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  • core-data relationships and data structure.

    - by Boaz
    What is the right way to build iPhone core data for this SMS like app (with location)? - I want to represent an entity of conversation with "profile1" "profile2" that heritage from a profile entity, and a message entity with: "to" "from" "body" where the "to" and "from" are equal to "profile1" and/or "profile2" in the conversation entity. How can I make such a relationships? is there a better way to represent the data (other structure)? Thanks

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  • Rails migration won't run, no error thrown

    - by kouak
    Here's a simple migration I'd like to run : class AddTimeOfRevisionToBrandWikis < ActiveRecord::Migration def self.up add_column :brand_wikis, :time_of_revision, :datetime end def self.down remove_column :brand_wikis, :time_of_revision end end Here's what I get when I try to run it : $ rake db:migrate (in /Users/kouak/Documents/workspace/wtb) You have 1 pending migrations: 20100404115341 AddTimeOfRevisionToBrandWikis Run "rake db:migrate" to update your database then try again. What's wrong with rake db:migrate ?

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  • What hardware combonation is better one with a i7-720QM processor and GeForce 310M graphics or one w

    - by Mason
    I am looking for a new laptop and the two i am deciding between is an Asus with Intel® Core™ i5-430M processor and NVIDIA GeForce GTS 360M graphics. Or a Toshiba with the Intel® Core™ i7-720QM processor and NVIDIA GeForce 310M graphics. I am looking for a computer to use for college and be able to play games on. I want to know what one I should get they are both the same price.

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  • How to properly set relationships in Core Data when using setValue and data already exists

    - by ern
    Let's say I have two objects: Articles and Categories. For the sake of this example all relevant categories have already been added to the data store. When looping through data that holds edits for articles, there is category relationship information that needs to be saved. I was planning on using the -setValue method in the Article class in order to set the relationships like so: - (void)setValue:(id)value forUndefinedKey:(NSString *)key { if([key isEqualToString:@"categories"]){ NSLog(@"trying to set categories..."); } } The problem is that value isn't a Category, it is just a string (or array of strings) holding the title of a category. I could certainly do a lookup within this method for each category and assign it, but that seems inefficient when processing a whole bunch of articles at once. Another option is to populate an array of all possible categories and just filter, but my question is where to store that array? Should it be a class method on Article? Is there a way to pass in additional data to the -setValue method? Is there another, better option for setting the relationship I'm not thinking of? Thanks for your help.

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  • inserting new relationship data in core-data

