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  • Business Intelligence goes Big Data

    - by Alliances & Channels Redaktion
    Big Data stellt die nächste große Herausforderung für die IT-Branche dar: Massen von Daten aus immer mehr Quellen – aus sozialen Netzwerken, Telekommunikations- und Weblogs, RFID-Lesern etc. – müssen logisch verknüpft, in Echtzeit integriert und verarbeitet werden. Doch wie sieht es mit der praktischen Umsetzung aus? Eine europaweite Studie von Steria Mummert Consulting zeigt: Lediglich 28 % der Unternehmen haben bereits heute eine übergreifende, abgestimmte Business-Intelligence-Strategie implementiert. Vorherrschend sind BI-Insellösungen, die schon jetzt an den Grenzen ihrer Kapazität arbeiten. Daten werden also bisher nur eingeschränkt als wertschöpfende Ressource genutzt! Das Ergebnis der Studie klingt erschreckend, doch Unternehmen können es zu Ihrem Vorteil nutzen: Wer jetzt das Thema Big Data anpackt, kann sich einen gewinnbringenden Vorsprung vor dem Wettbewerb sichern. Wie sieht die Analyse-Umgebung der Zukunft aus? Wie und wo kann Big Data für den Geschäftserfolg genutzt werden? Antworten darauf liefert die Kunden-Event Reihe von Oracle und dem Oracle Platinum Partner Steria Mummert Consulting: Hier werden Strategien entwickelt, wie Unternehmen mit Information Discovery ihr BI-Potenzial auf dem Weg zur Big Data Schritt für Schritt ausbauen können. Highlights aus München Durchweg positives Feedback haben wir aus München, der ersten Station der Eventreihe am 23.7., erhalten: Nicht nur die tolle Location, das "La Villa" im Bamberger Haus, überzeugte. Die 31 Teilnehmerinnen und Teilnehmer konnten auch inhaltlich eine Menge mitnehmen – unter anderem einen konkreten Vorschlag für ihre eigene Roadmap in Richtung Big Data. Die Ausgangsfrage des Tages lautete – einfach und umfassend zugleich: Wie können wir den Überblick in einer komplexen Welt behalten? Den Status quo in Europa für Business Intelligence präsentierte Steria Mummert Consulting entlang der Europäischen biMA®-Studie 2012/13. Anhand von Anwendungsbeispielen aus ihrer Praxis präsentierten die geladenen Experten von Oracle und Steria Mummert Consulting verschiedene Lösungsansätze. Eine sehr anschauliche Demo zu Endeca zeigte beispielsweise, wie einfach und flexibel ein Dashboard sein kann: Hier gibt es keine vordefinierten Reports, stattdessen können Entscheider die Filter einfach per Drag & Drop verändern und bekommen so einen individuell sturkturierten Überblick über ihre Daten. Einen Ausblick bot die Session zu Oracle Business Analytics für mobile Anwendungen und Real-Time Decisions. Fazit: eine gelungene Mischung aus Überblicks-Informationen und ganz konkreten Ideen für die spezifischen Anwendungsbereiche der Kunden. Die Eventreihe „BI goes Big Data“ macht im August in Hamburg und Frankfurt Station. Die kostenfreie Veranstaltung findet zusammen mit Steria Mummert Consulting statt und richtet sich an Endkunden. In Hamburg am 14.8.2013 – zur AnmeldungIn Frankfurt a.M. am 20.8.2013 – zur Anmeldung

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  • Business Intelligence goes Big Data

