DQS allows domain experts, often referred to as subject matter experts (SMEs) or data stewards, to improve the quality of data. This is good because it lifts the load from developers and DBAs who are rarely experts in the format and valid values of business data. However, that is not to say DBAs and developers can't use it; they can, and to great effect if they know the data, and now DQS can help achieve this. DQS allows the data steward to create, refine and use reference data, as well as associate reference data with operational input data, whose quality needs to be improved. It produces a set of output data in the form of a new dataset, either in a table or in a CSV file. It does not alter the existing operational data, that is left to the DBA or developer to decide how best to handle, and is not a part of DQS. So, why would you want to use DQS? Why not just manually cleanse the data, either by directly editing it or updating it with T-SQL? By using DQS, SMEs can easily become involved and leverage their business knowledge to improve the quality of the data during the data cleansing exercise. DQS combines human and machine intelligence and provides the ability to easily and manually review the data cleansing output with very little effort.
If you have done some work in the world of extract, transfer, and load (ETL) processes, then you’ve run into the proverbial crossroads of handling bad data. The test data is staged, but all attempts to retrieve a foreign key from a dimension table result in no matches for a number of rows. This is the crossroads of bad data. At this point, you have a fi nite set of options. You could create a set of hand-coded complex lookup functions using SQL Sound-Ex, full-text searching, or distance- based word calculation formulas. This strategy is time-consuming to create and test, complicated to implement, and dependent on a given language, and it isn’t always consistent or reusable (not to mention that everyone after you will be scared to alter the code for fear of breaking it). You could just give up and divert the row for manual processing by subject matter experts (that’s a way to make some new friends). You could just add the new data to the lookup tables and retrieve the new keys. If you just add the data, the foreign key retrieval issue is solved, but you could be adding an entry into the dimension table that skews data-mining results downstream. This is what we like to call a lazy-add. This is a descriptive, not a technical, term. A lazy-add would import a misspelled job title like “prasedent” into the dimension table when there is already an entry of “president.” It was added, but it was lazy.
W hen we, the authors of this book, first learned what Microsoft’s plans were for Analysis Services in the SQLServer2012 release, we were not happy. Analysis Services hadn’t acquired much in the way of new features since 2005, even though in the meantime it had grown to become the biggest-selling OLAP tool. It seemed as if Microsoft had lost interest in the product. The release of PowerPivot and all the hype surrounding self-service Business Intelligence (BI) suggested that Microsoft was no longer interested in traditional corporate BI, or even that Microsoft thought profes- sional BI developers were irrelevant in a world where end users could build their own BI applications directly in Excel. Then, when Microsoft announced that the technology underpinning PowerPivot was to be rolled into Analysis Services, it seemed as if all our worst fears had come true: the richness of the multidimensional model was being aban- doned in favor of a dumbed-down, table-based approach; a mature product was being replaced with a version 1.0 that was missing a lot of useful functionality. Fortunately, we were proven wrong and as we started using the first CTPs of the new release, a much more positive—if complex—picture emerged.
implement a change tracking solution, allowing future analysis or assessment of data changes for certain business entities. A readily accessible example is the change history on a customer account in a CRM system. The options for implementing such a change tracking system are varied and each option has strengths and weaknesses. One such implementation that has been widely adopted is the use of triggers to capture data changes and store historical values in an archive table. Regardless of the implementation chosen, it was often cumbersome to develop and maintain these solutions. One of the challenges was incorporating table structure changes in the table being tracked. It was equally challenging creating solutions to allow for querying both the base table and the archive table belonging to it. The intelligence of deciding whether to query the live and/or archive data can require some complex query logic.
Once an attacker has gained adequate privileges on the SQLServer, they will then want to upload “binaries” to the server. Since this can not be done using protocols such as SMB, since port 137-139 typically is blocked at the firewall, the attacker will need another method of getting the binaries onto the victim’s file system. This can be done by uploading a binary file into a table local to the attacker and then pulling the data to the victim’s file system using a SQLServer connection.
Microsoft SQLServer is a complete suite of tools that include an RDBMS system, a multidimensional OLAP and tabular database engines, as well as other services, for example a broker service, a scheduling service (SQL Agent), and many more. As discussed in Chapter 1, Introduction to Big Data and Hadoop, it has become extremely important these days to integrate data between different sources. SQLServer also offers a powerful business intelligence stack, which provides rich features for data mining and interactive reporting. One of these BI components is an extract, transform, and load (ETL) tool called SQLServer Integration Services (SSIS). SSIS offers the ability to merge structured and un-structured data by importing Hive data into SQLServer and apply powerful analytics on the integrated data. Throughout the rest of this chapter, we will get a basic understanding on how SSIS works and create a simple SSIS package to import data from Hive to SQLServer.
Not long ago, the information technology (IT) group for a large ﬁ nancial services company wanted to make sure that they were using the best reporting tool on the market. They decided to hire a consulting company to evaluate every major reporting product and give them an unbiased analy- sis. I was lucky to land this assignment. We worked with the client to identify about 50 points of evaluation criteria. Then I contacted all the major vendors, installed evaluation copies and explored features, and spoke with other customers and with those who specialized in using these various products. It really helped us see the industry from a broad perspective and was a valuable learning experience. There are some respectable products on the market, and all have their strengths, but I can honestly say that Microsoft has a unique and special platform. As a consultant, contractor to Microsoft, and Microsoft SQLServer MVP, I have had the opportunity to work alongside the Reporting Services product team for many years. They have a vision, and they’re passionate about their product. I have a great deal of respect for the ﬁ ne people who continue to develop and improve Reporting Services, version after version.
