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  • SSIS 2005 - How to Import a Fixed Width Flat File?

    - by Greg
    I have a flat file that looks something like this: junk I don't care about \n \n columns names\n val1 val2 val3\n val1 val2 val3\n columns names \n val1 val2 val3\n I only care the lines with values. These value lines are all fixed width format and have the same line length. The other junk lines and column names can have any line width. When I try the flat file fixed width option or the ragged right option the preview looks all wrong. Any ideas what the easiest way to get this into SSIS is?

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  • SSIS - Can I get the column schema for a flat file source from a database?

    - by Steve Clement
    We receive a nightly data export from a vendor in the form of about 10 tab-delimited flat file without column headers. In addition, the vendor provides us with the SQL scripts for the database tables so that we can import the files into our system. Unfortunately, the vendor recently changed the schema for the flat files. Each file has upwards 150 columns, and having to go through the DB schema and adjust column types on a Flat File Data Source in SSIS is extremely time consuming, not to mention a royal pain. Since I know the file data layout in the database schema, is there any way I can dynamically pull that into a Flat File source to set the columns correctly? Or am I just stuck with manually setting everything?

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  • Calculating percentiles in Excel with "buckets" data instead of the data list itself

    - by G B
    I have a bunch of data in Excel that I need to get certain percentile information from. The problem is that instead of having the data set made up of each value, I instead have info on the number of or "bucket" data. For example, imagine that my actual data set looks like this: 1,1,2,2,2,2,3,3,4,4,4 The data set that I have is this: Value No. of occurrences 1 2 2 4 3 2 4 3 Is there an easy way for me to calculate percentile information (as well as the median) without having to explode the summary data out to full data set? (Once I did that, I know that I could just use the Percentile(A1:A5, p) function) This is important because my data set is very large. If I exploded the data out, I would have hundreds of thousands of rows and I would have to do it for a couple of hundred data sets. Help!

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  • How do I setup a WCF Data Service with an ADO.NET Entity Entity Model in another assembly?

    - by lsb
    Hi! I have an ASP.NET 4.0 website that has an Entity Data Model hooked up to WCF Data Service. When the Service and Model are in the same assembly everything works. Unfortunately, when I move the Model to another "shared" assembly (and change the namespace) the service compiles but throws a 500 error when launched in a browser. The reason I want to have the Model in a common assembly (lets call it RiaTest.Shared) is that I want share common validation code between the client and service (by checking "Reuse types in referenced assemblies" in the Advanced tab of the Add Service Reference dialog). Anyway, I've spent a couple of hours on this to no avail so any help in the regard would be appreciated...

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  • Open Data, Government and Transparency

    - by Tori Wieldt
    A new track at TDC (The Developer's Conference in Sao Paulo, Brazil) is titled Open Data. It deals with open data, government and transparency. Saturday will be a "transparency hacker day" where developers are invited to create applications using open data from the Brazilian government.  Alexandre Gomes, co-lead of the track, says "I want to inspire developers to become "Civic hackers:" developers who create apps to make society better." It is a chance for developers to do well and do good. There are many opportunities for developers, including monitoring government expenditures and getting citizens involved via social networks. The open data movement is growing worldwide. One initiative, the Open Government Partnership, is working to make government data easier to find and access. Making this data easily available means that with the right applications, it will be easier for people to make decisions and suggestions about government policies based on detailed information. Last April, the Open Government Partnership held its annual meeting in Brasilia, the capitol of Brazil. It was a great success showcasing the innovative work being done in open data by governments, civil societies and individuals around the world. For example, Bulgaria now publishes daily data on budget spending for all public institutions. Alexandre Gomes Explains Open Data At TDC, the Open Data track will include a presentation of examples of successful open data projects, an introduction to the semantic web, how to handle big data sets, techniques of data visualization, and how to design APIs.The other track lead is Christian Moryah Miranda, a systems analyst for the Brazilian Government's Ministry of Planning. "The Brazilian government wholeheartedly supports this effort. In order to make our data available to the public, it forces us to be more consistent with our data across ministries, and that's a good step forward for us," he said. He explained the government knows they cannot achieve everything they would like without help from the public. "It is not the government versus the people, rather citizens are partners with the government, and together we can achieve great things!" Miranda exclaimed. Saturday at TDC will be a "transparency hacker day" where developers will be invited to create applications using open data from the Brazilian government. Attendees are invited to pitch their ideas, work in small groups, and present their project at the end of the conference. "For example," Gomes said, "the Brazilian government just released the salaries of all government employees and I can't wait to see what developers can do with that." Resources Open Government Partnership  U.S. Government Open Data ProjectBrazilian Government Open Data ProjectU.K. Government Open Data Project 2012 International Open Government Data Conference 

