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  • Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. 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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  • Google Analytics: understanding dimensions and metrics?

    - by flossfan
    If I run a query on the Google Analytics API and set the dimension to ga:pagePathLevel1 and the metric to ga:avgTimeOnPage, I get results like this: { pagePathLevel1: /about, avgTimeOnPage: 28 }, { pagePathLevel1: /contact, avgTimeOnPage: 10 } I'm not completely sure how to interpret this. Is the value of avgTimeOnPage the average time spent by any user on all pages that match that path? Or is 28 seconds the average time spent by any user on any single page that matches that path? I'm looking for the average time spent across all pages matching that path, but the time estimates look shorter than I'd expect. I hope that question makes sense! Please tell me if it doesn't.

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  • Real-time Big Data Analytics is a reality for StubHub with Oracle Advanced Analytics

    - by Mark Hornick
    What can you use for a comprehensive platform for real-time analytics? How can you process big data volumes for near-real-time recommendations and dramatically reduce fraud? Learn in this video what Stubhub achieved with Oracle R Enterprise from the Oracle Advanced Analytics option to Oracle Database, and read more on their story here. Advanced analytics solutions that impact the bottom line of a business are challenging due to the range of skills and individuals involved in realizing such solutions. While we hear a lot about the role of the data scientist, that role is but one piece of the puzzle. Advanced analytics solutions also have an operationalization aspect that also requires close proximity to where the transactional activity occurs. The data scientist needs access to the right data with which to model the business problem. This involves IT for data collection, management, and administration, as well as ensuring zero downtime (a website needs to be up 24x7). This also involves working with the data scientist to keep predictive models refreshed with the latest scripts. Integrating advanced analytics solutions into enterprise apps involves not just generating predictions, but supporting the whole life-cycle from data collection, to model building, model assessment, and then outcome assessment and feedback to the model building process again. Application and web interface designers need to take into account how end users will see and use the advanced analytics results, e.g., supporting operations staff that need to handle the potentially fraudulent transactions. As just described, advanced analytics projects can be "complicated" from just a human perspective. The extent to which software can simplify the interactions among users and systems will increase the likelihood of project success. The ability to quickly operationalize advanced analytics projects and demonstrate measurable value, means the difference between a successful project and just a nice research report. By standardizing on Oracle Database and SQL invocation of R, along with in-database modeling as found in Oracle Advanced Analytics, expedient model deployment and zero downtime for refreshing models becomes a reality. Meanwhile, data scientists are also able to explore leading edge techniques available in open source. The Oracle solution propels the entire organization forward to realize the value of advanced analytics.

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  • Using RegEx's in Multi-Channel Funnels in Google Analytics

    - by Rob H
    For some reason, I can't get my multi-channel funnel which utilizes RegEx's in the path steps to function -- it keeps coming back with no data. There are a few variables which may be holding things up, but I can't figure out the origin of the problem, nor a solution. Here's the situation: The funnel is tracking conversions, defined as when a user completes 4 steps to signup Steps are not "required" Default URL is set to https://example.com There is a 302 redirect set up on our site that leads from http://example.com to https://example.com Within the funnel, steps switch from non-secure pages (unless browser is set to secure browsing), to secure pages once the user moves from the landing page to the second page of the sign-up process (account placeholder has been created) URL at that point contains the variable of publisher number within (but not at the end) the URL My RegEx's are all properly written as tested on rubular.com

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  • Data Quality Through Data Governance

    Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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  • Willy Rotstein on Analytics and Social Media in Retail