    - by michael
    My app will allow users to create a personalised list of events from a large list of events. I have a table view which simply displays these events, tapping on one of them takes the user to the event details view, which has a button "add to my events". In this detailed view I own the original event object, retrieved via an NSFetchedResultsController and passed to the detailed view (via a table cell, the same as the core data recipes sample). I have no trouble retrieving/displaying information from this "event". I am then trying to add it to the list of MyEvents represented by a one to many (inverse) relationship: This code: NSManagedObjectContext *context = [event managedObjectContext]; MyEvents *myEvents = (MyEvents *)[NSEntityDescription insertNewObjectForEntityForName:@"MyEvents" inManagedObjectContext:context]; [myEvents addEventObject:event];//ERROR And this code (suggested below): //would this add to or overwrite the "list" i am attempting to maintain NSManagedObjectContext *context = [event managedObjectContext]; MyEvents *myEvents = (MyEvents *)[NSEntityDescription insertNewObjectForEntityForName:@"MyEvents" inManagedObjectContext:context]; NSMutableSet *myEvent = [myEvents mutableSetValueForKey:@"event"]; [myEvent addObject:event]; //ERROR Bot produce (at the line indicated by //ERROR): *** -[NSComparisonPredicate evaluateWithObject:]: message sent to deallocated instance Seems I may have missed something fundamental. I cant glean any more information through the use of debugging tools, with my knowledge of them. 1) Is this a valid way to compile and store an editable list like this? 2) Is there a better way? 3) What could possibly be the deallocated instance in error? -- I have now modified the Event entity to have a to-many relationship called "myEvents" which referrers to itself. I can add Events to this fine, and logging the object shows the correct memory addresses appearing for the relationship after a [event addMyEventObject:event];. The same failure happens right after this however. I am still at a loss to understand what is going wrong. This is the backtrace #0 0x01f753a7 in ___forwarding___ () #1 0x01f516c2 in __forwarding_prep_0___ () #2 0x01c5aa8f in -[NSFetchedResultsController(PrivateMethods) _preprocessUpdatedObjects:insertsInfo:deletesInfo:updatesInfo:sectionsWithDeletes:newSectionNames:treatAsRefreshes:] () #3 0x01c5d63b in -[NSFetchedResultsController(PrivateMethods) _managedObjectContextDidChange:] () #4 0x0002e63a in _nsnote_callback () #5 0x01f40005 in _CFXNotificationPostNotification () #6 0x0002bef0 in -[NSNotificationCenter postNotificationName:object:userInfo:] () #7 0x01bbe17d in -[NSManagedObjectContext(_NSInternalNotificationHandling) _postObjectsDidChangeNotificationWithUserInfo:] () #8 0x01c1d763 in -[NSManagedObjectContext(_NSInternalChangeProcessing) _createAndPostChangeNotification:withDeletions:withUpdates:withRefreshes:] () #9 0x01ba25ea in -[NSManagedObjectContext(_NSInternalChangeProcessing) _processRecentChanges:] () #10 0x01bdfb3a in -[NSManagedObjectContext processPendingChanges] () #11 0x01bd0957 in _performRunLoopAction () #12 0x01f4d252 in __CFRunLoopDoObservers () #13 0x01f4c65f in CFRunLoopRunSpecific () #14 0x01f4bc48 in CFRunLoopRunInMode () #15 0x0273878d in GSEventRunModal () #16 0x02738852 in GSEventRun () #17 0x002ba003 in UIApplicationMain () solution I managed to get to the bottom of this. I was fetching the event in question using a NSFetchedResultsController with a NSPredicate which I was releasing after I had the results. Retrieving values from the entities returned was no problem, but when I tried to update any of them it gave the error above. It should not have been released. oustanding part of my question What is a good way to create this sub list from a list of existing items in terms of a core data model. I don't believe its any of the ways I tried here. I need to show/edit it in another table view. Perhaps there is a better way than a boolean property on each event entity? The relationship idea above doesn't seem to work here (even though I can now create it). Cheers.

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  • 3d Studio Max and 2+CPUs - Core limit ?

    - by FreekOne
    Hi guys, I am scouting for parts to put in a new machine, and in the process, while looking at different benchmarks I stumbled upon this benchmark and it got me a bit worried. Quote form it: Noticably absent from this review is an old-time favorite, 3ds Max. I did attempt to run our custom 3ds Max benchmark on both the 2009 and 2010 versions of the software, but the application would simply not load on the Westmere box with hyper-threading enabled. Evidently Autodesk didn't plan far enough ahead to write their software for more than 16 threads. Once there is an update that addresses this issue, I will happily add 3ds Max back into the benchmarking mix. Since I was looking at dual hexa-core Xeons (x5650), that would put my future machine at 24 logical cores which (duh) is well over 16 cores and since I'm mostly building this for 3DS Max work, you can see how this would seriously spoil my plans. I tried looking for additional information on this potential issue, but the above article seems to be the only one who mentions it. Could anyone who has access to a 16 core machine or an in-depth knowledge about 3DS Max please confirm this ? Any help would be much appreciated !

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  • SQL Developer Debugging, Watches, Smart Data, & Data