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  • Tuesday 6th Manchester SQL User Group - Chris Testa-O'Neil (Loading a datawarehouse using SSIS) and

    - by tonyrogerson
    Chris will give a talk on Loading a datawarehouse using SQL Server Integration Services, Tony Rogerson will give a talk on Database Design: Normalisation/Denormalisation and using Surrogate Keys - practicalities/pitfalls and benefits in Microsoft SQL Server. Registration is essential which you can do here: http://sqlserverfaq.com?eid=218 . Come and join us for an evening of SQL Server discussion, as well as the two formal sessions by Chris Testa-O'Neil and Tony Rogerson there will be a chance...(read more)

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  • Master Data Management and Cloud Computing

    - by david.butler(at)oracle.com
    Cloud Computing is all the rage these days. There are many reasons why this is so. But like its predecessor, Service Oriented Architecture, it can fall on hard times if the underlying data is left unmanaged. Master Data Management is the perfect Cloud companion. It can materially increase the chances for successful Cloud initiatives. In this blog, I'll review the nature of the Cloud and show how MDM fits in.   Here's the National Institute of Standards and Technology Cloud definition: •          Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.   Cloud architectures have three main layers: applications or Software as a Service (SaaS), Platforms as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS generally refers to applications that are delivered to end-users over the Internet. Oracle CRM On Demand is an example of a SaaS application. Today there are hundreds of SaaS providers covering a wide variety of applications including Salesforce.com, Workday, and Netsuite. Oracle MDM applications are located in this layer of Oracle's On Demand enterprise Cloud platform. We call it Master Data as a Service (MDaaS). PaaS generally refers to an application deployment platform delivered as a service. They are often built on a grid computing architecture and include database and middleware. Oracle Fusion Middleware is in this category and includes the SOA and Data Integration products used to connect SaaS applications including MDM. Finally, IaaS generally refers to computing hardware (servers, storage and network) delivered as a service.  This typically includes the associated software as well: operating systems, virtualization, clustering, etc.    Cloud Computing benefits are compelling for a large number of organizations. These include significant cost savings, increased flexibility, and fast deployments. Cost advantages include paying for just what you use. This is especially critical for organizations with variable or seasonal usage. Companies don't have to invest to support peak computing periods. Costs are also more predictable and controllable. Increased agility includes access to the latest technology and experts without making significant up front investments.   While Cloud Computing is certainly very alluring with a clear value proposition, it is not without its challenges. An IDC survey of 244 IT executives/CIOs and their line-of-business (LOB) colleagues identified a number of issues:   Security - 74% identified security as an issue involving data privacy and resource access control. Integration - 61% found that it is hard to integrate Cloud Apps with in-house applications. Operational Costs - 50% are worried that On Demand will actually cost more given the impact of poor data quality on the rest of the enterprise. Compliance - 49% felt that compliance with required regulatory, legal and general industry requirements (such as PCI, HIPAA and Sarbanes-Oxley) would be a major issue. When control is lost, the ability of a provider to directly manage how and where data is deployed, used and destroyed is negatively impacted.  There are others, but I singled out these four top issues because Master Data Management, properly incorporated into a Cloud Computing infrastructure, can significantly ameliorate all of these problems. Cloud Computing can literally rain raw data across the enterprise.   According to fellow blogger, Mike Ferguson, "the fracturing of data caused by the adoption of cloud computing raises the importance of MDM in keeping disparate data synchronized."   David Linthicum, CTO Blue Mountain Labs blogs that "the lack of MDM will become more of an issue as cloud computing rises. We're moving from complex federated on-premise systems, to complex federated on-premise and cloud-delivered systems."    Left unmanaged, non-standard, inconsistent, ungoverned data with questionable quality can pollute analytical systems, increase operational costs, and reduce the ROI in Cloud and On-Premise applications. As cloud computing becomes more relevant, and more data, applications, services, and processes are moved out to cloud computing platforms, the need for MDM becomes ever more important. Oracle's MDM suite is designed to deal with all four of the above Cloud issues listed in the IDC survey.   Security - MDM manages all master data attribute privacy and resource access control issues. Integration - MDM pre-integrates Cloud Apps with each other and with On Premise applications at the data level. Operational Costs - MDM significantly reduces operational costs by increasing data quality, thereby improving enterprise business processes efficiency. Compliance - MDM, with its built in Data Governance capabilities, insures that the data is governed according to organizational standards. This facilitates rapid and accurate reporting for compliance purposes. Oracle MDM creates governed high quality master data. A unified cleansed and standardized data view is produced. The Oracle Customer Hub creates a single view of the customer. The Oracle Product Hub creates high quality product data designed to support all go-to-market processes. Oracle Supplier Hub dramatically reduces the chances of 'supplier exceptions'. Oracle Site Hub masters locations. And Oracle Hyperion Data Relationship Management masters financial reference data and manages enterprise hierarchies across operational areas from ERP to EPM and CRM to SCM. Oracle Fusion Middleware connects Cloud and On Premise applications to MDM Hubs and brings high quality master data to your enterprise business processes.   An independent analyst once said "Poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything."  Cloud Computing has the potential to significantly degrade data quality across the enterprise over time. Deploying a Master Data Management solution prior to or in conjunction with a move to the Cloud can insure that the data flowing into the enterprise from the Cloud is clean and governed. This will in turn insure that expected returns on the investment in Cloud Computing will be realized.       Oracle MDM has proven its metal in this area and has the customers to back that up. In fact, I will be hosting a webcast on Tuesday, April 10th at 10 am PT with one of our top Cloud customers, the Church Pension Group. They have moved all mainline applications to a hosted model and use Oracle MDM to insure the master data is managed and cleansed before it is propagated to other cloud and internal systems. I invite you join Martin Hossfeld, VP, IT Operations, and Danette Patterson, Enterprise Data Manager as they review business drivers for MDM and hosted applications, how they did it, the benefits achieved, and lessons learned. You can register for this free webcast here.  Hope to see you there.