    - by sarah.taylor(at)oracle.com
    Recently I came across a presentation from Dan Zarrella on "The Science of Retweets. (http://www.slideshare.net/HubSpot/the-science-of-retweets-with-dan-zarrella). It is an insightful, fact-based analysis of how tweets propagate and what makes them successful. The analysis is of course very interesting for those of us interested Tweeting. However, what really caught my attention is how well it illustrates, form a very different angle, some of the issues I am discussing with retailers these days. In particular the opportunities that e-commerce and social media open to those retailers with the appetite and vision to tackle the associated analytical challenges. And these challenges are of course not straightforward.   In his presentation Dan introduces the concept of Observability, I haven't had the opportunity to discuss with Dan his specific definition for the term. However, in practical retail terms, I would say that it means that through social media (and other web channels such as search) we can analyze and track processes by measuring Indicators that were not measurable before. The focus is in identifying patterns across a large number of consumers rather than what a particular individual "Likes".   The potential impact for retailers is huge. It opens the opportunity to monitor changes in consumer preference  and plan the business accordingly. And you can do this almost "real time" rather than through infrequent surveys that provide a "rear view" picture of your consumer behaviour. For instance, you could envision identifying when a particular set of fashion styles are breaking out from the pack, and commit a re-buy. Or you could monitor when the preference for a specific mobile device has declined and hence markdowns should be considered; or how demand for a specific ready-made food typically flows across regions and manage the inventory accordingly. Search, blogging, website and store data may need to be considered in identifying these trends. The data volumes involved are huge (check Andrea Morgan's recent post on "Big Data" in retail) but so are the benefits. As Andrea says, for the first time we can start getting insight into "Why" the business is performing in a certain way rather than just reporting on what is happening. And it is not just about the data volumes. Tackling the challenge also calls for integrated planning systems that can bring data and insight into the context of the Decision Making process Buyers, Merchandisers and Supply Chain managers are following. I strongly believe that only when data and process come together you can move from the anecdotal to systematically improving business performance.   I would love to hear your opinions on these trends and where you think Retail is heading to exploit these topics - please email me: [email protected]

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  • Google Analytics Funnel Step Regular Expression Not Working

    - by scoarescoare
    The first step in a funnel is going to have a dynamic ending fragment. Examples: http://mysite.com/invite/tickle-party http://mysite.com/invite/pajama-party http://mysite.com/invite/puppy-party To allow for such dynamism, I provided this url for step one: \invite(.*) My goals work but the funnel visualization report shows 0 for everything. I know this problem is due to the regex in the funnel step because I copied this entire goal except I replaced \invite(.*) with /invite/puppy-party When I hardcoded /invite/puppy-party the funnel worked as expected. Why is my funnel report not working with my original funnel step url parameter?

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  • Issue tracking multiple domains with Google Analytics

    - by user359650
    I have 2 domains mydomain.com and mydomain.net which I'm trying to track with the same GA code. Here are the options I turned on: Subdomains of mydomain ON Examples: www.mydomain.com -and- apps.mydomain.com -and- store.mydomain.com Multiple top-level domains of mydomain ON Examples: mydomain.uk -and- mydomain.cn -and- mydomain.fr Which gave me the following code: _gaq.push(['_setAccount', 'UA-123456789-1']); _gaq.push(['_setDomainName', 'mydomain.com']); _gaq.push(['_setAllowLinker', true]); _gaq.push(['_trackPageview']); In this help page I read that _setDomainName must be changed for each domain which I did: -if you go to mydomain.net you get _gaq.push(['_setDomainName', 'mydomain.net']); -if you go to mydomain.com you get _gaq.push(['_setDomainName', 'mydomain.com']); When I generate traffic on both mydomain.dom and mydomain.net and watches GA push requests made with firebug I can see requests generated for both domains and the parameter called utmhn has the proper domain value (which matches that of _setDomainName and the browser address bar). However when I monitor the realtime statistics under Home->Real-Time->Overview I see pageviews for mydomain.net BUT NOT for mydomain.dom :( What am I missing to properly track both domains? PS: in the help page I mentioned they talk about setting up cross links which I didn't do for now as my understanding is that it shouldn't be needed to get what I'm trying to do to work. Also I want to mention that I do not have any tracking code for any of these 2 domains other than the one I mentioned.

