for all the dates in the future and lose the data bars when the date has past. Thanks

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  • Import data in Excel that doesn't have a row delimiter, but number of columns is known

    - by Alex B
    So i have this text file that looks something like this: Header1 Header2 Header3 Header4 A1 B1 C1 D1 A2 B2 C2 D2 and so on. When imported, I'd want the data to format itself in 4 columns. I tried the Get External Data from Text, and it successfully imports it, but it doesn't wrap it around, so it just keeps making columns for every space. I'd want it to go on the next line after 4 (in this case) elements have been added. What's the simplest way to achieve this? EDIT: My answer follows, since I'm not yet allowed to answer my own questions yet. The Excel function I needed is called indirect(). Not sure how it actually works though, so hopefully someone can help out with that, but the function call that worked for me is =INDIRECT(ADDRESS((ROW(A1)-1)*4+COLUMN(A1),1)) which i found over here: http://www.ozgrid.com/forum/showthread.php?t=101584&p=456031#post456031 Note: this required me to add the text to excel where i'd get this row full of columns, and then flip it so that i'd have a column full of rows.

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  • Big Data – Beginning Big Data – Day 1 of 21

    - by Pinal Dave
    What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Reading data from an Entity Framework data model through a WCF Data Service

    - by nikolaosk
    This is going to be the fourth post of a series of posts regarding ASP.Net and the Entity Framework and how we can use Entity Framework to access our datastore. You can find the first one here , the second one here and the third one here . I have a post regarding ASP.Net and EntityDataSource. You can read it here .I have 3 more posts on Profiling Entity Framework applications. You can have a look at them here , here and here . Microsoft with .Net 3.0 Framework, introduced WCF. WCF is Microsoft's...(read more)

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  • Big Data&rsquo;s Killer App&hellip;

    - by jean-pierre.dijcks
    Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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  • Accelerate your SOA with Data Integration - Live Webinar Tuesday!

    - by dain.hansen
    Need to put wind in your SOA sails? Organizations are turning more and more to Real-time data integration to complement their Service Oriented Architecture. The benefit? Lowering costs through consolidating legacy systems, reducing risk of bad data polluting their applications, and shortening the time to deliver new service offerings. Join us on Tuesday April 13th, 11AM PST for our live webinar on the value of combining SOA and Data Integration together. In this webcast you'll learn how to innovate across your applications swiftly and at a lower cost using Oracle Data Integration technologies: Oracle Data Integrator Enterprise Edition, Oracle GoldenGate, and Oracle Data Quality. You'll also hear: Best practices for building re-usable data services that are high performing and scalable across the enterprise How real-time data integration can maximize SOA returns while providing continuous availability for your mission critical applications Architectural approaches to speed service implementation and delivery times, with pre-integrations to CRM, ERP, BI, and other packaged applications Register now for this live webinar!

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  • Excel - working in a bank

    - by Einsteins Grandson
    I am supposed to go to an interview to a bank for just supporting managers in projects. It's a part-time job and the thing is that bank uses Excel for everything. Modifications of tables of really lot of data... What can I expect to find in the test of Excel? I have some books that are around 1000 pages thick but I don't have time and also don't feel like reading everything that's in them. These are the books that I have: http://www.amazon.com/Excel-2010-Bible-John-Walkenbach/dp/0470474874/ref=sr_1_1?ie=UTF8&qid=1347571864&sr=8-1&keywords=excel+bible http://www.amazon.com/Excel-2010-The-Missing-Manual/dp/1449382355/ref=sr_1_1?ie=UTF8&qid=1347571884&sr=8-1&keywords=Excel+2010+The+Missing+Manual http://www.amazon.com/Microsoft-Excel-2010-In-Depth/dp/0789743086/ref=sr_1_1?ie=UTF8&qid=1347571904&sr=8-1&keywords=Microsoft+Excel+2010+In+Depth So, anybody knows a good online tutorial or a book that would contain the basics and was not that much thick? ;-) Thanks so much!!!