    - by Alliances & Channels Redaktion
    Big Data stellt die nächste große Herausforderung für die IT-Branche dar: Massen von Daten aus immer mehr Quellen – aus sozialen Netzwerken, Telekommunikations- und Weblogs, RFID-Lesern etc. – müssen logisch verknüpft, in Echtzeit integriert und verarbeitet werden. Doch wie sieht es mit der praktischen Umsetzung aus? Eine europaweite Studie von Steria Mummert Consulting zeigt: Lediglich 28 % der Unternehmen haben bereits heute eine übergreifende, abgestimmte Business-Intelligence-Strategie implementiert. Vorherrschend sind BI-Insellösungen, die schon jetzt an den Grenzen ihrer Kapazität arbeiten. Daten werden also bisher nur eingeschränkt als wertschöpfende Ressource genutzt! Das Ergebnis der Studie klingt erschreckend, doch Unternehmen können es zu Ihrem Vorteil nutzen: Wer jetzt das Thema Big Data anpackt, kann sich einen gewinnbringenden Vorsprung vor dem Wettbewerb sichern. Wie sieht die Analyse-Umgebung der Zukunft aus? Wie und wo kann Big Data für den Geschäftserfolg genutzt werden? Antworten darauf liefert die Kunden-Event Reihe von Oracle und dem Oracle Platinum Partner Steria Mummert Consulting: Hier werden Strategien entwickelt, wie Unternehmen mit Information Discovery ihr BI-Potenzial auf dem Weg zur Big Data Schritt für Schritt ausbauen können. Highlights aus München Durchweg positives Feedback haben wir aus München, der ersten Station der Eventreihe am 23.7., erhalten: Nicht nur die tolle Location, das "La Villa" im Bamberger Haus, überzeugte. Die 31 Teilnehmerinnen und Teilnehmer konnten auch inhaltlich eine Menge mitnehmen – unter anderem einen konkreten Vorschlag für ihre eigene Roadmap in Richtung Big Data. Die Ausgangsfrage des Tages lautete – einfach und umfassend zugleich: Wie können wir den Überblick in einer komplexen Welt behalten? Den Status quo in Europa für Business Intelligence präsentierte Steria Mummert Consulting entlang der Europäischen biMA®-Studie 2012/13. Anhand von Anwendungsbeispielen aus ihrer Praxis präsentierten die geladenen Experten von Oracle und Steria Mummert Consulting verschiedene Lösungsansätze. Eine sehr anschauliche Demo zu Endeca zeigte beispielsweise, wie einfach und flexibel ein Dashboard sein kann: Hier gibt es keine vordefinierten Reports, stattdessen können Entscheider die Filter einfach per Drag & Drop verändern und bekommen so einen individuell sturkturierten Überblick über ihre Daten. Einen Ausblick bot die Session zu Oracle Business Analytics für mobile Anwendungen und Real-Time Decisions. Fazit: eine gelungene Mischung aus Überblicks-Informationen und ganz konkreten Ideen für die spezifischen Anwendungsbereiche der Kunden. Die Eventreihe „BI goes Big Data“ macht im August in Hamburg und Frankfurt Station. Die kostenfreie Veranstaltung findet zusammen mit Steria Mummert Consulting statt und richtet sich an Endkunden. In Hamburg am 14.8.2013 – zur AnmeldungIn Frankfurt a.M. am 20.8.2013 – zur Anmeldung

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  • Big level objects collision system for 2d game

    - by Aristarhys
    I read many variants today and get some knowledge in general, so here is a steps of mine thoughts in pictures (horrible paint.net ones). We need to develop grid system, so we check only thing near, perform simple check to cut out deep check, and at - last deep check like per-pixel collision check. Step 1 - Let p1, p2 are some sprites lets first just check with circle collision - because large distance between p1, p2 this fails and of course so we don't need test more deeply. But if we have not 2, but 20 objects, why we need to even circle test something so far outside of our view. Step 2 - Add basic column system, now we don't bother with p2 if it's in a column far from p1 column, so we even don't do circle test. But p3 is in the same col, so let do circle test, which of course will fail. Step 3 - Lets improve column system to the grid system with grid cell size just like p1, p2, p3 collision boxes, so we cut out things much top or below p1. And this is all great until comes BIG OBJs which is some kind of platforms. They are much bigger then grid cell. Circle test for will be successful, but deep check for whole big obj will fail And that the part I can't get. How do I store the grid position of big object? Like 4 grid coords for big object vertexes? And if one of them close to p1 do circle check for centre of big object then a deep one if succeed? Am I do it wrong? My possible solution:

