Data Model Options for Power BI Solutions

At the heart of every a Business Intelligence reporting  solution is a data model, to optimize queries and enable ad hoc report interactions.  Data modeling technology has evolved quite a lot over the past 20 years or so.  You have several choices and options depending on the simplicity or formality of your project, and factors like data size and security.  In the past, choices were simpler.  Power BI was the choice for smallish, “good enough” projects and when data quality, high volume and exactness were the priority, SSAS was the better choice.  Now, using Power BI for modelling larger data sets is even advantageous with new features like hybrid models, aggregations and incremental data refresh.

I’ve been a quest to find the best medium to break these choices down into simple terms but it truly is a journey and not a destination.

Earlier this year, I presented a session called “The Nine Realms of Power BI” where I enumerated different categories of solution architectures for Power BI solutions; and they are numerous.  Just in the past year, so o optimize queries & support self-service reporting.  have been made – and are in the process of being added – to the Power BI platform, that the list of options and considerations continue to grow.

One important consideration is Microsoft’s commitment to support a product with new features in the future.  They have made it very clear that the Power BI platform is their primary focus and that they will continue to invest in enterprise-scale capabilities in the cloud service.  Never plan on a crucial feature being added later to a product but give serious consideration to  where a product is headed.

Making a side-by-side comparison of features between products and options is a little like comparing oranges, apples, grapes and bananas.  As a best effort, I started with the feature lists for SQL Server Analysis Services and added the Power BI variations.  Admittedly, this type of comparison doesn’t fit perfectly into this type of pivot format but I think it serves the purpose.  This post will likely evolve a bit with time.  Your feedback and input are welcome.

open/download Excel file  (site theme doesn’t currently support wide tables… working on that)

Feature
Enterprise/Developer
StandardAzure Analysis ServicesPower BI StandardPBI Report ServerPower BI PremiumComments
Max model size (compressed)No limit*16 GB No limit*1 GB2 GB10-12 GB**Premium supports 10 GB upload & 12 GB refresh.
Hybrid models
(DirectQuery & cached mode)
NoNoNoNoNoYes 
M/Power Query sources & transformationsYesYesYesYesYesYes**Query extensions in the service using dataflows
Integrated application lifecycle management (ALM) toolsYesYesYesNoNoNo 
Integrated version control toolsYesYesYesNoNoNo 
        
Tabular Models       
Programmability (AMO, ADOMD.Net, OLEDB, XML/A, ASSL, TMSL)YesYesYes*Yes*Yes*Yes*Third-party tool support, new XMLA endpoint for Power BI
HierarchiesYesYesYes**Yes*Yes*Yes**Simple hierarchies,
**AS supports HideMemberIfBlank
KPIsYesYesYesNoNoNo 
PerspectivesYes YesNoNoNo 
TranslationsYesYesYesNoNoNo 
DAX calculations, DAX queries, MDX queriesYesYesYesYesYesYes 
Row-level securityYesYesYesYesYesYes 
Multiple partitionsYes YesNoNoYes**Incremental refresh builds partitions
In-memory storage modeYesYesYesYesYesYes 
DirectQuery storage modeYes YesYesYesYes 
        