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  • Basics of Join Factorization

    - by Hong Su
    We continue our series on optimizer transformations with a post that describes the Join Factorization transformation. The Join Factorization transformation was introduced in Oracle 11g Release 2 and applies to UNION ALL queries. Union all queries are commonly used in database applications, especially in data integration applications. In many scenarios the branches in a UNION All query share a common processing, i.e, refer to the same tables. In the current Oracle execution strategy, each branch of a UNION ALL query is evaluated independently, which leads to repetitive processing, including data access and join. The join factorization transformation offers an opportunity to share the common computations across the UNION ALL branches. Currently, join factorization only factorizes common references to base tables only, i.e, not views. Consider a simple example of query Q1. Q1:    select t1.c1, t2.c2    from t1, t2, t3    where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c2 = 2 and t2.c2 = t3.c2   union all    select t1.c1, t2.c2    from t1, t2, t4    where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c3 = t4.c3; Table t1 appears in both the branches. As does the filter predicates on t1 (t1.c1 > 1) and the join predicates involving t1 (t1.c1 = t2.c1). Nevertheless, without any transformation, the scan (and the filtering) on t1 has to be done twice, once per branch. Such a query may benefit from join factorization which can transform Q1 into Q2 as follows: Q2:    select t1.c1, VW_JF_1.item_2    from t1, (select t2.c1 item_1, t2.c2 item_2                   from t2, t3                    where t2.c2 = t3.c2 and t2.c2 = 2                                  union all                   select t2.c1 item_1, t2.c2 item_2                   from t2, t4                    where t2.c3 = t4.c3) VW_JF_1    where t1.c1 = VW_JF_1.item_1 and t1.c1 > 1; In Q2, t1 is "factorized" and thus the table scan and the filtering on t1 is done only once (it's shared). If t1 is large, then avoiding one extra scan of t1 can lead to a huge performance improvement. Another benefit of join factorization is that it can open up more join orders. Let's look at query Q3. Q3:    select *    from t5, (select t1.c1, t2.c2                  from t1, t2, t3                  where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c2 = 2 and t2.c2 = t3.c2                 union all                  select t1.c1, t2.c2                  from t1, t2, t4                  where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c3 = t4.c3) V;   where t5.c1 = V.c1 In Q3, view V is same as Q1. Before join factorization, t1, t2 and t3 must be joined first before they can be joined with t5. But if join factorization factorizes t1 from view V, t1 can then be joined with t5. This opens up new join orders. That being said, join factorization imposes certain join orders. For example, in Q2, t2 and t3 appear in the first branch of the UNION ALL query in view VW_JF_1. T2 must be joined with t3 before it can be joined with t1 which is outside of the VW_JF_1 view. The imposed join order may not necessarily be the best join order. For this reason, join factorization is performed under cost-based transformation framework; this means that we cost the plans with and without join factorization and choose the cheapest plan. Note that if the branches in UNION ALL have DISTINCT clauses, join factorization is not valid. For example, Q4 is NOT semantically equivalent to Q5.   Q4:     select distinct t1.*      from t1, t2      where t1.c1 = t2.c1  union all      select distinct t1.