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  • Two interesting big data sessions around Openworld

    - by Jean-Pierre Dijcks
    For those who want to talk (not listen) about big data, here are 2 very cool sessions: BOF9877 - A birds of a feather session around all things big data. It is on Monday, Oct 1, 6:15 PM - 7:00 PM - Marriott Marquis - Golden Gate. While all guests on the panel are special, we will have very special guest on the panel. He is a proud owner of a Big Data Appliance (see here). Then there is a Big Data SIG meeting (the invite from Gwen): I'd like to invite everyone to our OOW12 meet up. We'll meet on Tuesday, October 2nd, 8:45 to 9:45 at Moscone West Level 3, Overlook 3. We will network, socialize and discuss plans for the group. Which topics interest us for webinars? Which conferences do we want to meet in? What other activities we are interested in? We can also discuss big data topics, show off our great work, and seek advice on the challenges. Other than figuring out what we are collectively interested in, the discussion will be pretty open. Here is the official invite. See you at Openworld!!

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  • MIPS assembly: big and little endian confusion

    - by Barney
    I've run the following code snippet on the MIPS MARS simulator. That simulator is little endian. So the results are as follows: lui $t0,0x1DE # $t0 = 0x01DE0000 ori $t0,$t0,0xCADE # $t0 = 0x01DECADE lui $t1,0x1001 # $t1 = 0x10010000 sw $t0,200($t1) # $t1 + 200 bytes = 0x01DECADE lw $t2,200($t1) # $t2 = 0x01DECADE So on a little endian MIPS simulator, the value of $t2 at the end of the program is 0x01DECADE. If this simulator was big endian, what would the value be? Would it be 0xDECADE01 or would it still be 0x01DECADE?

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  • Endian check in C

    - by webgenius
    Got this code snippet from some website: int num = 1; if(*(char *)&num == 1) { printf("\nLittle-Endian\n"); } else { printf("Big-Endian\n"); } Can anyone explain this step-by-step? &num - Adress of a (char *)&num - Type-cast address of a into a string *(char *)&num - Points to the first character of the string Am I missing anything here?

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  • “Big Data” Is A Small Concept Unless You Can Apply It To The Customer Experience

    - by Michael Hylton
    There’s been a lot of recent talk in the industry about “big data”.  Much can be said about the importance of big data and the results from it, but you need to always consider the customer experience when analyzing and applying customer data. Personalization and merchandising drive the user experience.  Big data should enable you to gain valuable insight into each of your customers and apply that insight at the moment they are on your Web site, talking to one of your call center agents, or any other touchpoint.  While past customer experience is important, you need to combine that with what your customer is doing on your Web site now as well what they are doing and saying on social networking sites.  It’s key to have a 360 degree view of your customer across all of your touchpoints in order to provide that relevant and consistent experience that they come to expect when interacting with your brand. Big data can enable you to effectively market, merchandize, and recommend the right products to the right customers and the right time.  By taking customer data and applying it to product recommendations, you have an opportunity to gain a greater share of wallet through the cross-selling and up-selling of additional products and services.  You can also build sustaining loyalty programs to continue to engage with your customers throughout their long-term relationship with your brand.