    - by thatjeffsmith
    After presenting the SQL Developer PL/SQL debugger for about an hour yesterday at KScope12 in San Antonio, my boss came up and asked, “Now, would you really want to know what the Smart Data panel does?” Apparently I had ‘made up’ my own story about what that panel’s intent is based on my experience with it. Not good Jeff, not good. It was a very small point of my presentation, but I probably should have read the docs. The Smart Data tab displays information about variables, using your Debugger: Smart Data preferences. You can also specify these preferences by right-clicking in the Smart Data window and selecting Preferences. Debugger Smart Data Preferences, control number of variables to display The Smart Data panel auto-inspects the last X accessed variables. So if you have a program with 26 variables, instead of showing you all 26, it will just show you the last two variables that were referenced in your program. If you were to click on the ‘Data’ debug panel, you’ll see EVERYTHING. And if you only want to see a very specific set of values, then you should use Watches. The Smart Data Panel As I step through the code, the variables being tracked change as they are referenced. Only the most recent ones display. This is controlled by the ‘Maximum Locations to Remember’ preference. Step through the code, see the latest variables accessed The Data Panel All variables are displayed. Might be information overload on large PL/SQL programs where you have many dozens or even hundreds of variables to track. Shows everything all the time Watches Watches are added manually and only show what you ask for. Data on Demand – add a watch to track a specific variable Remember, you can interact with your data If you want to do more than just watch, you can mouse-right on a data element, and change the value of the variable as the program is running. This is one of the primary benefits to debugging over using DBMS_OUTPUT to track what’s happening in your program. Change the values while the program is running to test your ‘What if?’ scenarios

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  • SQL – Biggest Concerns in a Data-Driven World

    - by Pinal Dave
    The ongoing chaos over Government Agency’s snooping has ignited a heated debate on privacy of personal data and its use by government and/or other institutions. It has created a feeling of disapproval and distrust among users. This incident proves to be a lesson for companies that are looking to leverage their business using a data driven approach. According to analysts, the goal of gathering personal information should be to deliver benefits to both the parties – the user as well as the data collector(government or business). Using data the right way is crucial, and companies need to deploy the right software applications and systems to ensure that their efforts are well-directed. However, there are various issues plaguing analysts regarding available software, which are highlighted below. According to a InformationWeek 2013 Survey of Analytics, Business Intelligence and Information Management where 541 business technology professionals contributed as respondents, it was discovered that the biggest concern was deemed to be the scarcity of expertise and high costs associated with the same. This concern was voiced by as many as 38% of the participants. A close second came out to be the issue of data warehouse appliance platforms being expensive, with 33% of those present believing it to be a huge roadblock. Another revelation made in this respect was that 31% professionals weren’t even sure how Data Analytics can create business opportunities for them. Another 17% shared that they found data platform technologies such as Hadoop and NoSQL technologies hard to learn. These results clearly pointed out that there are awareness and expertise issues that also need much attention. Unless the demand-supply gap of Business Intelligence professionals well versed in data analysis technologies is met, this divide is going to affect how companies make the most of their BI campaigns. One of the key action points that can be taken to salvage the situation, is to provide training on Data Analytics concepts. Koenig Solutions offer courses on many such technologies including a course on MCSE SQL Server 2012: BI Platform. So it’s time to brush up your skills and get down to work in a data driven world that awaits you ahead. 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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  • Core Data grouping data in table

    - by OscarTheGrouch
    I am using core data trying to create a simple database app, I have an entity called "Game" which has a "creator". I have basically used the iPhone table view template and replaced the names. I have the games listed by creator. Currently the tableview looks like this... Chris Ryder Chris Ryder Chris Ryder Chris Ryder Dan Grimaldi Dan Grimaldi Dan Grimaldi Scott Ricardo Tim Thermos Tim Thermos I am trying to group the tableview, so that each creator has only one cell in the tableview and is listed once and only once like this... Chris Ryder Dan Grimaldi Scott Ricardo Tim Thermos any help or suggestions would be greatly appreciated.

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  • Where can I find free and open data?

    - by kitsune
    Sooner or later, coders will feel the need to have access to "open data" in one of their projects, from knowing a city's zip to a more obscure information such as the axial tilt of Pluto. I know data.un.org which offers access to the UN's extensive array of databases that deal with human development and other socio-economic issues. The other usual suspects are NASA and the USGS for planetary data. There's an article at readwriteweb with more links. infochimps.org seems to stand out. Personally, I need to find historic commodity prices, stock values and other financial data. All these data sets seem to cost money however. Clarification To clarify, I'm interested in all kinds of open data, because sooner or later, I know I will be in a situation where I could need it. I will try to edit this answer and include the suggestions in a structured manners. A link for financial data was hidden in that readwriteweb article, doh! It's called opentick.com. Looks good so far! Update I stumbled over semantic data in another question of mine on here. There is opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). A project called UMBEL provides a light-weight, distilled version of opencyc. Umbel has semantic data in rdf/owl/skos n3 syntax. The Worldbank also released a very nice API. It offers data from the last 50 years for about 200 countries