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  • Excel Macro Help - Data Input

    - by B-Ballerl
    I'm want to develop a macro where in my excel worksheet I type a date in a specific cell, and the macro will go into a folder containing text files. A database you could say. I want it to find the corresponding file name which is written as a date, put the data through a delimeter, and paste into the cells directly below where I orginally put the date. I'm very new with Macro's so if you must answer try to be a little more simple than you might usually be. Thanks In Advance if anyone can Help!!

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  • Real Excel Templates I

    - by Tim Dexter
    As promised, I'm starting to document the new Excel templates that I teased you all with a few weeks back. Leslie is buried in 11g documentation and will not get to officially documenting the templates for a while. I'll do my best to be professional and not ramble on about this and that, although the weather here has finally turned and its 'scorchio' here in Colorado today. Maybe our stand of Aspen will finally come into leaf ... but I digress. Preamble These templates are not actually that new, I helped in a small way to develop them a few years back with Excel 'meistress' Shirley for a company that was trying to use the Report Manager(RR) Excel FSG outputs under EBS 12. The functionality they needed was just not there in the RR FSG templates, the templates are actually XSL that is created from the the RR Excel template builder and fed to BIP for processing. Think of Excel from our RTF templates and you'll be there ie not really Excel but HTML masquerading as Excel. Although still under controlled release in EBS they have now made their way to the standlone release and are willing to share their Excel goodness. You get everything you have with hte Excel Analyzer Excel templates plus so much more. Therein lies a question, what will happen to the Analyzer templates? My understanding is that both will come together into a single Excel template format some time in the post-11g release world. The new XLSX format for Exce 2007/10 is also in the mix too so watch this space. What more do these templates offer? Well, you can structure data in the Excel output. Similar to RTF templates you can create sheets of data that have master-detail n relationships. Although the analyzer templates can do this, you have to get into macros whereas BIP will do this all for you. You can also use native XSL functions in your data to manipulate it prior to rendering. BP functions are not currently supported. The most impressive, for me at least, is the sheet 'bursting'. You can split your hierarchical data across multiple sheets and dynamically name those sheets. Finally, you of course, still get all the native Excel functionality. Pre-reqs You must be on 10.1.3.4.1 plus the latest rollup patch, 9546699. You can patch upa BIP instance running with OBIEE, no problem You need Excel 2000 or above to build the templates Some patience - there is no Excel template builder for these new templates. So its all going to have to be done by hand. Its not that tough but can get a little 'fiddly'. You can not test the template from Excel , it has to be deployed and then run. Limitations The new templates are definitely superior to the Analyzer templates but there are a few limitations. Re-grouping is not supported. You can only follow a data hierarchy not bend it to your will unless you want to get into macros. No support for BIP functions. The templates support native XSL functions only. No template builder Getting Started The templates make the use of named cells and groups of cells to allow BIP to find the insertion point for data points. It also uses a hidden sheet to store calculation mappings from named cells to XML data elements. To start with, in the great BIP tradition, we need some sample XML data. Becasue I wanted to show the master-detail output we need some hierarchical data. If you have not yet gotten into the data templates, now is a good time, I wrote a post a while back starting from the simple to more complex. They generate ideal data sets for these templates. Im working with the following data set: <EMPLOYEES> <LIST_G_DEPT> <G_DEPT> <DEPARTMENT_ID>10</DEPARTMENT_ID> <DEPARTMENT_NAME>Administration</DEPARTMENT_NAME> <LIST_G_EMP> <G_EMP> <EMPLOYEE_ID>200</EMPLOYEE_ID> <EMP_NAME>Jennifer Whalen</EMP_NAME> <EMAIL>JWHALEN</EMAIL> <PHONE_NUMBER>515.123.4444</PHONE_NUMBER> <HIRE_DATE>1987-09-17T00:00:00.000-06:00</HIRE_DATE> <SALARY>4400</SALARY> </G_EMP> </LIST_G_EMP> <TOTAL_EMPS>1</TOTAL_EMPS> <TOTAL_SALARY>4400</TOTAL_SALARY> <AVG_SALARY>4400</AVG_SALARY> <MAX_SALARY>4400</MAX_SALARY> <MIN_SALARY>4400</MIN_SALARY> </G_DEPT> ... <LIST_G_DEPT> <EMPLOYEES> Simple enough to follow and bread and butter stuff for an RTF template. Building the Template For an Excel template we need to start by thinking about how we want to render the data. Come up with a sample output in Excel. Its all dummy data, nothing marked up yet with one row of data for each level. I have the department name and then a repeating row for the employees. You can apply Excel formatting to the layout. The total is going to be derived from a data element. We'll get to Excel functions later. Marking Up Cells Next we need to start marking up the cells with custom names to map them to data elements. The cell names need to follow a specific format: For data grouping, XDO_GROUP_?group_name? For data elements, XDO_?element_name? Notice the question mark delimter, the group_name and element_name are case sensitive. The next step is to find how to name cells; the easiest method is to highlight the cell and then type in the name. You can also find the Name Manager dialog. I use 2007 and its available on the ribbon under the Formulas section Go thorugh the process of naming all the cells for the element values you have. Using my data set from above.You should end up with something like this in your 'Name Manager' dialog. You can update any mistakes you might have made through this dialog. Creating Groups In the image above you can see there are a couple of named group cells. To create these its a simple case of highlighting the cells that make up the group and then naming them. For the EMP group, highlight the employee row and then type in the name, XDO_GROUP?G_EMP? Notice the 10,000 total is outside of the G_EMP group. Its actually named, XDO_?TOTAL_SALARY?, a query calculated value. For the department group, we need to include the department name cell and the sub EMP grouping and name it, XDO_GROUP?G_DEPT? Notice, the 10,000 total is included in the G_DEPT group. This will ensure it repeats at the department level. Lastly, we do need to include a special sheet in the workbook. We will not have anything meaningful in there for now, but it needs to be present. Create a new sheet and name it XDO_METADATA. The name is important as the BIP rendering engine will looking for it. For our current example we do not need anything other than the required stuff in our XDO_METADATA sheet but, it must be present. Easy enough to hide it. Here's what I have: The only cell that is important is the 'Data Constraints:' cell. The rest is optional. To save curious users getting distracted, hide the metadata sheet. Deploying & Running Templates We should now have a usable Excel template. Loading it into a report is easy enough using the browser UI, just like an RTF template. Set the template type to Excel. You will now be able to run the report and hopefully get something like this. You will not get the red highlighting, thats just some conditional formatting I added to the template using Excel functionality. Your dates are probably going to look raw too. I got around this for now using an Excel function on the cell: =--REPLACE(SUBSTITUTE(E8,"T"," "),LEN(E8)-6,6,"") Google to the rescue on that one. Try some other stuff out. To avoid constantly loading the template through the UI. If you have BIP running locally or you can access the reports repository, once you have loaded the template the first time. Just save the template directly into the report folder. I have put together a sample report using a sample data set, available here. Just drop the xml data file, EmpbyDeptExcelData.xml into 'demo files' folder and you should be good to go. Thats the basics, next we'll start using some XSL functions in the template and move onto the 'bursting' across sheets.