Multidimensional Models       
Semi-additive measuresYesNo 1Yes*Yes*Yes*Yes**Using DAX code,
Effort: moderate
HierarchiesYesYesYes**Yes*Yes*Yes**Simple hierarchies,
**AS supports HideMemberIfBlank
KPIsYesYesYesNoNoNo 
PerspectivesYes YesNoNoNo 
ActionsYesYes*Using 3rd party tool*Possible, limited*Possible, limited*Possible, limited 
Account intelligenceYesYesYes*Yes*Yes*Yes**Using DAX code,
Effort: high
Time intelligenceYesYesYesYesYesYes 
Custom rollupsYesYesYes*Yes*Yes*Yes**Using DAX code,
Effort: moderate
Writeback cubeYesYesNoNoNoNo 
Writeback dimensionsYes NoNoNoNo 
Writeback cellsYesYesNoNoNoNo 
DrillthroughYesYesYes*Yes*Yes*Yes**Multiple techniques
Advanced hierarchy types (parent-child and ragged hierarchies)YesYes*YesNoNoNo*Supports HideMemberIfBlank
Advanced dimensions (reference dimensions, many-to-many dimensions)YesYesYesYesYesYes 
Linked measures and dimensionsYesYes 2No*No*No*No**Equivelent functionality
TranslationsYesYesNoNoNoNo 
AggregationsYesYesYesYesYesYes 
Multiple partitionsYesYes, up to 3YesNoNoYes**Incremental refresh builds partitions
Proactive cachingYes *****In-memory model is always cached
Custom assemblies (stored procedures)YesYesNoNoNoNo 
MDX queries and scriptsYesYes*****Supports MDX queries & DAX scripts
DAX queriesYesYesYesYesYesYes 
Role-based security modelYesYesYesYesYesYes 
Dimension and cell-level securityYesYesNo*No*No*No**Equivelent functionality for measures
Scalable string storageYesYesYes*Yes*Yes*Yes**Equivelent functionality
MOLAP, ROLAP, and HOLAP storage modelsYesYesYes*Yes**cached or SSAS directYes**DirectQuery & hybrid models are equivelent or superior
Binary and compressed XML transportYesYesYes*Yes*Yes*Yes**VertiPaq in-memory compression on all data types
Push-mode processingYes YesYes*Yes*Yes**Multiple & different processing techniques supported
Direct writebackYes NoNoNoNo 
Measure expressionsYes YesYesYesYes 

Interviews with Microsoft Data Community Speakers and Leaders

What is the key to advancing your career in the Microsoft data platform?  Here is some advice from some of the most successful people in the industry…

Every year we have some big community events that bring together community leaders from all over.  These are international user group and community leaders who write books and speak at conferences. we had our local Oregon SQL Saturday “SQL Train” which is a chartered train – some coaches that one of our speaker’s chartered to bring all of our speakers and attendees up to the PASS summit after Oregon SQL Saturday, and then the big PASS summit (the big conference up in Seattle). I had a chance to sit down with a number of our speakers, community leaders and attendees and just ask questions about what brought them there, and advice that they would give people in the industry about how to get the most value out of that experience …and this is what they said:

Well-run monthly user group meetings and big annual events local events like SQL Saturday don’t just happen by themselves. It takes a lot of planning, a lot of volunteers and a lot of coordination to make these events successful. Part of that effort are the annual leadership planning meetings that we have during the week of PASS summit. Here are some short clips from those meetings where several hundred local leaders from all over the world got together to share ideas, to exchange notes and to coordinate to be able to make these events successful. Leaders cross-pollinate, exchange ideas and they work together to make this a great community. Why? …because somebody did that for us when we were getting started and we want to give back to the community. So, get involved; join the leadership committees at your local user groups, volunteer at SQL Saturday. Volunteer to do a short speaking engagement. Just get up and talk to some of your peers. Get started by volunteering in the community so that you can be part of the ongoing great community we have around the Microsoft data platform.

How to Configure the Power BI Gateway to use Dataset Connection Parameters

A service provider or vendor might want to publish multiple copies of a report that should connect to different database servers or databases.  In a true multitenant service solution, we would have a singe database with row-level user mapping tables that filter data by the logged in user.  True multitenant solutions require quite a lot of planning and development work to implement.  In smaller-scale or interim solutions, copies of a report can be deployed to different workspaces and then queries can be parameterized to use different database connections.

In this post, I’ll demonstrate deploying and configuring such a solution where the server name and database name have been parameterized and setup to use the on-premises gateway to connect and refresh data.  I’ll also setup scheduled refresh.  The full video walk-through is below but I’ll do a quick review to set the stage.