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  • Help Creating a Google Analytics Funnel for Check out process

    - by Drew
    have a funnel question. I am currently working on tracking (through GA) guest and logged in member activity once they get to my sites shopping cart. But need help with setting up funnels. Specifically to see; Total sales Logged in member total sales List item Guest member sales The urls associated to the check out proces are: Logged in members /cart (arriving to checkout) /checkout (checking out as a logged in member) /checkout/confirmation (thank you - confirmed sale) Guest members - /cart (arriving to checkout) - /checkout-guest (checking out as a guest) - /checkout/confirmation (thanks you - confirmed sale) I've tested the funnels set up for the above with 9 transactions. But the end maths doesn't seem to line up. Total sales funnel shows 9 completed transactions when only tracking these to urls: - /cart - /checkout/confirmation Which is great - cause it's working Logged in member sales show a total of 9 completed transactions based on each step of the logged in url steps (above) being tracked in a funnel. Not good because this number should be 3. Guest check out funnel (see guest steps above) shows 9 as well. What the?!?!?!? The results I am looking for should reflect the following - total sales = 9, logged in members = 3, guest members = 6 Is there any way to set these urls up so that the funnels report the correct results - or do I need to changed the urls and provide logged in members and guest stand alone purchase confirmation pages (this would mean I can not track total sales which combine results from both streams)? Any knowledge in this area is welcome. Thanks.

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  • Google Analytics Visitors drop-off for certain region of site only

    - by crmpicco
    I have an issue with the tracking on my site where I have seen a dramatic drop off of visitors to the site from a certain region. I have four regions on my site at the moment, these are UK, EU, US and RoW (Rest of the World). The UK, EU and US regions are unaffected, only the RoW region suffers this drop-off. I have included a screen shot below from my GA account, which shows this effect. My GA code, which is included on every page on the site is below. I have changed the UA account number intentionally for this example. There have been no changes made to the GA account or the tracking code in a live environment for some considerable time, but for some reason I am seeing the drop-off for this region only. In the code below I am not tracking page views on certain pages as I have event tracking setup for these pages. <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-18721873-5']); _gaq.push(['_setCookiePath', '/row/']); if ( typeof(p_page) != 'undefined') { // do nothing if user is on above pages // N.B. there are a series of conditions in this if statement checking that we are not on a particular page } else { _gaq.push(['_trackPageview']); } </script>

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  • Google Analytics - include filter not working

    - by gerl
    I just added an include filter this morning in my domain (test.org). I have: Custom Filter Include Request URI ^/test-a/46212$|^/test-a/46212|^/test-a/46315 Now after I go to Content Site Content All Pages, I see stats for other pages that I didn't include in my filter. For example I see /somethingelse. I only want to see stats for /test-a/46212 and whatever else in my filter. Please let me know what I'm doing wrong.

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  • SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

    - by pinaldave
    There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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  • Site too large to officially use Google Analytics?

    - by Jeff Atwood
    We just got this email from the Google Analytics team: We love that you love our product and use it as much as you do. We have observed however, that a website you are tracking with Google Analytics is sending over 1 million hits per day to Google Analytics servers. This is well above the "5 million pageviews per month per account" limit specified in the Google Analytics Terms of Service. Processing this amount of data multiple times a day takes up valuable resources that enable us to continue to develop the product for all Google Analytics users. Processing this amount of data multiple times a day takes up valuable resources that enable us to continue to develop the product for all Google Analytics users. As such, starting August 23rd, 2010, the metrics in your reports will be updated once a day, as opposed to multiple times during the course of the day. You will continue to receive all the reports and features in Google Analytics as usual. The only change will be that data for a given day will appear the following day. We trust you understand the reasons for this change. I totally respect this decision, and I think it's very generous to not kick us out. But how do we do this the right way -- what's the official, blessed Google way to use Google Analytics if you're a "whale" website with lots of hits per day? Or, are there other analytics services that would be more appropriate for very large websites?