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  • Insert new relationship data in core data

    - by michael
    Hoping someone can shed some light on what I might be doing wrong here. Trying to add an "event" to a list of events, represented by a one to many (inverse) relationship: MyEvents <--- Event This is the code: MyEvents *myEvents = (MyEvents *)[NSEntityDescription insertNewObjectForEntityForName:@"MyEvents" inManagedObjectContext:context]; NSLog(@"MYEVENTS: %@", myEvents); NSLog(@"EVENT: %@", event); [myEvents addEventObject:event]; My context is fine, and both the myEvents and event print perfectly valid information. When I try to add the event that I have (which is passed into this view controller, having been retrieved from core data previously) with this code [myEvents addEventObject:event]; It falls over with *** -[NSComparisonPredicate evaluateWithObject:]: message sent to deallocated instance MyEvents and Event are just the default generated code. MyEvents contains only the relationship to event. Thanks.

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  • Temporary storage for keeping data between program iterations?

    - by mr.b
    I am working on an application that works like this: It fetches data from many sources, resulting in pool of about 500,000-1,500,000 records (depends on time/day) Data is parsed Part of data is processed in a way to compare it to pre-existing data (read from database), calculations are made, and stored in database. Resulting dataset that has to be stored in database is, however, much smaller in size (compared to original data set), and ranges from 5,000-50,000 records. This process almost always updates existing data, perhaps adds few more records. Then, data from step 2 should be kept somehow, somewhere, so that next time data is fetched, there is a data set which can be used to perform calculations, without touching pre-existing data in database. I should point out that this data can be lost, it's not irreplaceable (key information can be read from database if needed), but it would speed up the process next time. Application components can (and will be) run off different computers (in the same network), so storage has to be reachable from multiple hosts. I have considered using memcached, but I'm not quite sure should I do so, because one record is usually no smaller than 200 bytes, and if I have 1,500,000 records, I guess that it would amount to over 300 MB of memcached cache... But that doesn't seem scalable to me - what if data was 5x that amount? If it were to consume 1-2 GB of cache only to keep data in between iterations (which could easily happen)? So, the question is: which temporary storage mechanism would be most suitable for this kind of processing? I haven't considered using mysql temporary tables, as I'm not sure if they can persist between sessions, and be used by other hosts in network... Any other suggestion? Something I should consider?

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  • Why are data structures so important in interviews?

    - by Vamsi Emani
    I am a newbie into the corporate world recently graduated in computers. I am a java/groovy developer. I am a quick learner and I can learn new frameworks, APIs or even programming languages within considerably short amount of time. Albeit that, I must confess that I was not so strong in data structures when I graduated out of college. Through out the campus placements during my graduation, I've witnessed that most of the biggie tech companies like Amazon, Microsoft etc focused mainly on data structures. It appears as if data structures is the only thing that they expect from a graduate. Adding to this, I see that there is this general perspective that a good programmer is necessarily a one with good knowledge about data structures. To be honest, I felt bad about that. I write good code. I follow standard design patterns of coding, I do use data structures but at the superficial level as in java exposed APIs like ArrayLists, LinkedLists etc. But the companies usually focused on the intricate aspects of Data Structures like pointer based memory manipulation and time complexities. Probably because of my java-ish background, Back then, I understood code efficiency and logic only when talked in terms of Object Oriented Programming like Objects, instances, etc but I never drilled down into the level of bits and bytes. I did not want people to look down upon me for this knowledge deficit of mine in Data Structures. So really why all this emphasis on Data Structures? Does, Not having knowledge in Data Structures really effect one's career in programming? Or is the knowledge in this subject really a sufficient basis to differentiate a good and a bad programmer?

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  • Data structure for pattern matching.