This is the Power Query Editor in Power BI Desktop.  I have two parameters that are used to specify the ServerName and DatabaseName for each SQL Server query:

Power BI Gateway with Parameters (Time 0_00_57;24)

Once deployed to a workspace in the service, the gateway must be configured with a data source for every possible server name and database combination.  In my example, I can connect to my local server using the NetBIOS name, IP address or LocalHost.  These all are acceptable methods but a data source must be added to the gateway configuration for each so the that the connection strings match exactly.  Remember that the connection is from the on-prem gateway to the database server so names like LocalHost or an internal IP address will work just fine.  In my example, I’m using the IP address of my local loopback adaptor on the local machine to connect to a local instance of SQL Server over the TCP connection.

Power BI Gateway with Parameters (Time 0_04_31;15)

In the workspace, the dataset is bound to the gateway.  Click the ellipsis and choose Settings.

Power BI Gateway with Parameters (Time 0_02_44;22)

To bind the gateway to the dataset, click to select the radio button next to the gateway.  This flips the switch titled “Use a data gateway”.  Apply the setting and then you can refresh the data or schedule refresh.

Power BI Gateway with Parameters (Time 0_05_47;00)Power BI Gateway with Parameters (Time 0_06_09;27)Finally, the parameters can be updated right here in the dataset settings.

Power BI Gateway with Parameters (Time 0_06_46;21)

 

Video Demonstration

 

Pareto, Burn-down & Accumulating Trend Charts in Power BI

I’m managing an Agile team project using Microsoft Teams – the new project management platform integrated with Office 365.  Teams is a simple and useful project management tool but it’s new and light on features.  Using Power BI, we want to show the hourly task burn-down for each two-week sprint.  In JIRA and some other more mature project management platforms, the burn-down chart is a standard feature in the tool that shows the number of hours or story points remaining, compared to the estimated number for the sprint.  Just as I began working on that, a client asked for some help creating a Pareto chart and it occurred to me that burn-down and Pareto charts are very similar variations of the same type of chart presentation.  These are not so much chart types as they are a set of calculations and techniques for displaying a desired result.

Project Hours Burn-Down Chart

Here’s the Burn-down chart showing days of the sprint on the X-axis and the hours for all resources as columns.  The burn-down line represents the number of estimated hours remaining for the duration of the sprint as a percentage of the total estimated hours for all resources.

imageHere’s another variation of the burn-down chart using stacked columns for each project resource, in my example; developers named Marta, Rob and Vivek.  Again, the burn-down line shows the daily percentage of estimated hours remaining compared to the total hours that have been “burned” for all days up to and including the current day.

image

The columns in the chart are simply the sum of hours reported.  I didn’t even create a measure for this value.  It’s just a summable column in the ‘Project Hours’ table named “Hours”.  Here’s a look at the data, including the measures used in the chart:

image

…and finally, here is the DAX measure code used for the line part of the column/line combination chart:

Project Hours Remaining % =
VAR DayAccumHours =
CALCULATE( SUM( ‘Project Hours'[Hours] ),
FILTER( ALLSELECTED( ‘Day’ ),
‘Day'[Day Number] <= MIN( ‘Day'[Day Number] )
)
)
VAR ProjectHoursRemaining = SUM( ‘Sprint Estimates'[Est Hours] ) – DayAccumHours

RETURN
DIVIDE( ProjectHoursRemaining, SUM( ‘Sprint Estimates'[Est Hours] ) )

Pareto Chart

This style of chart is very similar but the key differences are that the columns are ordered by the contribution value in descending order.   You can see that my sample dataset just uses numbers for the categories (e.g. “8”, “2”, “4”, etc.) but these could just as easily be names of resources, sales people, customers or products.  The columns are in descending order of aggregate value rather a time series of the axis field value.  To order by measure or value (my aggregate field is literally called “Value”), set the Type property for the X-axis to “Categorical”.