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  • “Big Data” Is A Small Concept Unless You Can Apply It To The Customer Experience

    - by Michael Hylton
    There’s been a lot of recent talk in the industry about “big data”.  Much can be said about the importance of big data and the results from it, but you need to always consider the customer experience when analyzing and applying customer data. Personalization and merchandising drive the user experience.  Big data should enable you to gain valuable insight into each of your customers and apply that insight at the moment they are on your Web site, talking to one of your call center agents, or any other touchpoint.  While past customer experience is important, you need to combine that with what your customer is doing on your Web site now as well what they are doing and saying on social networking sites.  It’s key to have a 360 degree view of your customer across all of your touchpoints in order to provide that relevant and consistent experience that they come to expect when interacting with your brand. Big data can enable you to effectively market, merchandize, and recommend the right products to the right customers and the right time.  By taking customer data and applying it to product recommendations, you have an opportunity to gain a greater share of wallet through the cross-selling and up-selling of additional products and services.  You can also build sustaining loyalty programs to continue to engage with your customers throughout their long-term relationship with your brand.

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  • Big Data Appliance

    - by David Dorf
    Today Oracle announced the next release of it's Big Data Appliance, an engineered system composed of hardware and software targeting the efficient processing of big data.  The solution leverages 288 Intel cores running Cloudera's distribution of Apache Hadoop in 1.1 TB of main memory.  This monster helps companies acquire, organize, and analyze large volumes of structured and un-structured data. Additionally a new versions of the Oracle Big Data Connectors and Oracle NoSQL Database were released. Why is this important to retailers?  As the infographic below conveys, mobile and social have added even more data to the already huge collections of POS transactions and e-commerce weblogs.  Retailers know that mining that data will help them make better decisions that lead to increased sales, better customer service, and ultimately a successful retail business. Monetate

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  • "Adoption des Big Data : ce n'est que le commencement", selon Talend, qui analyse cette nouvelle tendance

    « Adoption des Big Data : ce n'est que le commencement », selon Talend qui affirme que les entreprises mettent en place des stratégies de Big Data Les volumes de données augmentent à un rythme croissant. De plus en plus, les entreprises explorent leurs usages et trouvent des moyens pour traiter, exploiter, analyser et fouiller les données qu'elles collectent, afin d'en tirer les connaissances qui serviront de base à leurs décisions futures. Yves de Montcheuil, VP Marketing, Talend, livre son analyse suite à une nouvelle enquête sur l'adoption des Big Data réalisée par l'éditeur auprès de professionnels impliqués dans la délivrance de solutions de données, qui confirme cette mat...

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  • R Statistical Analytics with Faster Performance for Enterprise Database Access and Big Data

    - by Mike.Hallett(at)Oracle-BI&EPM
    Further demonstrating commitment to the open source community, Oracle has just released enhanced support of the R statistical programming language for Oracle Solaris and AIX in addition to Linux and Windows, connectivity to Oracle TimesTen In-Memory Database in addition to Oracle Database, and integration of hardware-specific Math libraries for faster performance.  Oracle’s Open Source distribution of R is available with the Oracle Big Data Appliance and available for download now. Oracle also offers Oracle R Enterprise, a component of Oracle Advanced Analytics that enables R processing on Oracle Database servers.   This all goes to make big data analytics more accessible in the enterprise and improving data scientist productivity with faster performance Since its introduction in 1995, R has attracted more than two million users and is widely used today for developing statistical applications that analyze big data. Analyst Report: Oracle Advances its Advanced Analytics Strategy  

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  • Darth Vader Wins Big [Humorous Comic]

    - by Asian Angel
    Everyone’s favorite Star Wars villain receives a notice in the mail saying he won a contest, but did he really hit it big or is karma dishing out some payback? Note: Make sure to take a close look at the letter shown in the second panel for an additional laugh! Darth Vader Wins Big (Dorkly) [via Neatorama] HTG Explains: Why Linux Doesn’t Need Defragmenting How to Convert News Feeds to Ebooks with Calibre How To Customize Your Wallpaper with Google Image Searches, RSS Feeds, and More

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  • Desktop Fun: Big Game Cats Wallpaper Collection Series 2

    - by Asian Angel
    Two years ago we shared a wonderful collection of big game cats wallpapers with you and today we are back with more cattitude goodness for you. Fill your desktop with these sleek and graceful friends from the animal kingdom with the second in our series of Big Game Cats Wallpaper collections. How To Create a Customized Windows 7 Installation Disc With Integrated Updates How to Get Pro Features in Windows Home Versions with Third Party Tools HTG Explains: Is ReadyBoost Worth Using?