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  • How do you scale a pictureLink object in Excel 2010

    - by vzczc
    In Excel 2007 it is possible to scale a pictureLink object (created with the Camera Tool) using the following VBA code. With ActiveWorkbook.Sheets(sht).Pictures(name) .ShapeRange.ScaleWidth scaleValue, msoTrue .ShapeRange.ScaleHeight scaleValue, msoTrue .top = top .left = left End With This code places the picture correctly in 2010, but the scaleValue is ignored. The 2010 Excel documentation is patchy on this subject. The same code works fine in Excel 2007.

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  • Excel: Make conditional formatting static

    - by Martin
    Is there any way to convert conditional formatting to static formatting in Excel? I'm trying to export a range of a Excel Sheet to a new Workbook, with identical appearance but no formulas, links, etc. The problem here is that I have conditional formatting that relies on calculations outside exported range. I've tried saving the workbook to .html, oddly enough the formatting shows in IE but not when reopening it in Excel.

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  • OpenXML - using Excel sheet as template vs. a "real" Excel template

    - by marc_s
    Does anyone have any good answer what kind of difference there is between using some arbitrary pre-formatted Excel 2007 *.xlsx file as a template, loading it in my C# app, and filling up some of its cells with data using the Microsoft OpenXML SDK versus creating specific Excel templates (*.xltx) files and using those as basis for my "data filling" exercise Do I loose something when I don't use the Excel templates (*.xltx)? If so - what do I loose?

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  • Dynamic Column lookup with different pages in excel

    - by CinCity
    I have a multi page spread sheet in excel that needs to read information dynamically from columns on other pages and have these values show up on a main page. This is the formula I'm using: =IF(VLOOKUP($B:$B,'CP01'!$B:$BN,3,FALSE)="r","r", IF(VLOOKUP($B:$B,'CP01'!$B:$BN,3,FALSE)="a","a","")) CP01 is a sheet in the excel file and instead of look at the specific sheet I want it to look at all of the sheets in the file. Is there a way to do this as an excel formula or with excel-VBA? Edit: I also tried CP* (* being a wildcard character) and it didn't work. Edit2: Is there a way to match the value where the 'CP' is placed with its a other columns value?

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  • Big Data – Basics of Big Data Analytics – Day 18 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the various components in Big Data Story. In this article we will understand what are the various analytics tasks we try to achieve with the Big Data and the list of the important tools in Big Data Story. When you have plenty of the data around you what is the first thing which comes to your mind? “What do all these data means?” Exactly – the same thought comes to my mind as well. I always wanted to know what all the data means and what meaningful information I can receive out of it. Most of the Big Data projects are built to retrieve various intelligence all this data contains within it. Let us take example of Facebook. When I look at my friends list of Facebook, I always want to ask many questions such as - On which date my maximum friends have a birthday? What is the most favorite film of my most of the friends so I can talk about it and engage them? What is the most liked placed to travel my friends? Which is the most disliked cousin for my friends in India and USA so when they travel, I do not take them there. There are many more questions I can think of. This illustrates that how important it is to have analysis of Big Data. Here are few of the kind of analysis listed which you can use with Big Data. Slicing and Dicing: This means breaking down your data into smaller set and understanding them one set at a time. This also helps to present various information in a variety of different user digestible ways. For example if you have data related to movies, you can use different slide and dice data in various formats like actors, movie length etc. Real Time Monitoring: This is very crucial in social media when there are any events happening and you wanted to measure the impact at the time when the event is happening. For example, if you are using twitter when there is a football match, you can watch what fans are talking about football match on twitter when the event is happening. Anomaly Predication and Modeling: If the business is running normal it is alright but if there are signs of trouble, everyone wants to know them early on the hand. Big Data analysis of various patterns can be very much helpful to predict future. Though it may not be always accurate but certain hints and signals can be very helpful. For example, lots of data can help conclude that if there is lots of rain it can increase the sell of umbrella. Text and Unstructured Data Analysis: unstructured data are now getting norm in the new world and they are a big part of the Big Data revolution. It is very important that we Extract, Transform and Load the unstructured data and make meaningful data out of it. For example, analysis of lots of images, one can predict that people like to use certain colors in certain months in their cloths. Big Data Analytics Solutions There are many different Big Data Analystics Solutions out in the market. It is impossible to list all of them so I will list a few of them over here. Tableau – This has to be one of the most popular visualization tools out in the big data market. SAS – A high performance analytics and infrastructure company IBM and Oracle – They have a range of tools for Big Data Analysis Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Data Scientist. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Excel crashes when opening Excel files from Internet Explorer

    - by Rob
    I have been running into some issues when opening Excel files from Internet Explorer, generally the first document or two will open fine but after that trying to open a file will cause Excel and Internet Explorer to crash to the desktop without any notifications being given. This doesn't happen for users who are running Excel 2007, but for users with Excel 2003 it may or may not happen to them. The files in question are Excel XML files and Internet Explorer 6 and Excel 2003 are being use. At this time it would not be possible to upgrade Internet Explorer, but it would be able to upgrade to Excel to version 2007 if that would resolve the issue. Overdue Update: We recently upgraded to Firefox at the office which has rendered this error a non-issue; however, it is still unresolved from the standpoint that we haven't been able to come up with an explanation to the issue. Since IE6 is still installed on the systems, a fix to the problem (or explanation of why it's happening) would be appreciated.