SNAGHTMLc49dd9d

Here’s the Pareto chart.  The contribution line shows the percentage of the total contribution that a category item and all of it’s predecessors in ranked descending order have made to that point.  For example, the first four top-ranking categories (8, 2, 4 and 5) account for about 50% of the total.

image

The DAX measure code for the accumulating percentage value used for the line, is as follows:

Accum Value % All =
VAR AccumValue =
CALCULATE( SUM(‘Pareto Series'[Value] ),
FILTER( ALLSELECTED( ‘Pareto Series’ ), ‘Pareto Series'[Value] >= MIN( ‘Pareto Series'[Value] ) )
)
VAR AllSeriesTotal = CALCULATE( SUM( ‘Pareto Series'[Value] ), ALLSELECTED( ‘Pareto Series’ ) )

RETURN
DIVIDE( AccumValue, AllSeriesTotal )

Accumulating Trend Series or Trend Chart

For good measure, I have one more example.  I actually started experimenting with calculation techniques using this chart.  It is essentially the burn-down chart example with the line presenting the opposite of the burn-down, so maybe this is a “burn-up chart” – I don’t know.  This may be useful when you want to visualize a time or other continuous series on the X-axis and the accumulating contribution as a line along with the actual contribution values as columns.

image

…and the measure DAX code used for the trend line:

Accum Series Value % All =
VAR AccumSeriesValue =
CALCULATE( SUM(‘Pareto Series'[Value] ),
FILTER( ALLSELECTED( ‘Pareto Series'[Period] ), ‘Pareto Series'[Period] <= MIN( ‘Pareto Series'[Period] ) )
)
VAR AllSeriesTotal = CALCULATE( SUM(‘Pareto Series'[Value] ), ALLSELECTED(‘Pareto Series’ ) )

RETURN
DIVIDE( AccumSeriesValue, AllSeriesTotal )

I’m hopeful that this will be useful and save you some time and energy.  As always, please leave comments and reach-out if you need some help.

You can download the example Power BI Desktop file here: Simple Pareto & Burn-down Chart Example.zip

SQL or M? – SSAS Partitions in Power Query/M

This is a continuation of this post

In the data platform industry, we have been working with SQL for decades.  It’s a powerful language and over many years, we’ve learned to work with it’s strengths and to understand and work around it’s idiosyncrasies.  M is a considerably more modern and flexible query language.  Some best practices have evolved but many are still learning the basic patterns of effective query design.  Reaching that stage with a technology often takes years of trial-and-error design and a community willing to share their learnings.  I will continue to share mine and appreciate so many in the community who share theirs.

Why Use M Instead of SQL?

For database professionals using SQL Server as the sole source of data for an SSAS or Power BI data model, there is a solid argument to be made in favor of encapsulating the query logic in database objects.  DBAs need to manage access to important databases.  A comment posted in an earlier post on this topic mentioned that SQL Server views can implement schema binding – which doesn’t allow a table or any other dependent object to be altered in such a way that it would break the view.  This is a good design pattern that should be followed if you are the database owner, have the necessary permission and flexibility to manage database objects as part of your BI solution design.  Ultimately, this is an organizational decision.  If the BI solution developer is not the DBA, you may have limited options.  If you don’t have control over the source database objects, if you are not using SQL Server or otherwise prefer to manage everything in the SSAS or Power BI project, Power Query is probably the right place to manage all the query logic.

In my earlier post, I used a table-valued user-defined function to manage the partition filtering logic in SQL Server.  Rather than using SQL and database objects, we’ll use Power Query alone.  The working M script is shown below.

For brevity, I’m starting by showing the solution but I will show you the steps we went through to get there a bit later.

