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  • Automated unit testing, integration testing or acceptance testing

    - by bjarkef
    TDD and unit testing seems to be the big rave at the moment. But it is really that useful compared to other forms of automated testing? Intuitively I would guess that automated integration testing is way more useful than unit testing. In my experience the most bugs seems to be in the interaction between modules, and not so much the actual (usual limited) logic of each unit. Also regressions often happened because of changing interfaces between modules (and changed pre and post-conditions.) Am I misunderstanding something, or why are unit testing getting so much focus compared to integration testing? It is simply because it is assumed that integration testing is something you have, and unit testing is the next thing we need to learn to apply as developers? Or maybe unit testing simply yields the highest gain compared to the complexity of automating it? What are you experience with automated unit testing, automated integration testing, and automated acceptance testing, and in your experience what has yielded the highest ROI? and why? If you had to pick just one form of testing to be automated on your next project, which would it be? Thanks in advance.

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  • Data Quality and Master Data Management Resources

    - by Dejan Sarka
    Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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  • Unit testing best practices for a unit testing newbie

    - by wilhil
    In recent years, I have only written small components for people in larger projects or small tools. I have never written a unit test and it always seems like learning how to write them and actually making one takes a lot longer than simply firing up the program and testing for real. I am just about to start a fairly large scale project that could take a few months to complete and whilst I will try to test elements as I write them (like always), I am wondering if unit testing could save me time. I was just wondering if anyone could give good advice: Should I be looking at unit testing at the start of the project and possibly adopt a TDD approach. Should I just write tests as I go along, after each section is complete. Should I complete the project and then write unit tests at the end.

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  • Should programmers itemize testing in testing? [on hold]

    - by Patton77
    I recently hired a programming team to do a port of my iPad app to the iPhone and Android platforms. Now, in a separate contract, I am asking them to implement a bunch of tips on how to play the app, similar like you would find in Candy Crush or Cut the Rope. They want to charge 12 hours @ $35/hr for the "Testing all of the Tips", telling me that normally it would take them more than 25 hours but that they will 'bear the difference'. I am not familiar with this level of itemization, but maybe it's a new practice? I am used to devs doing their own quality control, and then having a testing/acceptance period. They are using Cocos 2D-X, and they say that the tips going to multiple platforms makes all of the hours jack up. I feel like they might be overcharging, and it's difficult for me to know because it's kind of like with a mechanic. "It took us 5 hours to replace the radiator". How can you dispute that? It seems to me that most of you would charge for the work but NOT for hours that you are 'testing'. Am I missing something? Thanks for any help and advice you can give!

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  • Manual testing Vs Automated testing

    - by mgj
    Respected all, As many know testing can be mainly classified into manual and automated testing. With regard to this certain questions come to mind. Hope you can help... They include: What is the basic difference between the two types of testing? What are the elements of challenges involved in both manual and automated testing? What are the different skill sets required by a software tester for manual and automated testing respectively? What are the different job prospects and growth opportunities among software testers who do manual testing automated testing respectively? Is manual testing under rated to automated testing in anyway(s)? If yes, kindly specify the way. How differently are the manual testers treated in comparison to automated testers in the corporate world?( If they truly are differentiated in any terms as such ) I hope you can share your knowledge in answering these questions.. Thank you for your time..:)

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  • Test Data in a Distributed System

    - by Davin Tryon
    A question that has been vexing me lately has been about how to effectively test (end-to-end) features in a distributed system. Particuarly, how to effectively manage (through time) test data for feature testing. The system in question is a typical SOA setup. The composition is done in JavaScript when call to several REST APIs. Each service is built as an independent block. Each service has some kind of persistent storage (SQL Server in most cases). The main issue at the moment is how to approach test data when testing end-to-end features. Functional end-to-end testing occurs through the UI, and it is therefore necessary for test data to be set up before the test run (this could be manual or automated testing). As is typical in a distributed system, identifiers from one service are used as a link in another service. So, some level of synchronization needs to be present in the data to effectively test. What is the best way to manage and set up this data after a successful deployment to a test environment? For example, is it better to manage this test data inside each service? Or package it together with the testing suite? Does that testing suite exist as a separate project? I'm interested in design guidance about how to store and manage this test data as the application features evolve.

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  • In rails, what defines unit testing as opposed to other kinds of testing

    - by junky
    Initially I thought this was simple: unit testing for models with other testing such as integration for controller and browser testing for views. But more recently I've seen a lot of references to unit testing that doesn't seem to exactly follow this format. Is it possible to have a unit test of a controller? Does that mean that just one method is called? What's the distinction? What does unit testing really means in my rails world?

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  • Is unit testing development or testing?

