Skip to main content

SQL Server 2012 Data Quality Services

One of the hardest parts of a DBA’s job, is keeping the data quality of the databases good over time. You would expect that only correct data is entered into the system, but we are all humans, so mistakes happen…

A powerful tool that  can help your DBA’s detect and prevent data quality issues is Microsoft SQL Server 2012 Data Quality Services (DQS). DQS is a knowledge-driven solution that provides both computer-assisted and interactive ways to manage the integrity and quality of your data sources. DQS enables you to discover, build, and manage knowledge about your data. You can then use that knowledge to perform data cleansing, matching, and profiling. You can also leverage the cloud-based services of reference data providers in a DQS data-quality project.

DataQualityProject

From MSDN:

DQS provides the following features to resolve data quality issues.

  • Data Cleansing: the modification, removal, or enrichment of data that is incorrect or incomplete, using both computer-assisted and interactive processes. For more information, see Data Cleansing.

  • Matching: the identification of semantic duplicates in a rules-based process that enables you to determine what constitutes a match and perform de-duplication. For more information, see Data Matching.

  • Reference Data Services: verification of the quality of your data using the services of a reference data provider. You can use reference data services from Windows Azure Marketplace DataMarket to easily cleanse, validate, match, and enrich data. For more information, see Reference Data Services in DQS.

  • Profiling: the analysis of a data source to provide insight into the quality of the data at every stage in the knowledge discovery, domain management, matching, and data cleansing processes. Profiling is a powerful tool in a DQS data quality solution. You can create a data quality solution in which profiling is just as important as knowledge management, matching, or data cleansing. For more information, see Data Profiling and Notifications in DQS.

  • Monitoring: the tracking and determination of the state of data quality activities. Monitoring enables you to verify that your data quality solution is doing what it was designed to do. For more information, see DQS Administration.

  • Knowledge Base: Data Quality Services is a knowledge-driven solution that analyzes data based upon knowledge that you build with DQS. This enables you to create data quality processes that continually enhances the knowledge about your data and in so doing, continually improves the quality of your data.

The following illustration displays the DQS process:

DQS Process

 

A good article on how to get started, can be found here.

Popular posts from this blog

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

Azure DevOps/ GitHub emoji

I’m really bad at remembering emoji’s. So here is cheat sheet with all emoji’s that can be used in tools that support the github emoji markdown markup: All credits go to rcaviers who created this list.