MsBIP møde nr. 15

23 okt 2013 - 17:00

Jyllands-Posten, Grøndalsvej 3, Aarhus


  • Velkomst og kort intro til MsBIP
  • Præsentation af Jyllands-Posten og hvordan de arbejder med Microsoft BI
  • BIML and EzAPI – Two approaches to creating SSIS packages programmatically
  • Power BI – Self service BI in the cloud

Tilmelding foregår via indmeldelse i MsBIP møde nr. 15 gruppen på LinkedIn. Bemærk at Jyllands-Posten har forhøjet sikkerhed, hvilket betyder at tilmeldingsfristen er d. 21. oktober kl. 12:00, da der skal afleveres navneliste. Samtlige deltagere skal medbringe og vise billed ID (kørekort/pas) ved ankomst.


Beskrivelse af indlæg

BIML and EzAPI – Two approaches to creating SSIS packages programmatically (Daniel Otykier)
In this talk, we are going to explore some of the benefits and drawbacks of creating SSIS packages from code. There are basically two practical approaches to this; BIML, which is an XML-based scripting language that integrates nicely with your existing SSIS solutions through BIDSHelper; and EzAPI, which is a C# library that makes it easy to build your packages from scratch in an object-oriented fashion. We will be looking at various samples for both approaches. BIML and EzAPI are valuable time-savers when it comes to repetitive tasks, such as extracting data from multiple tables. In addition, both allow you to build completely metadata-driven SSIS solutions that are very easy to maintain and extend. Lastly, we will provide references to online resources containing useful code snippets and samples.
(This talk is intended for an SSIS-experienced audience. Programming experience is not necessary although some parts of the talk assume a basic understanding of programming concepts.)


Power BI – Self service BI in the cloud (David L. Bojsen)
In this session we will look at the new kid in town – Power BI for Office 365.
We will go thru all of the tools that are part of this offering as well as look at real world use scenarios.
Power BI will enable business users worldwide to work with and visualize their data in familiar tools, and afterwards share and collaborate around their findings.