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  • Excel crashes when opening Excel files from Internet Explorer

    - by Rob Z
    I have been running into some issues when opening Excel files from Internet Explorer, generally the first document or two will open fine but after that trying to open a file will cause Excel and Internet Explorer to crash to the desktop without any notifications being given. This doesn't happen for users who are running Excel 2007, but for users with Excel 2003 it may or may not happen to them. The files in question are Excel XML files and Internet Explorer 6 and Excel 2003 are being use. At this time it would not be possible to upgrade Internet Explorer, but it would be able to upgrade to Excel to version 2007 if that would resolve the issue. Overdue Update: We recently upgraded to Firefox at the office which has rendered this error a non-issue; however, it is still unresolved from the standpoint that we haven't been able to come up with an explanation to the issue. Since IE6 is still installed on the systems, a fix to the problem (or explanation of why it's happening) would be appreciated.

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  • Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

    - by Pinal Dave
    Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • The Data Scientist

    - by BuckWoody
    A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it. In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets. The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly. I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas. Persistence The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all? This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization. Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to. Processing Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on. A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one. Presentation This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays. This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does. And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist. Why I’m excited I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail. Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data. So  watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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  • Free libraries to work with Excel

    - by Danil Gholtsman
    I got some excel files, I need to read data from it and upload data to some database (I need to use firebird, but whatever). Right now I use <QAxObject> from Qt and code look like QAxObject* excel = new QAxObject("Excel.Application"); //pointer to excel //excel->setProperty("Visible", false); QAxObject* workbooks = excel->querySubObject("WorkBooks"); //get pointer to booklist workbooks->dynamicCall("Open (const QString&)", QString("C:\\databases\\test.xls")); //opening file, getting pointer to booklist QAxObject* workbook = excel->querySubObject("ActiveWorkBook"); QAxObject* worksheets = workbook->querySubObject("WorkSheets"); etc. The problem is that this way on users PC there must be installed Excel. Is there exists some free C++ libraries to work with *.xls, *.xlsx files without Excel installed?

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  • Fraud Detection with the SQL Server Suite Part 2

    - by Dejan Sarka
    This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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  • Internal drives vs USB-3 with external SSD or eSata with External SSD

    - by normstorm
    I have a need to carry VMWare Virtual Machines with me for work. These are very large files (each VM is 20GB or more) and I carry around about 40 to 50 VM's to simulate different software configurations for different client needs. Key: they won't fit on the internal hard drive of my current laptop. I currently execute the VM's from an external 7200RPM 2.5" USB-2 drive. I keep copies of the VM's on other 5400 external USB-2 drives. The VM's work from this drive, but they are slow, costing me much time and frustration. It can take upwards of 30 minutes just to make a copy of one of the VM's. They can take upwards of 10-15 minutes to fully launch and then they operate sluggishly. I am buying a new laptop (Core I7, 8GB RAM and other high-end specs). I intend to buy an SSD for the O/S volume (C:). This SSD will not be large enough to hold the VM's. I have always wanted a second internal hard drive to operate the VM's. To have two hard drives, though, I am finding that I will have to go to a 17" laptop which would be bulky/heavy. I am instead considering purchasing a 15" laptop with either an eSATA port or USB-3 ports and then purchasing two external drives. One of the drives might be an external SSD (maybe OCX brand) for operating the VM's and the other a 7400RPM 1TB hard drive for carrying around the VM's not currently in use. The question is which options would give me the biggest bang for the buck and the weight: 1) 2nd Internal SSD hard drive. This would mean buying a 17" laptop with two drive "bays". The first bay would hold an SSD drive for the C: drive. I would leave the first bay empty from the manufacture and then purchase/install an aftermarket SSD drive. This second SSD drive would have to be very large (256 GB), which would be expensive. I would still also need another external hard drive for carrying around the VM's not in use. 2) 2nd internal hard drive - 7400 RPM. Again, a 17" laptop would be required, but there are models available with on SSD drive for the C: drive and a second 7200 RPM hard drives. The second drive could probably be large enough to hold the VM's in use as well as those not in use. But would it be fast enough to drive the VM's? 3) USB-3 with External SSD. I could buy a 15" laptop with an SSD drive for the C: drive and a second hard drive for general files. I would operate the VM's from an external USB-3 SSD drive and have a third USB-3 external 7200 RPM drive for holding the VM's not in use. 4) eSATA with External SSD. Ditto, just eSATA instead of USB-3 5) USB-3 with External 7400 RPM drive. Ditto, but the drive running the VM's would be USB-3 attached 7400 RPM drives rather than SSD. 6) eSATA with External 7400 RPM drive. Dittor, but the drive running the VM's would be eSATA attached 7400 RPM drives rather than SSD. Any thoughts on this and any creative solutions?