    - by Rubio
    I had a discussion with a testing manager about the role of unit and integration testing. She requested that developers report what they have unit and integration tested and how. My perspective is that unit and integration testing are part of the development process, not the testing process. Beyond semantics what I mean is that unit and integration tests should not be included in the testing reports and systems testers should not be concerned about them. My reasoning is based on two things. Unit and integration tests are planned and performed against an interface and a contract, always. Regardless of whether you use formalized contracts you still test what e.g. a method is supposed to do, i.e. a contract. In integration testing you test the interface between two distinct modules. The interface and the contract determine when the test passes. But you always test a limited part of the whole system. Systems testing on the other hand is planned and performed against the system specifications. The spec determines when the test passes. I don't see any value in communicating the breadth and depth of unit and integration tests to the (systems) tester. Suppose I write a report that lists what kind of unit tests are performed on a particular business layer class. What is he/she supposed to take away from that? Judging what should and shouldn't be tested from that is a false conclusion because the system may still not function the way the specs require even though all unit and integration tests pass. This might seem like useless academic discussion but if you work in a strictly formal environment as I do, it's actually important in determining how we do things. Anyway, am I totally wrong? (Sorry for the long post.)

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  • Big Data – Learning Basics of Big Data in 21 Days – Bookmark

    - by Pinal Dave
    Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big DataData Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 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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  • Differences between software testing processes and techniques?

    - by Aptos
    I get confused between these terms. For examples, should Unit testing be listed as a software testing process or technique? I think unit testing is a software testing technique. And how about Test driven development? Can you give me some examples for software testing processes and techniques? In my opinion, software testing process is a part of the software development life cycle. For example, if we use V-Model, the software testing process will be System test, Acceptance test, Integration Test... Thank you.

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  • Is it dangerous to substitute unit tests for user testing? [closed]

    - by MushinNoShin
    Is it dangerous to substitute unit tests for user testing? A co-worker believes we can reduce the manual user testing we need to do by adding more unit tests. Is this dangerous? Unit tests seem to have a very different purpose than user testing. Aren't unit tests to inform design and allow breaking changes to be caught early? Isn't that fundamentally different than determining if an aspect of the system is correct as a whole of the system? Is this a case of substituting apples for oranges?

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  • Unit testing newbie team needs to unit test

    - by Walter
    I'm working with a new team that has historically not done ANY unit testing. My goal is for the team to eventually employ TDD (Test Driven Development) as their natural process. But since TDD is such a radical mind shift for a non-unit testing team I thought I would just start off with writing unit tests after coding. Has anyone been in a similar situation? What's an effective way to get a team to be comfortable with TDD when they've not done any unit testing? Does it make sense to do this in a couple of steps? Or should we dive right in and face all the growing pains at once?? EDIT Just for clarification, there is no one on the team (other than myself) who has ANY unit testing exposure/experience. And we are planning on using the unit testing functionality built into Visual Studio.

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  • Isolating test data in acceptance tests

    - by Matt Phillips
    I'm looking for guidance on how to keep my acceptance tests isolated. Right now the issue I'm having with being able to run the tests in parallel is the database records that are manipulated in the tests. I've written helpers that take care of doing inserts and deletes before tests are executed, to make sure the state is correct. But now I can't run them in parallel against the same database without uniquely generating the test data fields for each test. For example. Testing creating a row i'll delete everything where column A = foo and column B = bar Then I'll navigate through the UI in the test and create a record with column A = foo and column B = bar. Testing that a duplicate row is not allowed to be created. I'll insert a row with column A = foo and column B = bar and then use the UI to try and do the exact same thing. This will display an error message in the UI as expected. These tests work perfectly when ran separately and serially. But I can't run them at the same time for fear that one will create or delete a record the other is expecting. Any tips on how to structure them better so they can be run in parallel?

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  • Unit and Integration testing: How can it become a reflex

    - by LordOfThePigs
    All the programmers in my team are familiar with unit testing and integration testing. We have all worked with it. We have all written tests with it. Some of us even have felt an improved sense of trust in his/her own code. However, for some reason, writing unit/integration tests has not become a reflex for any of the members of the team. None of us actually feel bad when not writing unit tests at the same time as the actual code. As a result, our codebase is mostly uncovered by unit tests, and projects enter production untested. The problem with that, of course is that once your projects are in production and are already working well, it is virtually impossible to obtain time and/or budget to add unit/integration testing. The members of my team and myself are already familiar with the value of unit testing (1, 2) but it doesn't seem to help bringing unit testing into our natural workflow. In my experience making unit tests and/or a target coverage mandatory just results in poor quality tests and slows down team members simply because there is no self-generated motivation to produce these tests. Also as soon as pressure eases, unit tests are not written any more. My question is the following: Is there any methods that you have experimented with that helps build a dynamic/momentum inside the team, leading to people naturally wanting to create and maintain those tests?