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  • Can we use both Google Analytics (Asynchronous) and Google Analytics with Display Advertising code in same page

    - by Gadde
    I have Google Analytics (Asynchronous) script <script type=”text/javascript”> _gaq.push(['_setAccount', 'UA-XXXXX-X']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script> and Google Analytics with Display Advertising Script <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-XXXXX-X-yz']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://' : 'http://') + 'stats.g.doubleclick.net/dc.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script> The UA - codes are different can i use both the codes ? I've read some where that Universal Analytics will not interfere with previous versions of Google Analytics. If i have upgraded to Universal Analytics, If the UA - codes are different should i use only the Universal Analytics script or should i use both Universal Analytics script and Universal Analytics script. please advise.....

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  • Demographics and interests reports in Google universal analytics

    - by Dyf
    I have an issue with Google Analytics, can't seem to find a guide how to activate Demographics and Interests reports. I am using the new version of analytics (Universal Analytics) and the script code looks something like this: (function (i, s, o, g, r, a, m) { i['GoogleAnalyticsObject'] = r; i[r] = i[r] || function () { (i[r].q = i[r].q || []).push(arguments) }, i[r].l = 1 * new Date(); a = s.createElement(o), m = s.getElementsByTagName(o)[0]; a.async = 1; a.src = g; m.parentNode.insertBefore(a, m) })(window, document, 'script', '//www.google-analytics.com/analytics.js', 'ga'); But the guide I found on google's page is for the old version (Classis Analytics). Does anyone know how I can activate this on Universal Analytics? Is this even possible?

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  • Social Analytics in your current data

    - by Dan McGrath
    By now everyone is aware of the massive boom in social-networking (Twitter, Facebook, LinkedIn) and obviously a big part of its business model revolves around being able to mine this data to create information that can be used to make money for someone. Gartner has identified 'Social Analytics' as one of the top 10 strategic technologies for 2011. Has anyone looked at their existing data structures to determine if they could extract a social graph and then perform further data mining against this? How does it fit in with your other strategic development strategies? What information are you trying to extract from the data? Take for example, a bank. They could conceivably determine a social graph through account relationships and transactions. Obviously there would be open edges on the graph where funds enter/leave the institute, but that shouldn't detract from the usefulness of the data. I'm looking for actual examples with the answers, as well as why/how they did it. References to other sites will be greatly appreciated. Note: I'm not at all referring to mining data out of actual social networks.

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  • Sales tracker that allows complex queries?

    - by feklee
    On a site, every click on a product should be registered by a sales tracker: price, type, etc. The sales tracker should provide an API so that complex queries can be performed, such as: Which products of a type "teapot" had a price below 20 EUR? Requirements: Recorded data should be available for querying no later than two hours after it has been recorded. For example, there are reports that Google Analytics may take up to 24h to update data. That is not acceptable. Querying doesn't need to be fast, but recording does (of course). Which sales tracker allows complex queries against collected data?

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  • How can I get stats for what 3rd-party sites have embedded our iframe widget?

    - by Su'
    Say we've produced a widget for other sites to use, like so: <iframe src="http://example.com/whatever.php" frameBorder="0" width="200px" height="300px" scrolling="no"></iframe> The client would like to be able to see within GA who has embedded the thing. Is there some referer information automatically passed that I can look for, or do I need to add something? whatever.php is already loading the analytics Javascript(we're also tracking clicks on an outbound link). [EDIT] Looking around a bit more, I found what seems to be a similar question on SO with an answer saying this can be found, automatically, but I still can't seem to find the information. The question's also old enough the respondent is probably referring to the old interface, though. Maybe someone could explain getting to it in the new look. (I won't likely be able to train this client to switch, deal with the old look, etc.)