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  • Oracle Data Warehouse and Big Data Magazine MAY Edition for Customers + Partners

    - by KLaker
    Follow us on The latest edition of our monthly data warehouse and big data magazine for Oracle customers and partners is now available. The content for this magazine is taken from the various data warehouse and big data Oracle product management blogs, Oracle press releases, videos posted on Oracle Media Network and Oracle Facebook pages. Click here to view the May Edition Please share this link http://flip.it/fKOUS to our magazine with your customers and partners This magazine is optimized for display on tablets and smartphones using the Flipboard App which is available from the Apple App store and Google Play store

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  • How Big Data and Social Won the Election

    - by Mike Stiles
    The story of big data’s influence on the outcome of the US Presidential election is worth a good look, because a) it’s a harbinger of things to come, and b) it’s an example of similar successes available to any enterprise seriously resourcing integrated big data, modeling, and data-driven execution on all assets, including social. Obama campaign manager Jim Messina fielded a data and analytics brain trust 5 times larger than 2008. At that time, there were numerous databases from various sources, few of them talking to each other. This time, the mission was to be metrics-centered and measure everything measurable, and in context with all the other data. Big data showed them exactly what they needed to know and told them what to do about it. It showed them women 40-49 on the west coast would donate big money if they got to eat with George Clooney. Women on the east coast would pony up to hang out with Sarah Jessica Parker. Extensive daily modeling showed them what kinds of email appeals, from who, and to whom, would prove most successful in raising cash, recruiting volunteers, and getting out the vote. Swing state voters were profiled and approached with more customized targeting that at any time in history. Ads were purchased on specific shows watched by the targets, increasing efficiency 14% over traditional media buys. For all the criticism of the candidate’s focus on appearing on comedy and entertainment shows, and local radio morning shows, that’s where the data sent them to reach the voters most likely to turn out for them. And then there was social. Again, more than in any other election, Facebook was used for virtual, highly efficient door-to-door canvasing. Facebook fans got pictures of friends in swing states and were asked to encourage them to act. Using that approach, 1 in 5 peer-to-peer appeals led to the desired action. Assumptions, gut, intuition, campaign experience, all took a backseat to strategy shifts solidly backed up by data. Zeroing in on demographics likely to back the President and tracking their mood daily literally changed the voter landscape. The Romney team watched Obama voters appear seemingly out of thin air. One Obama campaign aide said, “We ran the election 66,000 times every night.” Which brings us to your organization. If you’re starting to feel like the battle-cry of “but this is the way we’ve always done it” is starting to put you in an extremely vulnerable position, you’re right. Social has become a key communication tool of the 21st century. Failing to use it, or failing to invest in a deep understanding of who your customers and prospects are so the content you post there will achieve desired actions and results, will leave you waking up one morning wondering, “What happened?”@mikestilesPhoto stock.xchng

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  • Oracle Social Analytics with the Big Data Appliance

    - by thegreeneman
    Found an awesome demo put together by one of the Oracle NoSQL Database partners, eDBA, on using the Big Data Appliance to do social analytics. In this video, James Anthony is showing off the BDA, Hadoop, the Oracle Big Data Connectors and how they can be used and integrated with the Oracle Database to do an end-to-end sentiment analysis leveraging twitter data.   A really great demo worth the view. 

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  • SQL Server 2012 disponible en version finale : AlwaysOn, Big Data, Power View, Microsoft tient ses promesses

    SQL Server 2012 disponible en version finale AlwaysOn, Big Data, Power View, la plateforme de gestion et d'analyse d'information de Microsoft tient ses promesses Mise à jour du 03/04/2012 Comme l'avait promis Microsoft, la version finale de SQL Server 2012 est disponible depuis le 1er avril, mais a été annoncée officiellement hier. La plateforme de gestion et d'analyse d'information de Microsoft a été conçue pour être l'environnement de référence des applications critiques d'entreprise, offrir une solution décisionnelle plus complète intégrant le Big Data et permettre une meilleure connexion avec le Cloud. ...

