SQLRally Amsterdam 

Business Intelligence (BI) Sessions

10 Tips and Tricks for Better SSIS Performance (Level 300)
David Peter Hansen
Your SQL Server Integration Services packages are taking longer than they should, but you are not sure what to do about it. Join this session to get 10 tips and tricks for gaining better performance. You’ll not only learn 10 reasons why your packages are running slow, but also 10 things to do about it. Find out when to use T-SQL instead of SSIS transformations, why you should care about data types, what a blocking transformation is, why buffers are important, and more.

Advanced Analytics in Excel 2013 (Level 300)
Dejan Sarka
Excel is “the” analytical tool in Microsoft’s suite for advanced analytics. You can use it as a SQL Server Analysis Services client or as a standalone analytical engine. With Excel, you can analyze both relational and unstructured big data. With PowerPivot, Excel becomes a single-user Analysis Services in tabular mode, and the performance of your analytical processing is amazing. With Power View inside Excel, you can create ad-hoc reports on the tabular model with minimal effort. And with the Data Mining add-in, you can perform even the most advanced analysis on your Excel data. This session will review the business intelligence capabilities of Excel 2013 and 2010 and then focus on data mining with Excel.

Analyzing Data with Power View (Level 100)
Jen Stirrup
Come learn about the best ways to present data to your Business Intelligence data consumers, and see how to apply these principles in Power View, Microsoft's data visualization tool. Using demos, we will investigate Power View based on current cognitive research around data visualization principles from such experts as Stephen Few, Edware Tufte, and others. We will then examine how data can be analyzed with Power View and look at where Power View is supplemented by other parts of the Microsoft Business Intelligence stack.

Automating DWH Patterns Through Metadatan (Level 300)
Davide Mauri
Around 80% of the work to create a data warehouse/BI solution is spent on the ETL phase. Although building an ETL solution can be a challenge, you can break down the project into at least two separate processes for easier management. One process is strictly related to business modeling, and therefore cannot be replicated. But the other is made up of purely technical processes that are always the same, regardless of the business environment we operate in, and thus can be highly automated.

In this session, we will look at well-known patterns to solving common problems and how they can be automated with the help of specific tools and techniques that use metadata to reduce development time and bugs. Using these engineering techniques, you will be able to adopt an Agile approach to your BI solution.

Big Data (Level 300)
Marcel Westra
Is Big Data hype or something that can truly make a difference for your organization? The intention of this session is to inspire and give you new ideas about what is possible in the world of data. We will start with a story about the origin of Big Data and continue with a model of how you can think about Big Data. Using this model, we will cover all the Big Data functionality available on the Microsoft platform, including Polybase, Power BI, Azure SQL Database, and Hadoop. We will examine these different technologies through demos and real-life examples.

Business Intelligence Authentication Jungle (Level 300)
Alessandro Recino
Claim, Kerberos, EffectiveUserName, Secure store. If you work with Business Intelligence solutions, either natively or integrated with SharePoint, sooner or later you will face some decisions around authentication. In this session, you will learn the do's and don’ts and how and when to configure the appropriate authentication method for Microsoft BI services.

Choosing Between Tabular and Multidimensional (Level 300)
Hans Geurtsen
OLAP functionality has been part of SQL Server since the introduction of OLAP Services in SQL Server 7.0. However, Analysis Services still is not used as much as you might expect for a feature freely available to all SQL Server customers for over a decade. SQL Server 2012 has now added a new option to make building and deploying BI solutions easier through Analysis Services: tabular model. In this session, you will learn the differences between this new model and the “old” multidimensional model. We will look at the BI Semantic Model, which is the foundation of both, and take a look under the hood at the xVelocity Engine, which makes tabular model impressively fast. Of course, you will also see tabular model in action and learn how easy it is to build a tabular model Analysis Services database.

Clustered Columnstore - Deep Dive (Level 400)
Niko Neugebauer
When it comes to clustered columnstore indexes, you may already understand row groups, delta stores, and compression methods, but come see how clustered columnstore indexes work with locking and blocking and when using different compression methods and techniques. We will also dive deep into Dictionaries creation and different methods for ETL processes.

Data Mining: The Art of Unknown Business Intelligence (Level 300)
Paul te Braak
Data mining is a technique used to derive previously unknown information from large amounts of data. The process of this knowledge discovery can help uncover new patterns that lie within the data and help analysts better understand the data they are looking at. Additionally, once these patterns have been defined, you can use them as part of predictive modeling to predict the likelihood of some event occurring.

SQL Server Analysis Services includes a data mining engine that can be used at various levels within an organization, from analyst to developer. This session will look at all facets of data mining within Analysis Services by explaining what data mining is and how it can be used across the organization. We will look at both simple and complex implementations of data mining that allow new information to be extracted from data and used in novel ways.

Hadoop (Level 300)
Henk van der Valk & Jan Pieter Posthuma

Everybody is talking about Hadoop, but what can you do with it? As organizations collect more and more data, they can turn to multiple solutions in the Microsoft stack – including Hadoop and PDW - to store, query, and analyze large amounts of data. But which is better or easier to use for your situation? And how to they compare? In this demo-rich session, learn the basics of Hadoop, how Microsoft has combined the worlds of Hadoop and SQL Server via PolyBase in PDW, and how to use Hadoop data directly in SQL Server PDW via External Tables.

Inside xVelocity Engine (Level 400)
Alberto Ferrari
The xVelocity engine in SQL Server Analysis Services 2012 Tabular is a columnar database capable of incredible performance, both in speed and compression ratio. In this session, we will dive deep into the internals of the database architecture, discovering how Vertipaq stores information and gaining better insights into the engine and the best way to model your data warehouse to leverage VertiPaq’s features. We will also look at common and useful techniques to increase the compression ratio and obtain better performance from your Tabular data model.

M vs. P vs. R (Level 300)
Oliver Engels & Julian Breunung
The new Power Query Excel add-in is a great data exploration tool, especially combined with other add-ins such as PowerPivot, Power View, and Power Map. This session will look in-depth at the language behind Power Query: "M." We will examine the language concepts and capabilities and compare them with other cool kids in town: "P" (for Python) and "R," THE language for statistical computing and data mining. And you will learn the possibilities these languages offer, where you can best use them, and about their integration with the Microsoft stack. You will walk away from this demo-driven session ready to start working with these three languages.

Master Data Services Best Practices (Level 300)
Steve Simon
Master Data Services (MDS) has come a long way since its inception in SQL Server 2008. In the meantime, we have gathered a vast wealth of best practices knowledge from practical production environments. In this session, we will look at best ways to model MDS entities and discuss the design decisions that you should consider to ensure efficient and effective solutions.

Optimizing Data Models in Tabular and PowerPivot (Level 400)
Alberto Ferrari
Is your new Tabular solution performing at its peak? Are you using best practices to reduce memory footprint, increase query speed, and get the most out of the new engine? In this session, we will look at several techniques that can really make a difference in your Tabular solution. We will cover distinct count reduction, join optimizations, condition consolidation, pros and cons of normalized data models, and selection of columns to store in the database. We will also highlight best practices, as well as those that were best practices for Multidimensional models but became worst practices for Tabular models. As I like to say: If you know Multidimensional, you need to forget it to let the new Tabular concepts shape your model.



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