*      from t1, t2      where t1.c1 = t2.c1 Q5:    select distinct t1.*     from t1, (select t2.c1 item_1                   from t2                union all                   select t2.c1 item_1                  from t2) VW_JF_1     where t1.c1 = VW_JF_1.item_1 Q4 might return more rows than Q5. Q5's results are guaranteed to be duplicate free because of the DISTINCT key word at the top level while Q4's results might contain duplicates.   The examples given so far involve inner joins only. Join factorization is also supported in outer join, anti join and semi join. But only the right tables of outer join, anti join and semi joins can be factorized. It is not semantically correct to factorize the left table of outer join, anti join or semi join. For example, Q6 is NOT semantically equivalent to Q7. Q6:     select t1.c1, t2.c2    from t1, t2    where t1.c1 = t2.c1(+) and t2.c2 (+) = 2  union all    select t1.c1, t2.c2    from t1, t2      where t1.c1 = t2.c1(+) and t2.c2 (+) = 3 Q7:     select t1.c1, VW_JF_1.item_2    from t1, (select t2.c1 item_1, t2.c2 item_2                  from t2                  where t2.c2 = 2                union all                  select t2.c1 item_1, t2.c2 item_2                  from t2                                                                                                    where t2.c2 = 3) VW_JF_1       where t1.c1 = VW_JF_1.item_1(+)                                                                  However, the right side of an outer join can be factorized. For example, join factorization can transform Q8 to Q9 by factorizing t2, which is the right table of an outer join. Q8:    select t1.c2, t2.c2    from t1, t2      where t1.c1 = t2.c1 (+) and t1.c1 = 1 union all    select t1.c2, t2.c2    from t1, t2    where t1.c1 = t2.c1(+) and t1.c1 = 2 Q9:   select VW_JF_1.item_2, t2.c2   from t2,             (select t1.c1 item_1, t1.c2 item_2            from t1            where t1.c1 = 1           union all            select t1.c1 item_1, t1.c2 item_2            from t1            where t1.c1 = 2) VW_JF_1   where VW_JF_1.item_1 = t2.c1(+) All of the examples in this blog show factorizing a single table from two branches. This is just for ease of illustration. Join factorization can factorize multiple tables and from more than two UNION ALL branches.  SummaryJoin factorization is a cost-based transformation. It can factorize common computations from branches in a UNION ALL query which can lead to huge performance improvement. 

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  • Can anyone explain to me what problem Core Data solves?

    - by Curtis Sumpter
    Core Data seems to add a needless layer of complexity. If you want to save data created natively by the user in an app why not just use an object and then write the data all to SQLite or back to a server using a RESTful script if necessary. Android doesn't have Core Data (though if it has something similar I haven't seen it.). What the heck is the point of buggy CD except useless needless overhead for people who can't write SQL or CGI scripts?

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  • Difference between "Data Binding'","Data Hiding","Data Wraping" and "Encapsulation"?

    - by krishna Chandra
    I have been studying the conpects of Object oriented programming. Still I am not able to distinguish between the following concepts of object oriented programming.. a) Data Binding b) Data Hiding c) Data Wrapping d) encapsulation e) Data Abstraction I have gone through a lot of books ,and I also search the difference in google. but still I am not able to make the difference between these? Could anyone please help me ?