Updating the Partition Definitions

The three partitions defined in the earlier example are replaced using the following M script, which returns exactly the same columns and rows as before.

image

Here is the M script for each partition:

“This week” partition:

let
Source = #”SQL/localhost;ContosoDW”,
SalesData = Source{[Schema=”dbo”,Item=”FactSalesCompleteDates”]}[Data],
#”Filtered Rows” =
Table.SelectRows( SalesData, each
[DateKey] >=
Date.StartOfWeek( DateTime.FixedLocalNow() )
)

in
#”Filtered Rows”

“This month before this week” partition:

let
Source = #”SQL/localhost;ContosoDW”,
SalesData = Source{[Schema=”dbo”,Item=”FactSalesCompleteDates”]}[Data],
#”Filtered Rows” =
Table.SelectRows( SalesData, each
[DateKey] >=
Date.StartOfMonth( DateTime.FixedLocalNow() )
and
[DateKey] <
Date.StartOfWeek( DateTime.FixedLocalNow() )
)

in
#”Filtered Rows”

“Before this month” partition:

let
Source = #”SQL/localhost;ContosoDW”,
SalesData = Source{[Schema=”dbo”,Item=”FactSalesCompleteDates”]}[Data],
#”Filtered Rows” =
Table.SelectRows( SalesData, each
[DateKey] <
Date.StartOfMonth( DateTime.FixedLocalNow() )
)

in
#”Filtered Rows”

The Power Query Litmus Test: Query Folding

When connected to an enterprise data source like SQL Server, Power Query should be able to pass an important test.  Use the Design… button to view the Power Query Editor.  Select the last query step and then right-click to show the menu.

image

If the View Native Query menu option is enabled, you are good.  This means the the query is being folded – and that’s a good thing.  Query folding converts the query steps into a native query for the database engine to execute.  This is the resulting T-SQL query script generated by Power Query:

select [_].[SalesKey],
[_].[DateKey],
[_].[channelKey],
[_].[StoreKey],
[_].[ProductKey],
[_].[PromotionKey],
[_].[CurrencyKey],
[_].[UnitCost],
[_].[UnitPrice],
[_].[SalesQuantity],
[_].[ReturnQuantity],
[_].[ReturnAmount],
[_].[DiscountQuantity],
[_].[DiscountAmount],
[_].[TotalCost],
[_].[SalesAmount],
[_].[ETLLoadID],
[_].[LoadDate],
[_].[UpdateDate]

from [dbo].[FactSalesCompleteDates] as [_]

where [_].[DateKey] >= convert(datetime2, ‘2018-07-01 00:00:00’) and [_].[DateKey] < convert(datetime2, ‘2018-07-15 00:00:00’)

You don’t need to do anything with this information.  It’s just good to know.  End of story.

And Now… The Rest of The Story

Power Query is an awesome tool that does some amazingly smart things with the simple data transformation steps you create in the designer.  However, it is important to make sure Power Query produces efficient queries.  During the development of this solution, I created an early prototype that didn’t produce a query that would fold into T-SQL.  Thanks to Brian Grant, who is an absolute genius with Power Query and M, for figuring this out (BTW, you can visit Brian’s YouTube tutorial collection here).

In my original design which I prototyped in Power BI Desktop, I thought it would make sense to create custom columns for each of the date parts needed to filter the partitions.  Here’s the prototype query for the query I originally named “This Month Thru Last Week”:

let
Source = FactSales,

#”Add DateTimeNow” = Table.AddColumn(Source, “DateTimeNow”, each DateTime.LocalNow()),
#”Change Type DateTime” = Table.TransformColumnTypes(#”Add DateTimeNow”,{{“DateTimeNow”, type datetime}}),
#”Add StartOfThisWeek” = Table.AddColumn(#”Change Type DateTime”, “StartOfThisWeek”, each Date.StartOfWeek([DateTimeNow]), type date),
#”Add StartOfThisMonth” = Table.AddColumn(#”Add StartOfThisWeek”, “StartOfThisMonth”, each Date.StartOfMonth([DateTimeNow]), type date),
#”Add StartOfPreviousMonth” = Table.AddColumn(#”Add StartOfThisMonth”, “StartOfPreviousMonth”, each Date.StartOfMonth(Date.AddMonths([DateTimeNow], -1)), type date),
#”Partition Filter” = Table.SelectRows(#”Add StartOfPreviousMonth”, each ([DateKey] >= [StartOfThisMonth] and [DateKey] < [StartOfThisWeek]) ),
#”Removed Columns” = Table.RemoveColumns(#”Partition Filter”,{“ETLLoadID”, “LoadDate”, “UpdateDate”, “DateTimeNow”, “StartOfThisWeek”, “StartOfThisMonth”, “StartOfPreviousMonth”})

in
#”Removed Columns”