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  • NRF Big Show 2011 -- Part 3

    - by David Dorf
    I'm back from the NRF show having been one of the lucky people who's flight was not canceled. The show was very crowded with a reported 20% increase in attendance and everyone seemed in high spirits. After two years of sluggish retail sales, things are really picking up and it was reflected in everyone's mood. The pop-up Disney Store in the Oracle booth was great and attracted lots of interest in their mobile POS. I know many attendees visited the Disney Store in Times Square to see the entire operation. It's an impressive two-story store that keeps kids engaged. The POS demonstration station, where most of our innovations were demoed, was always crowded. Unfortunately most of the demos used WiFi and the signals from other booths prevented anything from working reliably. Nevertheless, the demo team did an excellent job walking people through the scenarios and explaining how shopping is being impacted by mobile, analytics, and RFID. Big Show Links Disney uncovers its store magic Top 10 Things You Missed at the NRF Big Show 2011 Oracle Retail Stores Innovation Station at NRF Big Show 2011 (video) The buzz of the show was again around mobile solutions. Several companies are creating mobile POS using the iPod Touch, including integrations to Oracle POS for the following retailers: Disney Stores with InfoGain Victoria's Secret with InfoGain Urban Outfitters with Starmount The Gap with Global Bay Keeping with the mobile theme, the NRF release a revised version of their Mobile Blueprint at NRF. It will be posted to the NRF site very soon. The alternate payments section had a major rewrite that provides a great overview and proximity and remote payment technologies. NRF Mobile Blueprint Links New mobile blueprint provides fresh insights NRF Mobile Blueprint 2011 (slides) I hope to do some posts on some of the interesting companies I spoke with in the coming weeks.