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  • Regular Expression Transformation

    The regular expression transformation exposes the power of regular expression matching within the pipeline. One or more columns can be selected, and for each column an individual expression can be applied. The way multiple columns are handled can be set on the options page. The AND option means all columns must match, whilst the OR option means only one column has to match. If rows pass their tests then rows are passed down the successful match output. Rows that fail are directed down the alternate output. This transformation is ideal for validating data through the use of regular expressions. You can enter any expression you like, or select a pre-configured expression within the editor. You can expand the list of pre-configured expressions yourself. These are stored in a Xml file, %ProgramFiles%\Microsoft SQL Server\nnn\DTS\PipelineComponents\RegExTransform.xml, where nnn represents the folder version, 90 for 2005, 100 for 2008 and 110 for 2012. If you want to use regular expressions to manipulate data, rather than just validating it, try the RegexClean Transformation. The component is provided as an MSI file, however for 2005/200 you will have to add the transformation to the Visual Studio toolbox by hand. This process has been described in detail in the related FAQ entry for How do I install a task or transform component?, just select Regular Expression Transformation in the Choose Toolbox Items window. Downloads The Regular Expression Transformation is available for SQL Server 2005, SQL Server 2008 (includes R2) and SQL Server 2012. Please choose the version to match your SQL Server version, or you can install multiple versions and use them side by side if you have more than one version of SQL Server installed. Regular Expression Transformation for SQL Server 2005 Regular Expression Transformation for SQL Server 2008 Regular Expression Transformation for SQL Server 2012 Version History SQL Server 2012Version 2.0.0.87 - SQL Server 2012 release. Includes upgrade support for both 2005 and 2008 packages to 2012. (5 Jun 2012) SQL Server 2008Version 2.0.0.87 - Release for SQL Server 2008 Integration Services. (10 Oct 2008) SQL Server 2005 Version 1.1.0.93 - Added option for you to choose AND or OR logic when multiple columns have been selected. Previously behaviour was OR only. (31 Jul 2008) Version 1.0.0.76 - Installer update and improved exception handling. (28 Jan 2008) Version 1.0.0.41 - Update for user interface stability fixes. (2 Aug 2006) Version 1.0.0.24 - SQL Server 2005 RTM Refresh. SP1 Compatibility Testing. (12 Jun 2006) Version 1.0.0.9 - Public Release for SQL Server 2005 IDW 15 June CTP (29 Aug 2005) Screenshots  

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  • The SSIS File Sweeper

    Moving files around is a task that many DBAs need to accomplish. Whether as part of an import or export process, or just for administration purposes, this new article from JD Gonzalez can help you solve this problem.

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  • Recover harddrive data

    - by gameshints
    I have a dell laptop that recently "died" (It would get the blue screen of death upon starting) and the hard drive would make a weird cyclic clicking noises. I wanted to see if I could use some tools on my linux machine to recover the data, so I plugged it into there. If I run "fdisk" I get: Disk /dev/sdb: 20.0 GB, 20003880960 bytes 64 heads, 32 sectors/track, 19077 cylinders Units = cylinders of 2048 * 512 = 1048576 bytes Disk identifier: 0x64651a0a Disk /dev/sdb doesn't contain a valid partition table Fine, the partition table is messed up. However if I run "testdisk" in attempt to fix the table, it freezes at this point, making the same cyclical clicking noises: Disk /dev/sdb - 20 GB / 18 GiB - CHS 19078 64 32 Analyse cylinder 158/19077: 00% I don't really care about the hard drive working again, and just the data, so I ran "gpart" to figure out where the partitions used to be. I got this: dev(/dev/sdb) mss(512) chs(19077/64/32)(LBA) #s(39069696) size(19077mb) * Warning: strange partition table magic 0x2A55. Primary partition(1) type: 222(0xDE)(UNKNOWN) size: 15mb #s(31429) s(63-31491) chs: (0/1/1)-(3/126/63)d (0/1/32)-(15/24/4)r hex: 00 01 01 00 DE 7E 3F 03 3F 00 00 00 C5 7A 00 00 Primary partition(2) type: 007(0x07)(OS/2 HPFS, NTFS, QNX or Advanced UNIX) (BOOT) size: 19021mb #s(38956987) s(31492-38988478) chs: (4/0/1)-(895/126/63)d (15/24/5)-(19037/21/31)r hex: 80 00 01 04 07 7E FF 7F 04 7B 00 00 BB 6F 52 02 So I tried to mount just to the old NTFS partition, but got an error: sudo mount -o loop,ro,offset=16123904 -t ntfs /dev/sdb /mnt/usb NTFS signature is missing. Ugh. Okay. But then I tried to get a raw data dump by running dd if=/dev/sdb of=/home/erik/brokenhd skip=31492 count=38956987 But the file got up to 59885568 bytes, and made the same cyclical clicking noises. Obviously there is a bad sector, but I don't know what to do about it! The data is still there... if I view that 57MB file in textpad... I can see raw data from files. How can I get my data back? Thanks for any suggestions, Solution: I was able to recover about 90% of my data: Froze harddrive in freezer Used Ddrescue to make a copy of the drive Since Ddrescue wasn't able to get enough of my drive to use testdisk to recover my partitions/file system, I ended up using photorec to recover most of my files