As you can see, I created separate columns using Transform menu options based on the current date and time, stored in a custom column named “DateTimeNow”:

  • StartOfThisWeek
  • StartOfThisMonth
  • StartOfPreviousMonth

The rest was simple, I just added filters using these columns and then removed the custom columns from the query in the last step.  All good with one small exception… it didn’t work.  We learned that Power Query can’t use custom column values to build a foldable filter expression.  The filters just won’t translate into a T-SQL WHERE clause.

Checking the last query step with a right-click shows that the “View Native Query” menu option is grayed-out so No Folding For You!

image

Simple lesson: When query folding doesn’t work, do something else.  In this case, we just had to put the date comparison logic in the filter steps and not in custom columns.

SQL or M? – SSAS Partitions Using SQL Server Table-Valued Functions (UDFs)

[ Related posts in this series: SQL, M or DAX: When Does it Matter? SQL, M or Dax? – part 2 SQL or M? – SSAS Partitions Using SQL Server Table-Valued Functions (UDFs) SQL or M? – SSAS Partitions in Power Query/M ]

In SQL Server Analysis Services tabular projects, as of SQL Server Data Tools 2017, you can define table partitions using Power Query.  Of course, we still have the option to use SQL Server database objects like views or user-defined functions.  So, which of these two option makes most sense?  The same concepts and decision points apply to Power BI data models although the design experience is quite a bit different.

The following steps will bring us to a question: Using the new SSDT partition design method for SSAS 2017, should I define partition filtering logic in SQL or in Power Query/M?

The objective is to define three partitions in the data model for the Sales fact table in the ContosoDW database:

  • New transactions added in the current week
  • Adjusting entries for the current month
  • Historic records prior to the current month

New sales transactions in the source database needs to be refreshed in the data model every hour for reporting.  Reprocessing only the records since the beginning of the current week takes seconds to minutes.  If we schedule that partition to refresh every hour, users can have up-to-date reports throughout the day.  In addition to new transactions, adjusting records are made weekly but only to records in the current month before the end-of-month closing of the books.  Records in the current month that are older than the current week might be updated on occasion but changes don’t need to be available until the weekend.  Records older than a month rarely change and don’t need to be refreshed but once a month.  By scheduling only the first or second partition to process, data can be updated without requiring tens of millions of historical records to be reloaded.

Partitioning with a SQL User-Defined Function

I’ll step through the more conventional method we’ve been using for many years.  I’ve written the following T-SQL table-valued User-Defined Function named fnSalesPartitionForPeriod.  Three possible input parameter values allow the function to return rows for the past week, for the past month (up to the past week) or for all dates previous to the current month.

Here is the T-SQL script for a table-valued user-defined function created in SQL Server.  Passing in one of three parameter values will cause it to return the desired records.

/******************************
User-defined function used to partition Sales fact table in SSAS tabular model
@Period values:
PriorToThisMonth
ThisMonthPriorToThisWeek
ThisWeek

*******************************/

create function dbo.fnSalesPartitionForPeriod
( @Period varchar(100) )

returns table

return
select * from [ContosoDW].[dbo].[FactSalesCompleteDates]
where
(@Period = ‘BeforeThisMonth’
and
[DateKey] < dateadd(month, datediff(month, 0, getdate()), 0) ) or (@Period = ‘ThisWeek’ and [DateKey] >= dateadd(week, datediff(week, 0, getdate()), 0)
)
or
(@Period = ‘ThisMonthBeforeThisWeek’
and
[DateKey] >= dateadd(month, datediff(month, 0, getdate()), 0)
and
[DateKey] < dateadd(week, datediff(week, 0, getdate()), 0)
)