    - by alvonellos
    Let's say you have an input file with many entries like these: date, ticker, open, high, low, close, <and some other values> And you want to execute a pattern matching routine on the entries(rows) in that file, using a candlestick pattern, for example. (See, Doji) And that pattern can appear on any uniform time interval (let t = 1s, 5s, 10s, 1d, 7d, 2w, 2y, and so on...). Say a pattern matching routine can take an arbitrary number of rows to perform an analysis and contain an arbitrary number of subpatterns. In other words, some patterns may require 4 entries to operate on. Say also that the routine (may) later have to find and classify extrema (local and global maxima and minima as well as inflection points) for the ticker over a closed interval, for example, you could say that a cubic function (x^3) has the extrema on the interval [-1, 1]. (See link) What would be the most natural choice in terms of a data structure? What about an interface that conforms a Ticker object containing one row of data to a collection of Ticker so that an arbitrary pattern can be applied to the data. What's the first thing that comes to mind? I chose a doubly-linked circular linked list that has the following methods: push_front() push_back() pop_front() pop_back() [] //overloaded, can be used with negative parameters But that data structure seems very clumsy, since so much pushing and popping is going on, I have to make a deep copy of the data structure before running an analysis on it. So, I don't know if I made my question very clear -- but the main points are: What kind of data structures should be considered when analyzing sequential data points to conform to a pattern that does NOT require random access? What kind of data structures should be considered when classifying extrema of a set of data points?

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  • Database migration pattern for Java?

    - by Eno
    Im working on some database migration code in Java. Im also using a factory pattern so I can use different kinds of databases. And each kind of database im using implements a common interface. What I would like to do is have a migration check that is internal to the class and runs some database schema update code automatically. The actual update is pretty straight forward (I check schema version in a table and compare against a constant in my app to decide whether to migrate or not and between which versions of schema). To make this automatic I was thinking the test should live inside (or be called from) the constructor. OK, fair enough, that's simple enough. My problem is that I dont want the test to run every single time I instantiate a database object (it runs a query so having it run on every construction is not efficient). So maybe this should be a class static method? I guess my question is, what is a good design pattern for this type of problem? There ought to be a clean way to ensure the migration test runs only once OR is super-efficient.

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  • Database not completely updated in rails migration

    - by Aatish Sai
    I am new to Ruby on Rails. I have a migration called create user class CreateUsers < ActiveRecord::Migration def change create_table :users do |t| t.column :username, :string, :limit => 25, :default => "", :null => false t.column :hashed_password, :string, :limit => 40, :default => "", :null => false t.column :first_name, :string, :limit => 25, :default => "", :null => false t.column :last_name, :string, :limit => 40, :default => "", :null => false t.column :email, :string, :limit => 50, :default => "", :null => false t.column :display_name, :string, :limit => 25, :default => "", :null => false t.column :user_level, :integer, :limit => 3, :default => 0, :null => false end User.create(:username=>'test',:hashed_password=>'test',:first_name=>'test',:last_name=>'test',:email=>'[email protected]',:display_name=> 'test',:user_level=>9) end end When I run rake db:migrate the table is created with the columns as mentioned above but the test data are not there mysql>select * from users; Empty set (0.00 sec) EDIT I just dropped the whole database and restarted the migration and now it is showing the following error. rake aborted! An error has occurred, all later migrations canceled: Can't mass-assign protected attributes: username, hashed_password, first_name, last_name, email, display_name, user_level What am I doing wrong please help? Thank you.

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  • Five Key Strategies in Master Data Management