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  • Oracle Financial Analytics for SAP Certified with Oracle Data Integrator EE

    - by denis.gray
    Two days ago Oracle announced the release of Oracle Financial Analytics for SAP.  With the amount of press this has garnered in the past two days, there's a key detail that can't be missed.  This release is certified with Oracle Data Integrator EE - now making the combination of Data Integration and Business Intelligence a force to contend with.  Within the Oracle Press Release there were two important bullets: ·         Oracle Financial Analytics for SAP includes a pre-packaged ABAP code compliant adapter and is certified with Oracle Data Integrator Enterprise Edition to integrate SAP Financial Accounting data directly with the analytic application.  ·         Helping to integrate SAP financial data and disparate third-party data sources is Oracle Data Integrator Enterprise Edition which delivers fast, efficient loading and transformation of timely data into a data warehouse environment through its high-performance Extract Load and Transform (E-LT) technology. This is very exciting news, demonstrating Oracle's overall commitment to Oracle Data Integrator EE.   This is a great way to start off the new year and we look forward to building on this momentum throughout 2011.   The following links contain additional information and media responses about the Oracle Financial Analytics for SAP release. IDG News Service (Also appeared in PC World, Computer World, CIO: "Oracle is moving further into rival SAP's turf with Oracle Financial Analytics for SAP, a new BI (business intelligence) application that can crunch ERP (enterprise resource planning) system financial data for insights." Information Week: "Oracle talks a good game about the appeal of an optimized, all-Oracle stack. But the company also recognizes that we live in a predominantly heterogeneous IT world" CRN: "While some businesses with SAP Financial Accounting already use Oracle BI, those integrations had to be custom developed. The new offering provides pre-built integration capabilities." ECRM Guide:  "Among other features, Oracle Financial Analytics for SAP helps front-line managers improve financial performance and decision-making with what the company says is comprehensive, timely and role-based information on their departments' expenses and revenue contributions."   SAP Getting Started Guide for ODI on OTN: http://www.oracle.com/technetwork/middleware/data-integrator/learnmore/index.html For more information on the ODI and its SAP connectivity please review the Oracle® Fusion Middleware Application Adapters Guide for Oracle Data Integrator11g Release 1 (11.1.1)

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  • Classic vs universal Google analytics and loss of historical data

    - by iss42
    I'm keen to use some of the new features in Google Universal Analytics. I have an old site though that I don't want to lose the historical data for. The comparisons with historical data are interesting for example. However Google doesn't appear to allow you to change a property from the classic code to the new code. Am I missing something? I'm surprised this isn't a bigger issue for many other users.

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  • google analytics statistics

    - by colmcq
    I am compiling a report for a client using google analytics. I have observed that the client has unusually good page view times (5 mins) and excellent bounce rates (<25%). I need to reference research data that validates my assertion that these figures are excellent compared to an industry standard (the industry is ecommerce and gaming). Can you direct me to any published research data that specifies normal bounce rates and page view times for this industry?

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  • Big Data Appliance X4-2 Release Announcement