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  • Consolidating SQL Server Error Logs from Multiple Instances Using SSIS

    SQL Server hides a lot of very useful information in its error log files. Unfortunately, the process of hunting through all these logs, file-by-file, server-by-server, can cause a problem. Rodney Landrum offers a solution which will allow you to pull error log records from multiple servers into a central database, for analysis and reporting with T-SQL.

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  • Consolidating SQL Server Error Logs from Multiple Instances Using SSIS

    SQL Server hides a lot of very useful information in its error log files. Unfortunately, the process of hunting through all these logs, file-by-file, server-by-server, can cause a problem. Rodney Landrum offers a solution which will allow you to pull error log records from multiple servers into a central database, for analysis and reporting with T-SQL....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Script Task and Event Logging in SSIS

    components. These same configuration settings can be leveraged in Script Task, but you also have the option of triggering and tracking events in a considerably more customizable manner. This article explores features that provide this flexibility. NEW! SQL Monitor 2.0Monitor SQL Server Central's servers withRed Gate's new SQL Monitor.No installation required. Find out more.

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  • Export images from a SQL Server Table to a Folder with SSIS

    Can I export images from SQL Server to a file in Windows? What SQL Server options are available to do so? Check out this tip to learn more. Keep your database and application development in syncSQL Connect is a Visual Studio add-in that brings your databases into your solution. It then makes it easy to keep your database in sync, and commit to your existing source control system. Find out more.

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  • Webbased data modelling and management tool

    - by pixeldude
    Is there a web-based tool available, where I am able to... ...define data models (like in a database admin tool) ...fill in data (in custom web forms, not too generic) with basic features like completion ...import data from CSV oder Excel Sheets ...export data to CSV or SQL ...create snapshots of my data models (versions, diff, etc.) ...share my data models ...discuss/collaborate with other people about my data models Well, I can develop something like this in PHP or with Ruby or whatever. But this is such a common task, where the application support could be a lot better. And it would be language and database independent. This would help to maintain data models in different versions and you can maybe share your data models with others, extend it with your team members, etc. There is a website called FreeBase, which allows you to define a data entity model and fill in data, which also has export features, but I need to define my own data model with my own granularity and structure. And it should not be shared in public if I don't want to. How do you solve problems like this yourself?

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  • Advanced Data Source Engine coming to Telerik Reporting Q1 2010