    - by david.butler(at)oracle.com
    Here is a very interesting Profit Magazine article on MDM: A recent customer survey reveals the deleterious effects of data fragmentation. by Trevor Naidoo, December 2010   Across industries and geographies, IT organizations have grown in complexity, whether due to mergers and acquisitions, or decentralized systems supporting functional or departmental requirements. With systems architected over time to support unique, one-off process needs, they are becoming costly to maintain, and the Internet has only further added to the complexity. Data fragmentation has become a key inhibitor in delivering flexible, user-friendly systems. The Oracle Insight team conducted a survey assessing customers' master data management (MDM) capabilities over the past two years to get a sense of where they are in terms of their capabilities. The responses, by 27 respondents from six different industries, reveal five key areas in which customers need to improve their data management in order to get better financial results. 1. Less than 15 percent of organizations surveyed understand the sources and quality of their master data, and have a roadmap to address missing data domains. Examples of the types of master data domains referred to are customer, supplier, product, financial and site. Many organizations have multiple sources of master data with varying degrees of data quality in each source -- customer data stored in the customer relationship management system is inconsistent with customer data stored in the order management system. Imagine not knowing how many places you stored your customer information, and whether a customer's address was the most up to date in each source. In fact, more than 55 percent of the respondents in the survey manage their data quality on an ad-hoc basis. It is important for organizations to document their inventory of data sources and then profile these data sources to ensure that there is a consistent definition of key data entities throughout the organization. Some questions to ask are: How do we define a customer? What is a product? How do we define a site? The goal is to strive for one common repository for master data that acts as a cross reference for all other sources and ensures consistent, high-quality master data throughout the organization. 2. Only 18 percent of respondents have an enterprise data management strategy to ensure that data is treated as an asset to the organization. Most respondents handle data at the department or functional level and do not have an enterprise view of their master data. The sales department may track all their interactions with customers as they move through the sales cycle, the service department is tracking their interactions with the same customers independently, and the finance department also has a different perspective on the same customer. The salesperson may not be aware that the customer she is trying to sell to is experiencing issues with existing products purchased, or that the customer is behind on previous invoices. The lack of a data strategy makes it difficult for business users to turn data into information via reports. Without the key building blocks in place, it is difficult to create key linkages between customer, product, site, supplier and financial data. These linkages make it possible to understand patterns. A well-defined data management strategy is aligned to the business strategy and helps create the governance needed to ensure that data stewardship is in place and data integrity is intact. 3. Almost 60 percent of respondents have no strategy to integrate data across operational applications. Many respondents have several disparate sources of data with no strategy to keep them in sync with each other. Even though there is no clear strategy to integrate the data (see #2 above), the data needs to be synced and cross-referenced to keep the business processes running. About 55 percent of respondents said they perform this integration on an ad hoc basis, and in many cases, it is done manually with the help of Microsoft Excel spreadsheets. For example, a salesperson needs a report on global sales for a specific product, but the product has different product numbers in different countries. Typically, an analyst will pull all the data into Excel, manually create a cross reference for that product, and then aggregate the sales. The exact same procedure has to be followed if the same report is needed the following month. A well-defined consolidation strategy will ensure that a central cross-reference is maintained with updates in any one application being propagated to all the other systems, so that data is synchronized and up to date. This can be done in real time or in batch mode using integration technology. 4. Approximately 50 percent of respondents spend manual efforts cleansing and normalizing data. Information stored in various systems usually follows different standards and formats, making it difficult to match the data. A customer's address can be stored in different ways using a variety of abbreviations -- for example, "av" or "ave" for avenue. Similarly, a product's attributes can be stored in a number of different ways; for example, a size attribute can be stored in inches and can also be entered as "'' ". These types of variations make it difficult to match up data from different sources. Today, most customers rely on manual, heroic efforts to match, cleanse, and de-duplicate data -- clearly not a scalable, sustainable model. To solve this challenge, organizations need the ability to standardize data for customers, products, sites, suppliers and financial accounts; however, less than 10 percent of respondents have technology in place to automatically resolve duplicates. It is no wonder, therefore, that we get communications about products we don't own, at addresses we don't reside, and using channels (like direct mail) we don't like. An all-too-common example of a potential challenge follows: Customers end up receiving duplicate communications, which not only impacts customer satisfaction, but also incurs additional mailing costs. Cleansing, normalizing, and standardizing data will help address most of these issues. 5. Only 10 percent of respondents have the ability to share data that was mastered in a master data hub. Close to 60 percent of respondents have efforts in place that profile, standardize and cleanse data manually, and the output of these efforts are stored in spreadsheets in various parts of the organization. This valuable information is not easily shared with the rest of the organization and, more importantly, this enriched information cannot be sent back to the source systems so that the data is fixed at the source. A key benefit of a master data management strategy is not only to clean the data, but to also share the data back to the source systems as well as other systems that need the information. Aside from the source systems, another key beneficiary of this data is the business intelligence system. Having clean master data as input to business intelligence systems provides more accurate and enhanced reporting.  Characteristics of Stellar MDM When deciding on the right master data management technology, organizations should look for solutions that have four main characteristics: enterprise-grade MDM performance complete technology that can be rapidly deployed and addresses multiple business issues end-to-end MDM process management with data quality monitoring and assurance pre-built MDM business relevant applications with data stores and workflows These master data management capabilities will aid in moving closer to a best-practice maturity level, delivering tremendous efficiencies and savings as well as revenue growth opportunities as a result of better understanding your customers.  Trevor Naidoo is a senior director in Industry Strategy and Insight at Oracle. 