    - by Jean-Pierre Dijcks
    Today we are announcing the release of the 3rd generation Big Data Appliance. Read the Press Release here. Software Focus The focus for this 3rd generation of Big Data Appliance is: Comprehensive and Open - Big Data Appliance now includes all Cloudera Software, including Back-up and Disaster Recovery (BDR), Search, Impala, Navigator as well as the previously included components (like CDH, HBase and Cloudera Manager) and Oracle NoSQL Database (CE or EE). Lower TCO then DIY Hadoop Systems Simplified Operations while providing an open platform for the organization Comprehensive security including the new Audit Vault and Database Firewall software, Apache Sentry and Kerberos configured out-of-the-box Hardware Update A good place to start is to quickly review the hardware differences (no price changes!). On a per node basis the following is a comparison between old and new (X3-2) hardware: Big Data Appliance X3-2 Big Data Appliance X4-2 CPU 2 x 8-Core Intel® Xeon® E5-2660 (2.2 GHz) 2 x 8-Core Intel® Xeon® E5-2650 V2 (2.6 GHz) Memory 64GB 64GB Disk 12 x 3TB High Capacity SAS 12 x 4TB High Capacity SAS InfiniBand 40Gb/sec 40Gb/sec Ethernet 10Gb/sec 10Gb/sec For all the details on the environmentals and other useful information, review the data sheet for Big Data Appliance X4-2. The larger disks give BDA X4-2 33% more capacity over the previous generation while adding faster CPUs. Memory for BDA is expandable to 512 GB per node and can be done on a per-node basis, for example for NameNodes or for HBase region servers, or for NoSQL Database nodes. Software Details More details in terms of software and the current versions (note BDA follows a three monthly update cycle for Cloudera and other software): Big Data Appliance 2.2 Software Stack Big Data Appliance 2.3 Software Stack Linux Oracle Linux 5.8 with UEK 1 Oracle Linux 6.4 with UEK 2 JDK JDK 6 JDK 7 Cloudera CDH CDH 4.3 CDH 4.4 Cloudera Manager CM 4.6 CM 4.7 And like we said at the beginning it is important to understand that all other Cloudera components are now included in the price of Oracle Big Data Appliance. They are fully supported by Oracle and available for all BDA customers. For more information: Big Data Appliance Data Sheet Big Data Connectors Data Sheet Oracle NoSQL Database Data Sheet (CE | EE) Oracle Advanced Analytics Data Sheet

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  • SQL SERVER – Introduction to Big Data – Guest Post

    - by pinaldave
    BIG Data – such a big word – everybody talks about this now a days. It is the word in the database world. In one of the conversation I asked my friend Jasjeet Sigh the same question – what is Big Data? He instantly came up with a very effective write-up.  Jasjeet is working as a Technical Manager with Koenig Solutions. He leads the SQL domain, and holds rich IT industry experience. Talking about Koenig, it is a 19 year old IT training company that offers several certification choices. Some of its courses include SharePoint Training, Project Management certifications, Microsoft Trainings, Business Intelligence programs, Web Design and Development courses etc. Big Data, as the name suggests, is about data that is BIG in nature. The data is BIG in terms of size, and it is difficult to manage such enormous data with relational database management systems that are quite popular these days. Big Data is not just about being large in size, it is also about the variety of the data that differs in form or type. Some examples of Big Data are given below : Scientific data related to weather and atmosphere, Genetics etc Data collected by various medical procedures, such as Radiology, CT scan, MRI etc Data related to Global Positioning System Pictures and Videos Radio Frequency Data Data that may vary very rapidly like stock exchange information Apart from difficulties in managing and storing such data, it is difficult to query, analyze and visualize it. The characteristics of Big Data can be defined by four Vs: Volume: It simply means a large volume of data that may span Petabyte, Exabyte and so on. However it also depends organization to organization that what volume of data they consider as Big Data. Variety: As discussed above, Big Data is not limited to relational information or structured Data. It can also include unstructured data like pictures, videos, text, audio etc. Velocity:  Velocity means the speed by which data changes. The higher is the velocity, the more efficient should be the system to capture and analyze the data. Missing any important point may lead to wrong analysis or may even result in loss. Veracity: It has been recently added as the fourth V, and generally means truthfulness or adherence to the truth. In terms of Big Data, it is more of a challenge than a characteristic. It is difficult to ascertain the truth out of the enormous amount of data and the one that has high velocity. There are always chances of having un-precise and uncertain data. It is a challenging task to clean such data before it is analyzed. Big Data can be considered as the next big thing in the IT sector in terms of innovation and development. If appropriate technologies are developed to analyze and use the information, it can be the driving force for almost all industrial segments. These include Retail, Manufacturing, Service, Finance, Healthcare etc. This will help them to automate business decisions, increase productivity, and innovate and develop new products. Thanks Jasjeet Singh for an excellent write up.  Jasjeet Sign is working as a Technical Manager with Koenig Solutions. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Database, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Big Data

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  • Working with Temporal Data in SQL Server

    - by Dejan Sarka
    My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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