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  • Big Visible Charts

    - by Robert May
    An important part of Agile is the concept of transparency and visibility. In proper functioning teams, stakeholders can look at any team at any time in the iteration or release and see how that team is doing by simply looking at what we call Big Visible Charts. If you’ve done Scrum, you’ve seen these charts. However, interpreting these charts can often be an art form. There are several different charts that can be useful. In this newsletter, I’ll focus on the Iteration Burndown and Cumulative Flow charts. I’ve included a copy of the spreadsheet that I used to create the charts, and if you don’t have a tool that creates them for you, you can use this spreadsheet to do so. Our preferred tool for managing Scrum projects is Rally. Rally creates all of these charts for you, saving you quite a bit of time. The Iteration Burndown and Cumulative Flow Charts This is the main chart that teams use. Although less useful to stakeholders, this chart is critical to the team and provides quite a bit of information to the team about how their iteration is going. Most charts are a combination of the charts below, so you may need to combine aspects of each section to understand what is happening in your iterations. Ideal Ah, isn’t that a pretty picture? Unfortunately, it’s also very unrealistic. I’ve seen iterations that come close to ideal, but never that match perfectly. If your iteration matches perfectly, chances are, someone is playing with the numbers. Reality is just too difficult to have a burndown chart that matches this exactly. Late Planning Iteration started, but the team didn’t. You can tell this by the fact that the real number of estimated hours didn’t appear until day two. In the cumulative flow, you can also see that nothing was defined in Day one and two. You want to avoid situations like this. You’ll note that the team had to burn faster than is ideal to meet the iteration because of the late planning. This often results in long weeks and days. Testing Starved Determining whether or not testing is starved is difficult without the cumulative flow. The pattern in the burndown could be nothing more that developers not completing stories early enough or could be caused by stories being too big. With the cumulative flow, however, you see that only small bites are in progress and stories were completed early, but testing didn’t start testing until the end of the iteration, and didn’t complete testing all stories in the iteration. When this happens, question whether or not your testing resources are sufficient for your team and whether or not acceptance is adequately defined. No Testing With this one, both graphs show the same thing; the team needs testers and testing! Without testing, what was completed cannot be verified to make sure that it is acceptable to the business. If you find yourself in this situation, review your testing practices and acceptance testing process and make changes today. Late Development With this situation, both graphs tell a story. In the top graph, you can see that the hours failed to burn down as quickly as the team expected. This could be caused by the team not correctly estimating their hours or the team could have had illness or some other issue that affected them. Often, when teams are tackling something that is more unknown, they’ll run into technical barriers that cause the burn down to happen slower than expected. In the cumulative flow graph, you can see that not much was completed in the first few days. This could be because of illness or technical barriers or simply poor estimation. Testing was able to keep up with everything that was completed, however. No Tool Updating When you see graphs that look like this, you can be assured that it’s because the team is not updating the tool that generates the graphs. Review your policy for when they are to update. On the teams that I run, I require that each team member updates the tool at least once daily. You should also check to see how well the team is breaking down stories into tasks. If they’re creating few large tasks, graphs can look similar to this. As a general rule, I never allow tasks, other than Unit Testing and Uncertainty, to be greater than eight hours in duration. Scope Increase I always encourage team members to enter in however much time they think they have left on a task, even if that means increasing the total amount of time left to do. You get a much better and more realistic picture this way. Increasing time remaining could explain the burndown graph, but by looking at the cumulative flow graph, we can see that stories were added to the iteration and scope was increased. Since planning should consume all of the hours in the iteration, this is almost always a bad thing. If the scope change happened late in the iteration and the hours remaining were well below the ideal burn, then increasing scope is probably o.k., but estimation needs to get better. However, with the charts above, that’s clearly not what happened and the team was required to do extra work to make the iteration. If you find this happening, your product owner and ScrumMasters need training. The team also needs to learn to say no. Scope Decrease Scope decreases are just as bad as scope increases. Usually, graphs above show that the team did a poor job of estimating their stories and part way through had to reduce scope to change the iteration. This will happen once in a while, but if you find it’s a pattern on your team, you need to re-evaluate planning. Some teams are hopelessly optimistic. In those cases, I’ll introduce a task I call “Uncertainty.” With Uncertainty, the team estimates how many hours they might need if things don’t go well with the tasks they’ve defined. They try to estimate things that could go poorly and increase the time appropriately. Having an Uncertainty task allows them to have a low and high estimate. Uncertainty should not just be an arbitrary buffer. It must correlate to real uncertainty in the tasks that have been defined. Stories are too Big Often, we see graphs like the ones above. Note that the burndown looks fairly good, other than the chunky acceptance of stories. However, when you look at cumulative flow, you can see that at one point, everything is in progress. This is a bad thing. When you see graphs like this, you’re in one of two states. You may just have a very small team and can only handle one or two stories in your iteration. If you have more than one or two people, then the most likely problem is that your stories are far too big. To combat this, break large high hour stories into smaller pieces that can be completed independently and accepted independently. If you don’t, you’ll likely be requiring your testers to do heroic things to complete testing on the last day of the iteration and you’re much more likely to have the entire iteration fail, because of the limited amount of things that can be completed. Summary There are other charts that can be useful when doing scrum. If you don’t have any big visible charts, you really need to evaluate your process and change. These charts can provide the team a wealth of information and help you write better software. If you have any questions about charts that you’re seeing on your team, contact me with a screen capture of the charts and I’ll tell you what I’m seeing in those charts. I always want this information to be useful, so please let me know if you have other questions. Technorati Tags: Agile

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  • Windows Azure Recipe: Big Data