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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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  • 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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  • How and when to use UNIT testing properly

    - by Zebs
    I am an iOS developer. I have read about unit testing and how it is used to test specific pieces of your code. A very quick example has to do with processing JSON data onto a database. The unit test reads a file from the project bundle and executes the method that is in charge of processing JSON data. But I dont get how this is different from actually running the app and testing with the server. So my question might be a bit general, but I honestly dont understand the proper use of unit testing, or even how it is useful; I hope the experienced programmers that surf around StackOverflow can help me. Any help is very much appreciated!

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  • What are the disadvantages of automated testing?

    - by jkohlhepp
    There are a number of questions on this site that give plenty of information about the benefits that can be gained from automated testing. But I didn't see anything that represented the other side of the coin: what are the disadvantages? Everything in life is a tradeoff and there are no silver bullets, so surely there must be some valid reasons not to do automated testing. What are they? Here's a few that I've come up with: Requires more initial developer time for a given feature Requires a higher skill level of team members Increase tooling needs (test runners, frameworks, etc.) Complex analysis required when a failed test in encountered - is this test obsolete due to my change or is it telling me I made a mistake? Edit I should say that I am a huge proponent of automated testing, and I'm not looking to be convinced to do it. I'm looking to understand what the disadvantages are so when I go to my company to make a case for it I don't look like I'm throwing around the next imaginary silver bullet. Also, I'm explicity not looking for someone to dispute my examples above. I am taking as true that there must be some disadvantages (everything has trade-offs) and I want to understand what those are.

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  • Should programmers itemize testing for projects? [on hold]

    - by Patton77
    I recently hired a programming team to do a port of my iPad app to the iPhone and Android platforms. Now, in a separate contract, I am asking them to implement a bunch of tips on how to play the app, similar like you would find in Candy Crush or Cut the Rope. They want to charge 12 hours @ $35/hr for the "Testing all of the Tips", telling me that normally it would take them more than 25 hours but that they will 'bear the difference'. I am not familiar with this level of itemization, but maybe it's a new practice? I am used to devs doing their own quality control, and then having a testing/acceptance period. They are using Cocos 2D-X, and they say that the tips going to multiple platforms makes all of the hours jack up. I feel like they might be overcharging, and it's difficult for me to know because it's kind of like with a mechanic. "It took us 5 hours to replace the radiator". How can you dispute that? It seems to me that most of you would charge for the work but NOT for hours that you are 'testing'. Am I missing something? Thanks for any help and advice you can give!

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  • Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

    - by Pinal Dave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  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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  • Unit testing in Django

    - by acjohnson55
    I'm really struggling to write effective unit tests for a large Django project. I have reasonably good test coverage, but I've come to realize that the tests I've been writing are definitely integration/acceptance tests, not unit tests at all, and I have critical portions of my application that are not being tested effectively. I want to fix this ASAP. Here's my problem. My schema is deeply relational, and heavily time-oriented, giving my model object high internal coupling and lots of state. Many of my model methods query based on time intervals, and I've got a lot of auto_now_add going on in timestamped fields. So take a method that looks like this for example: def summary(self, startTime=None, endTime=None): # ... logic to assign a proper start and end time # if none was provided, probably using datetime.now() objects = self.related_model_set.manager_method.filter(...) return sum(object.key_method(startTime, endTime) for object in objects) How does one approach testing something like this? Here's where I am so far. It occurs to me that the unit testing objective should be given some mocked behavior by key_method on its arguments, is summary correctly filtering/aggregating to produce a correct result? Mocking datetime.now() is straightforward enough, but how can I mock out the rest of the behavior? I could use fixtures, but I've heard pros and cons of using fixtures for building my data (poor maintainability being a con that hits home for me). I could also setup my data through the ORM, but that can be limiting, because then I have to create related objects as well. And the ORM doesn't let you mess with auto_now_add fields manually. Mocking the ORM is another option, but not only is it tricky to mock deeply nested ORM methods, but the logic in the ORM code gets mocked out of the test, and mocking seems to make the test really dependent on the internals and dependencies of the function-under-test. The toughest nuts to crack seem to be the functions like this, that sit on a few layers of models and lower-level functions and are very dependent on the time, even though these functions may not be super complicated. My overall problem is that no matter how I seem to slice it, my tests are looking way more complex than the functions they are testing.

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  • design pattern for unit testing? [duplicate]

    - by Maddy.Shik
    This question already has an answer here: Unit testing best practices for a unit testing newbie 4 answers I am beginner in developing test cases, and want to follow good patterns for developing test cases rather than following some person or company's specific ideas. Some people don't make test cases and just develop the way their senior have done in their projects. I am facing lot problems like object dependencies (when want to test method which persist A object i have to first persist B object since A is child of B). Please suggest some good books or sites preferably for learning design pattern for unit test cases. Or reference to some good source code or some discussion for Dos and Donts will do wonder. So that i can avoid doing mistakes be learning from experience of others.

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  • Big Data – Basics of Big Data Architecture – Day 4 of 21

    - by Pinal Dave
    In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. 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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