A fter learning about the logical configuration of a data warehouse schema, you need to use that knowledge in practice. Creating dimensions and fact tables is simple. How- ever, using proper indexes and partitioning can make the physical implementation quite complex. This chapter discusses index usage, including the new Microsoft SQLServer2012 columnstore indexes. You will also learn how to use table partitioning to improve query per- formance and make tables and indexes more manageable. You can speed up queries with pre-prepared aggregations by using indexed views. If you use your data warehouse for que- rying, and not just as a source for SQLServer Analysis Services (SSAS) Business Intelligence Semantic Model (BISM) models, you can create aggregates when loading the data. You can store aggregates in additional tables, or you can create indexed views. In this chapter, you will learn how to implement a data warehouse and prepare it for fast loading and querying.
Static, printed reports may be an acceptable format for a list of products and prices or for a company, but not for the majority of the information people use to make important decisions today. Business decision makers need pertinent information, and they need to view it in a manner that applies to that person's role or responsibility. Since most users deal with information in a slightly different manner, you can create hundreds of reports, each designed for a specific need. Alternatively, you can create flexible reports that serve a broader range of user needs. For example, a sales summary report could be grouped or filtered by the sales person's region, by customer type, and include information for the week, month, quarter or year, or for a specific product category. To produce individual reports for each of these needs would be time-consuming and cost prohibitive. Besides, computer users are savvier than they were a few years ago and need to have tools that help them take informed decisions, not just look at the numbers.
The development of technology makes things easier to human. As we know, the development of technology bring an impact in every life aspect, including education aspe t. Now, e a i atio for test stude t’s a ilit is still ei g used s ince a long time ago. To be able to make it more efficient and fast, Aplikasi Ujian were made. Theories used are C#, SQLServer 2008 R2, ERD, DFD, UML.
ity at the database level . It maintains two copies of the database on instances of SQLServer running on separate servers . Typically, the servers are hosted in separate geographic locations, not only ensuring HA, but also providing DR. If you want to incorporate automatic failover, you must include a third server (witness) that will change which server is the owner of the da- tabase. Unlike with AlwaysOn, with database mirroring you cannot directly read the second- ary copy of the database . You can, however, create a snapshot of the database for read-only purposes . The snapshot will have a different name, so any clients connecting to it must be aware of the name change . Please note that this feature has been deprecated and replaced by AlwaysOn; therefore, going forward, you should use AlwaysOn instead of database mirroring.
Needs of the information system up to date database server encourage providers to build systems with high availability levels in line with demand for the performance of a system that is 24 hours of continuous work. Provider database server will think anyway impact and barriers in the provision of services such as damage to hardware or software. Therefore, Microsoft in this case the Microsoft SQLServer2012 has a feature AlwaysOn failover cluster that is a solution to high availability inserver database where if one database server malfunction will directly occur over the functions to servers other database so that the system keeps running properly.This research aims to design a network system in the server database as a solution to overcome the failure of servers using Windows Server2012 operating system and to support the needs of the installed SQLServer2012 also features that support AlwaysOn.
Syukur Alhamdulillaahi rabbil ‘alamin terucap atas ke hadirat ALLAH SWT dan atas segala limpahan Kekuatan-Nya sehingga dengan segala keterbatasan waktu, tenaga, pikiran dan keberuntungan yang dimiliki penulis, akhirnya penulis dapat menyelesaikan Skripsi yang berjudul “PERFORMANCE TEST REPLIKASI DATABASE MS SQL SQERVER KE POSTGRESQL” tepat pada waktunya.
Perancangan Aplikasi Performance Test Replikasi SqlServer Ke Postgre Sql berbasis web. Aplikasi ini bertujuan untuk menguji penelitian sebelumnya yang membahas tentang Implementasi Replikasi Database Microsoft SqlServer – Postgresql untuk penerapan Single Sign On (SSO). Pengerjaan tugas akhir sebelumnya menitik beratkan kepada proses replikasi Ms SqlServer ke PostgreSql. Selanjutnya akan dilakukan analisa dan pengujian hasil replikasi Ms SqlServer ke PostgreSql dengan menggunakan parameter kecepatan dan waktu untuk melihat proses masukknya user ke database Ms SqlServer dan mereplikasikan ke database PostgreSql.
The snapshot database starts relatively small, but, as changes are made to the mirror database, the original data pages are added to the snapshot database to provide the data as it appeared when the snapshot was created. After copying the original data pages over time, the snapshot database could become quite large, but never larger than the size of the original database at the time the snapshot was created (see Chapter 8 for details). To keep the data current and the size of the snapshot to a minimum, you should refresh the snapshot periodically by creating a new snapshot and directing the traffic there. Then you can delete the old snapshot as soon as all the open transactions have completed. The snapshot database will continue to function after a failover has occurred; you will only lose connectivity during the failover, while the databases are restarted. A failover could, however, place an additional load on the production system. Since it is then used for processing the data for the application and serving up reporting requests, you may need to drop the snapshots and suspend reporting until another server is brought online.
One caveat when ordering by unselected columns is that ORDER BY items must appear in the SELECT list if SELECT DISTINCT is speciﬁed. That’s because the grouping operation used internally to eliminate duplicate rows from the result set has the effect of disassociating rows in the result set from their original underlying rows in the table. That behavior makes perfect sense when you think about it. A deduplicated row in a result set would come from what originally were two or more table rows. And which of those rows would you go to for the excluded column? There is no answer to that question, and hence the caveat.