    This is the final blog post from the pre-release series. In it we are going to share with you some of the updates coming to our reporting solution in Q1 2010. A new Declarative Data Source Engine will be added to Telerik Reporting, that will allow full control over data management, and deliver significant gains in rendering performance and memory consumption. Some of the engines new features will be: Data source parameters - those parameters will be used to limit data retrieved from the data source to just the data needed for the report. Data source parameters are processed on the data source side, however only queried data is fetched to the reporting engine, rather than the full data source. This leads to lower memory consumption, because data operations are performed on queried data only, rather than on all data. As a result, only the queried data needs to be stored in the memory vs. the whole dataset, which was the case with the old approach Support for stored procedures - they will assist in achieving a consistent implementation of logic across applications, and are especially practical for performing repetitive tasks. A stored procedure stores the SQL statements and logic, which can then be executed in different reports and/or applications. Stored Procedures will not only save development time, but they will also improve performance, because each stored procedure is compiled on the data base server once, and then is reutilized. In Telerik Reporting, the stored procedure will also be parameterized, where elements of the SQL statement will be bound to parameters. These parameterized SQL queries will be handled through the data source parameters, and are evaluated at run time. Using parameterized SQL queries will improve the performance and decrease the memory footprint of your application, because they will be applied directly on the database server and only the necessary data will be downloaded on the middle tier or client machine; Calculated fields through expressions - with the help of the new reporting engine you will be able to use field values in formulas to come up with a calculated field. A calculated field is a user defined field that is computed "on the fly" and does not exist in the data source, but can perform calculations using the data of the data source object it belongs to. Calculated fields are very handy for adding frequently used formulas to your reports; Improved performance and optimized in-memory OLAP engine - the new data source will come with several improvements in how aggregates are calculated, and memory is managed. As a result, you may experience between 30% (for simpler reports) and 400% (for calculation-intensive reports) in rendering performance, and about 50% decrease in memory consumption. Full design time support through wizards - Declarative data sources are a great advance and will save developers countless hours of coding. In Q1 2010, and true to Telerik Reportings essence, using the new data source engine and its features requires little to no coding, because we have extended most of the wizards to support the new functionality. The newly extended wizards are available in VS2005/VS2008/VS2010 design-time. More features will be revealed on the product's what's new page when the new version is officially released in a few days. Also make sure you attend the free webinar on Thursday, March 11th that will be dedicated to the updates in Telerik Reporting Q1 2010. Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • BizTalk Cross Reference Data Management Strategy

    - by charlie.mott
    Article Source: http://geekswithblogs.net/charliemott This article describes an approach to the management of cross reference data for BizTalk.  Some articles about the BizTalk Cross Referencing features can be found here: http://home.comcast.net/~sdwoodgate/xrefseed.zip http://geekswithblogs.net/michaelstephenson/archive/2006/12/24/101995.aspx http://geekswithblogs.net/charliemott/archive/2009/04/20/value-vs.id-cross-referencing-in-biztalk.aspx Options Current options to managing this data include: Maintaining xml files in the format that can be used by the out-of-the-box BTSXRefImport.exe utility. Use of user interfaces that have been developed to manage this data: BizTalk Cross Referencing Tool XRef XML Creation Tool However, there are the following issues with the above options: The 'BizTalk Cross Referencing Tool' requires a separate database to manage.  The 'XRef XML Creation' tool has no means of persisting the data settings. The 'BizTalk Cross Referencing tool' generates integers in the common id field. I prefer to use a string (e.g. acme.country.uk). This is more readable. (see naming conventions below). Both UI tools continue to use BTSXRefImport.exe.  This utility replaces all xref data. This can be a problem in continuous integration environments that support multiple clients or BizTalk target instances.  If you upload the data for one client it would destroy the data for another client.  Yet in TFS where builds run concurrently, this would break unit tests. Alternative Approach In response to these issues, I instead use simple SQL scripts to directly populate the BizTalkMgmtDb xref tables combined with a data namepacing strategy to isolate client data. Naming Conventions All data keys use namespace prefixing.  The pattern will be <companyName>.<data Type>.  The naming conventions will be to use lower casing for all items.  The data must follow this pattern to isolate it from other company cross-reference data.  The table below shows some sample data. (Note: this data uses the 'ID' cross-reference tables.  the same principles apply for the 'value' cross-referencing tables). Table.Field Description Sample Data xref_AppType.appType Application Types acme.erp acme.portal acme.assetmanagement xref_AppInstance.appInstance Application Instances (each will have a corresponding application type). acme.dynamics.ax acme.dynamics.crm acme.sharepoint acme.maximo xref_IDXRef.idXRef Holds the cross reference data types. acme.taxcode acme.country xref_IDXRefData.CommonID Holds each cross reference type value used by the canonical schemas. acme.vatcode.exmpt acme.vatcode.std acme.country.usa acme.country.uk xref_IDXRefData.AppID This holds the value for each application instance and each xref type. GBP USD SQL Scripts The data to be stored in the BizTalkMgmtDb xref tables will be managed by SQL scripts stored in a database project in the visual studio solution. File(s) Description Build.cmd A sqlcmd script to deploy data by running the SQL scripts below.  (This can be run as part of the MSBuild process).   acme.purgexref.sql SQL script to clear acme.* data from the xref tables.  As such, this will not impact data for any other company. acme.applicationInstances.sql   SQL script to insert application type and application instance data.   acme.vatcode.sql acme.country.sql etc ...  There will be a separate SQL script to insert each cross-reference data type and application specific values for these types.

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