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  • Big Data – Beginning Big Data Series Next Month in 21 Parts

    - by Pinal Dave
    Big Data is the next big thing. There was a time when we used to talk in terms of MB and GB of the data. However, the industry is changing and we are now moving to a conversation where we discuss about data in Petabyte, Exabyte and Zettabyte. It seems that the world is now talking about increased Volume of the data. In simple world we all think that Big Data is nothing but plenty of volume. In reality Big Data is much more than just a huge volume of the data. When talking about the data we need to understand about variety and volume along with volume. Though Big data look like a simple concept, it is extremely complex subject when we attempt to start learning the same. My Journey I have recently presented on Big Data in quite a few organizations and I have received quite a few questions during this roadshow event. I have collected all the questions which I have received and decided to post about them on the blog. In the month of October 2013, on every weekday we will be learning something new about Big Data. Every day I will share a concept/question and in the same blog post we will learn the answer of the same. Big Data – Plenty of Questions I received quite a few questions during my road trip. Here are few of the questions. I want to learn Big Data – where should I start? Do I need to know SQL to learn Big Data? What is Hadoop? There are so many organizations talking about Big Data, and every one has a different approach. How to start with big Data? Do I need to know Java to learn about Big Data? What is different between various NoSQL languages. I will attempt to answer most of the questions during the month long series in the next month. Big Data – Big Subject Big Data is a very big subject and I no way claim that I will be covering every single big data concept in this series. However, I promise that I will be indeed sharing lots of basic concepts which are revolving around Big Data. We will discuss from fundamentals about Big Data and continue further learning about it. I will attempt to cover the concept so simple that many of you might have wondered about it but afraid to ask. Your Role! During this series next month, I need your one help. Please keep on posting questions you might have related to big data as blog post comments and on Facebook Page. I will monitor them closely and will try to answer them as well during this series. Now make sure that you do not miss any single blog post in this series as every blog post will be linked to each other. You can subscribe to my feed or like my Facebook page or subscribe via email (by entering email in the blog post). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Big Data, PostADay, SQL, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Problem with external monitor on my asus u36sd laptop

    - by Abonec
    To connect my laptop to external monitor (for the dual monitor configuration) I have to perform 7 weird steps: Suspend OS (close notebook for that) Connect external monitor to vga output Open notebook and unsuspend OS (at this moment in laptop screen is native resolution but on external monitor resolution is lower than native (not same as at laptop)). Suspend OS (close notebook for that) Open notebook and unsuspend OS (at this moment laptop screen has resolution as a external monitor but external monitor has lower resolution that should be in native) Suspend OS (close notebook for that) Unsuspend OS (at this moment laptop and external monitor have native resolution which will should be) I just open and close hood of the laptop until external monitor and laptop screen become in native resolution. Adjusting monitors in displays not give me proper result. I have ASUS U36SD with optimus (disabled by acpicall) with 1366x768 screen and external monitor with 1280x1024 and latest ubuntu 11.10. How to perform laptop to work with external monitor without this weird actions?

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