    - by Clint Edmonson
    As the name implies, what we’re talking about here is the explosion of electronic data that comes from huge volumes of transactions, devices, and sensors being captured by businesses today. This data often comes in unstructured formats and/or too fast for us to effectively process in real time. Collectively, we call these the 4 big data V’s: Volume, Velocity, Variety, and Variability. These qualities make this type of data best managed by NoSQL systems like Hadoop, rather than by conventional Relational Database Management System (RDBMS). We know that there are patterns hidden inside this data that might provide competitive insight into market trends.  The key is knowing when and how to leverage these “No SQL” tools combined with traditional business such as SQL-based relational databases and warehouses and other business intelligence tools. Drivers Petabyte scale data collection and storage Business intelligence and insight Solution The sketch below shows one of many big data solutions using Hadoop’s unique highly scalable storage and parallel processing capabilities combined with Microsoft Office’s Business Intelligence Components to access the data in the cluster. Ingredients Hadoop – this big data industry heavyweight provides both large scale data storage infrastructure and a highly parallelized map-reduce processing engine to crunch through the data efficiently. Here are the key pieces of the environment: Pig - a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Mahout - a machine learning library with algorithms for clustering, classification and batch based collaborative filtering that are implemented on top of Apache Hadoop using the map/reduce paradigm. Hive - data warehouse software built on top of Apache Hadoop that facilitates querying and managing large datasets residing in distributed storage. Directly accessible to Microsoft Office and other consumers via add-ins and the Hive ODBC data driver. Pegasus - a Peta-scale graph mining system that runs in parallel, distributed manner on top of Hadoop and that provides algorithms for important graph mining tasks such as Degree, PageRank, Random Walk with Restart (RWR), Radius, and Connected Components. Sqoop - a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases. Flume - a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large log data amounts to HDFS. Database – directly accessible to Hadoop via the Sqoop based Microsoft SQL Server Connector for Apache Hadoop, data can be efficiently transferred to traditional relational data stores for replication, reporting, or other needs. Reporting – provides easily consumable reporting when combined with a database being fed from the Hadoop environment. Training These links point to online Windows Azure training labs where you can learn more about the individual ingredients described above. Hadoop Learning Resources (20+ tutorials and labs) Huge collection of resources for learning about all aspects of Apache Hadoop-based development on Windows Azure and the Hadoop and Windows Azure Ecosystems SQL Azure (7 labs) Microsoft SQL Azure delivers on the Microsoft Data Platform vision of extending the SQL Server capabilities to the cloud as web-based services, enabling you to store structured, semi-structured, and unstructured data. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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  • Is there something better than a StringBuilder for big blocks of SQL in the code

    - by Eduardo Molteni
    I'm just tired of making a big SQL statement, test it, and then paste the SQL into the code and adding all the sqlstmt.append(" at the beginning and the ") at the end. It's 2011, isn't there a better way the handle a big chunk of strings inside code? Please: don't suggest stored procedures or ORMs. edit Found the answer using XML literals and CData. Thanks to all the people that actually tried to answer the question without questioning me for not using ORM, SPs and using VB edit 2 the question leave me thinking that languages could try to make a better effort for using inline SQL with color syntax, etc. It will be cheaper that developing Linq2SQL. Just something like: dim sql = <sql> SELECT * ... </sql>

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  • Skills to Focus on to land Big 5 Software Engineer Position

    - by Megadeth.Metallica
    Guys, I'm in my penultimate quarter of grad school and have a software engineering internship lined up at a big 5 tech company. I have dabbled a lot recently in Python and am average at Java. I want to prepare myself for coding interviews when I apply for new grad positions at the Big 5 tech companies when I graduate at the end of this year. Since I want to have a good shot at all 5 companies (Amazon,Google,Yahoo,Microsoft and Apple) - Should I focus my time and effort on mastering and improving my Java. Or is my time better spent checking out other languages and tools ( Attracted to RoR, Clojure, Git, C# ) I am planning to spend my spring break implementing all the common algorithms and Data structure out of my algorithms textbook in Java.

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