;

go

To create the three Sales table partitions using this UDF, I start by importing one table.  Here’s the Import Table dialog for the new Sales table in the data model.  I’ve selected the new UDF and entered the parameter value ‘BeforeThisMonth’ to define the first partition.

image

This part gets tricky and quite honestly, I rarely get the steps right the first time through.  I haven’t quite decided yet if my routine struggle with the SSDT Power Query editor is because I expect it work like it does in Power BI Desktop or if it truly has some quirks that catch me off guard.  Some of each, I think.  Regardless, I’m cautious to save copies of my work and if something doesn’t work, I delete the query and repeat the steps.

The query editor was smart enough to create an M function from the UDF query and this function needs to be invoked to generate the new Sales table.  Enter the parameter value once again and click the Invoke button.

image

Change the name of the new query to “Sales” and make sure that the query is set to “Create New Table”, then click the Import button on the toolbar.

image

After the table is imported, click the Partitions button on the SSDT toolbar.  As you can see, the Power Query “M” script for the Sales table calls the function and passes the parameter value I had set.  This default partition should be renamed and the other two partitions should be added using different parameter values.

image

Updating and adding the partitions is fairly simple, using these steps:

  1. Copy the original partition
  2. Rename the new partition
  3. Change the function parameter value

Rename the current partition with a friendly name.   Clicking the Copy button twice gives me two copies of the parameter.  You can see that I’ve commented the code with the valid UDF parameter values.

SNAGHTML3791b15

Now the table can be refreshed incrementally and only new transaction records for the current week or month can be updated during schedule refresh cycles.  Seeing green on this dialog is always a welcome sight:

SSAS Partition Process 2018-07-15_18-31-30

Partitioning with Power Query

No matter what the data source is; whether you use table-valued UDFs, views or in-line SQL, you are still using Power Query to define tables – so why not just use Power Query without creating database objects?

In another post, I’ll repeat the exercise using only Power Query to define the same partitions.

SQL or M? – SSAS Partitions in Power Query/M

Hands-on Workshops at the Pacific Northwest Power BI Symposium

Please join Power BI authors and community leaders for an afternoon and evening of deep learning.  Featured presenters include “Guy In A Cube” Adam Saxton and international author and Excel MVP, Matt Allington.  Deepen your skills with Power BI and get a recap from the Microsoft Business Applications Summit held in Seattle earlier in the week.

Jul 26, 2018 from 3:00 PM to 8:30 PM Portland, OR

Associated with  Portland Power BI User Group

Event Image

AGENDA –

3:00 ; check-in and registration

3:45 ; guide guests to workshops

4:00 ; workshops commence, ONLY REGISTER FOR ONE:

workshop 1 – TRISTAN MALHERBE (AZEO) – Power Query to Create a Calendar Table

register here: https://bit.ly/2K3U0lT

workshop 2 – PAUL TURLEY (CSG Pro) – Model and Visualize Financial and Accounting Data with Power BI and Excel

register here: https://bit.ly/2JOJy5v

5:00 ; workshops wrap up, seating in main event area (dinner served / networking)

5:35 ; presentation intro – RON ELLIS GAUT (CSG Pro / Portland Power BI User Group Leader)

6:00 ; presentation 1 – BRIAN GRANT (CSG Pro) – Shining a New Light on Calculate

6:45 ; presentation 2 – ADAM SAXTON (Microsoft / Guy in a Cube) – Business Applications Summit Recap

7:30 ; presentation 3 – MATT ALLINGTON (Excelerator BI) – DAX as a Query Language

8:15 ; closing remarks

8:30 PM ; event ends

Questions?  Contact Gregory Petrossian: gregp@csgpro.com for sponsorship options