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  • Address Regulatory Mandates for Data Encryption Without Changing Your Applications

    - by Troy Kitch
    The Payment Card Industry Data Security Standard, US state-level data breach laws, and numerous data privacy regulations worldwide all call for data encryption to protect personally identifiable information (PII). However encrypting PII data in applications requires costly and complex application changes. Fortunately, since this data typically resides in the application database, using Oracle Advanced Security, PII can be encrypted transparently by the Oracle database without any application changes. In this ISACA webinar, learn how Oracle Advanced Security offers complete encryption for data at rest, in transit, and on backups, along with built-in key management to help organizations meet regulatory requirements and save money. You will also hear from TransUnion Interactive, the consumer subsidiary of TransUnion, a global leader in credit and information management, which maintains credit histories on an estimated 500 million consumers across the globe, about how they addressed PCI DSS encryption requirements using Oracle Database 11g with Oracle Advanced Security. Register to watch the webinar now.

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  • Using Hadooop (HDInsight) with Microsoft - Two (OK, Three) Options

    - by BuckWoody
    Microsoft has many tools for “Big Data”. In fact, you need many tools – there’s no product called “Big Data Solution” in a shrink-wrapped box – if you find one, you probably shouldn’t buy it. It’s tempting to want a single tool that handles everything in a problem domain, but with large, complex data, that isn’t a reality. You’ll mix and match several systems, open and closed source, to solve a given problem. But there are tools that help with handling data at large, complex scales. Normally the best way to do this is to break up the data into parts, and then put the calculation engines for that chunk of data right on the node where the data is stored. These systems are in a family called “Distributed File and Compute”. Microsoft has a couple of these, including the High Performance Computing edition of Windows Server. Recently we partnered with Hortonworks to bring the Apache Foundation’s release of Hadoop to Windows. And as it turns out, there are actually two (technically three) ways you can use it. (There’s a more detailed set of information here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx, I’ll cover the options at a general level below)  First Option: Windows Azure HDInsight Service  Your first option is that you can simply log on to a Hadoop control node and begin to run Pig or Hive statements against data that you have stored in Windows Azure. There’s nothing to set up (although you can configure things where needed), and you can send the commands, get the output of the job(s), and stop using the service when you are done – and repeat the process later if you wish. (There are also connectors to run jobs from Microsoft Excel, but that’s another post)   This option is useful when you have a periodic burst of work for a Hadoop workload, or the data collection has been happening into Windows Azure storage anyway. That might be from a web application, the logs from a web application, telemetrics (remote sensor input), and other modes of constant collection.   You can read more about this option here:  http://blogs.msdn.com/b/windowsazure/archive/2012/10/24/getting-started-with-windows-azure-hdinsight-service.aspx Second Option: Microsoft HDInsight Server Your second option is to use the Hadoop Distribution for on-premises Windows called Microsoft HDInsight Server. You set up the Name Node(s), Job Tracker(s), and Data Node(s), among other components, and you have control over the entire ecostructure.   This option is useful if you want to  have complete control over the system, leave it running all the time, or you have a huge quantity of data that you have to bulk-load constantly – something that isn’t going to be practical with a network transfer or disk-mailing scheme. You can read more about this option here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx Third Option (unsupported): Installation on Windows Azure Virtual Machines  Although unsupported, you could simply use a Windows Azure Virtual Machine (we support both Windows and Linux servers) and install Hadoop yourself – it’s open-source, so there’s nothing preventing you from doing that.   Aside from being unsupported, there are other issues you’ll run into with this approach – primarily involving performance and the amount of configuration you’ll need to do to access the data nodes properly. But for a single-node installation (where all components run on one system) such as learning, demos, training and the like, this isn’t a bad option. Did I mention that’s unsupported? :) You can learn more about Windows Azure Virtual Machines here: http://www.windowsazure.com/en-us/home/scenarios/virtual-machines/ And more about Hadoop and the installation/configuration (on Linux) here: http://en.wikipedia.org/wiki/Apache_Hadoop And more about the HDInsight installation here: http://www.microsoft.com/web/gallery/install.aspx?appid=HDINSIGHT-PREVIEW Choosing the right option Since you have two or three routes you can go, the best thing to do is evaluate the need you have, and place the workload where it makes the most sense.  My suggestion is to install the HDInsight Server locally on a test system, and play around with it. Read up on the best ways to use Hadoop for a given workload, understand the parts, write a little Pig and Hive, and get your feet wet. Then sign up for a test account on HDInsight Service, and see how that leverages what you know. If you're a true tinkerer, go ahead and try the VM route as well. Oh - there’s another great reference on the Windows Azure HDInsight that just came out, here: http://blogs.msdn.com/b/brunoterkaly/archive/2012/11/16/hadoop-on-azure-introduction.aspx  

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  • Oracle - A Leader in Gartner's MQ for Master Data Management for Customer Data

    - by Mala Narasimharajan
      The Gartner MQ report for Master Data Management of Customer Data Solutions is released and we're proud to say that Oracle is in the leaders' quadrant.  Here's a snippet from the report itself:  " “Oracle has a strong, though complex, portfolio of domain-specific MDM products that include prepackaged data models. Gartner estimates that Oracle now has over 1,500 licensed MDM customers, including 650 customers managing customer data. The MDM portfolio includes three products that address MDM of customer data solution needs: Oracle Fusion Customer Hub (FCH), Oracle CDH and Oracle Siebel UCM. These three MDM products are positioned for different segments of the market and Oracle is progressively moving all three products onto a common MDM technology platform..." (Gartner, Oct 18, 2012)  For more information on Oracle's solutions for customer data in Master Data Management, click here.  

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  • Almost Realtime Data and Web application

    - by Chris G.
    I have a computer that is recording 100 different data points into an OPC server. I've written a simple OPC client that can read all of this data. I have a front-end website on a different network that I would like to consume this data. I could easily set the OPC client to send the data to a SQL server and the website could read from it, but that would be a lot of writes. If I wanted the data to be updated every 10 seconds I'd be writing to the database every 10 seconds. (I could probably just serialize the 100 points to get 1 write / 10 seconds but that would also limit my ability to search the data later). This solution wouldn't scale very well. If I had 100 of these computers the situation would quickly grow out of hand. Obviously I am well out of my league here and I have no experience with working with a large amount of data like this. What are my options and what should I research?

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  • NFS v4, HA Migration, and stale handles on clients

    - by Karl Katzke
    I'm managing a server running NFS v4 with Pacemaker/OpenAIS. NFS is configured to use TCP. When I migrate the NFS server to another node in the Pacemaker cluster, even though the metadata is persisted, connections from the clients 'hang' and eventually time out after 90 seconds. After that 90 seconds, the old mountpoint becomes 'stale' and the mounted files can no longer be accessed. The 90 second grace period seems to be part of the server configuration and not the client configuration. I see this message on the server: kernel: NFSD: starting 90-second grace period If I restart the NFS client on the client nodes after I migrate (unmounting and then remounting the share), then I don't experience the problem, but connections and file transfers still interrupted. Three questions: What is the 90 second grace period? What's it there for? How can I keep the files from going stale on the clients without restarting them after I migrate the NFS server to another node? Is it actually possible to migrate the NFS server without having large file uploads drop?

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