Newsletter – Page 4

Prologika Newsletter Fall 2016

September 4, 2016/2 Comments/in Blog, Newsletter/by Prologika - Teo Lachev

Embedding Reports in Custom Applications

Last week’s seminar on formulating a Power BI enterprise strategy held at the Microsoft office was a great success. Over 50 people witnessed the amazing capabilities of the Power BI platform. As Power BI evolves, we’ll have similar events to bring you up to date. If you couldn’t attend, you can find the slides here. Or, are you looking for more in-depth Power BI training? There is still time to register for my 2-day public Power BI workshop on Sep 14-15 at the Microsoft Office in Atlanta. Reserve your seat today to attend this exclusive event for only $999 and learn practical Power BI knowledge and data analytics skills that you can immediately apply to your job.

Speaking of Power BI, do you need to embed reports in custom apps in order to bring data analytics to your customers? If so, look no further than Power BI Embedded, which Prologika has been using to help ISVs increase the value of their apps by bringing instant insights to their customers. Most applications need some reporting capabilities. Report-enabling custom applications has been traditionally challenging with the Microsoft BI platform. True, Visual Studio includes Windows Forms and ASP.NET ReportViewer controls that make it very easy to embed Reporting Services reports (also known as paginated reports). However, the chances are that you might prefer more interactive reports that can be viewed with any browser and on any device. This is where Power BI Embedded comes in. You have the app. You have the data. Now bring data to life inside your app with Power BI Embedded!

What’s Power BI Embedded?

Power BI Embedded is an Azure cloud service that help developers embed Power BI interactive reports in custom apps for a third party. Notice that I said “third party”. The current licensing model prevents you for using Power BI Embedded to distribute reports inside your organization or re-implement functionality that already exists in Power BI. Fair enough – internal users are already covered by Power BI licenses and Microsoft doesn’t want you to come up with a tool that competes with Power BI. Speaking about licensing, what’s great about the Power BI Embedded pricing model is the recent change that Microsoft made where you’re charged per a report session and not for the number of reports that are rendered or registered customers! When the user opens a report, a session is started for one hour or until the user closes the app. The first 100 sessions/mo are on Microsoft. After that you’re charged $5 for 100 sessions per month. Besides the Power BI Embedded great features, this cost-effective pricing model is why independent software vendors (ISVs) and developers are flocking to Power BI Embedded.

How are users provisioned?

If you recall, Power BI requires each user to sign up. Power BI Pro charges the user $9.99 per month (based on feedback from customers, they don’t pay the sticker price because they either acquire Power BI through the Office 365 E5 plan and/or have a discounted price). If you have an app that potentially can be accessed by thousands of users, the Power BI pricing model is not cost effective. By contrast, Power BI Embedded leaves it up to the custom app to authenticate the user. So there is no provisioning you have do. However, although Power BI Embedded uses the Power BI infrastructure, don’t expect the Power BI reports to show up when you log in to Power BI. In other words, Power BI Embedded and Power BI don’t share datasets and reports. Consequently, at the least for now, Power BI Embedded is limited to embedding reports only. No Q&A, no quick insights, no portal, and no other Power BI goodies. Another limitation is that at least for now Power BI Embedded is limited to report viewing only. Users can’t edit the reports, such as to add or remove fields.

What data sources are supported?

One what to acquire data is to import your data in Power BI Desktop. When you import data, you’re limited of 1 GB of compressed data, which is the same dataset limitation that Power BI currently has. Because of the excellent compression (x5-10 ration), you can still pack a lot of data into a 1 GB dataset. For example, you can create a separate extract for each customer if you have a limited number of customers or you need to provide a high degree of customization for each customer. Another option is to connect live to cloud data sources. Because Power BI Embedded is an Azure cloud service, naturally it works with Azure-resident PaaS data sources, such as Azure SQL Database and Azure SQL Data Warehouse.

How does security work?

Power BI Embedded uses OAuth to authorize your users with Power BI. Your application authenticates the user as it would normally do. If the user has access to reports, your application would login to the Power BI Embedded using a special access key (think of it as a password). When the user requests the report, the application generates a token that Power BI validates to grant access to the report. If you need to support row-level security (RLS), Power BI Embedded get you covered too! You can define your security roles and row filters in Power BI Desktop. Then, your application can pass the user login (it doesn’t have to be on your domain) and what role(s) the user belongs to. The net result is that the user can see only the data the user is authorized to see.

How customizable is Power BI Embedded?

The Power BI Embedded customization story got even better with the introduction of the JavaScript APIs. They allow you to programmatically access the report object model on the client, such as to react to page change events, pass filters, or hide the report filter pane. For example, in the screenshot above, the application has its own page navigation and filter pane that replaces the Microsoft-provided filter pane. This gives the application more control about validating and passing visual, page, or report-level filters.

How do I get started?

Microsoft has provided an excellent ASP MVC sample to get you started. Check also my “Configuring Power BI Embedded” blog for a better configuration experience

MS BI Events in Atlanta

Prologika: 2-day Applied Power BI public workshop in Atlanta by Teo Lachev on Sep 14-15
Atlanta BI Group: “Power BI at one year – a look back and a look forward ” presentation by Siva Harinath on 9/26
Atlanta Code Camp 2016: A free training event for developers on October 15th. I’ll have a presentation on Power BI Embedded.

As you’d probably agree, the BI landscape is fast-moving and it might be overwhelming. If you need any help with planning and implementing your next-generation BI solution, don’t hesitate to contact me. As a Microsoft Gold Partner and premier BI firm, you can trust us to help you plan and implement your data analytics projects, and rest assured that you’ll get the best service.

Regards,

Teo Lachev
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

Prologika Newsletter Summer 2016

June 19, 2016/0 Comments/in Blog, Newsletter/by Prologika - Teo Lachev

Why Choose Power BI as BI Platform

Are you deciding which BI platform to adopt? With so many vendors and products, you are not alone and the choice is far from easy. For the past few months, I’ve consulted with and mentored several large publicly traded companies to help them understand the benefits of Power BI and teach their staff on how to get the most out of it. Speaking of Power BI and large organizations, Prologika added a new Power BI case study to the Power BI partner showcase that demonstrates why the world’s largest logistic company decided to standardize on a single platform and adopt Power BI. Last but not least, if you are in Atlanta, visit our Atlanta MS BI group which is now the only official local Power BI group. So, if you’re serious about Power BI, check our monthly meetings which now feature plenty of Power BI content.

In this newsletter, I’ll enumerate the most important advantages of Power BI that make it stand out from the rest of the competing platforms. Also, I’ll mention some areas my customers have identified where Power BI has left a room for improvement.

Overall

In this section, I’ll review some general implementation and cost considerations that in my opinion make Power BI a very compelling choice.

Data Platform – No matter how good it is, a self-service visualization tool addresses only a small subset of data analytics needs. By contrast, Power BI is a part of the Microsoft Data Platform that allows you to implement versatile solutions and use Power BI as a presentation layer. Want to implement a real-time dashboard from data streams? Azure Stream Analytics and IoT integrates Power BI. What to show reports on the desktop from natural questions? Cortana lets you do it by typing questions or voice. Want to implement smart reports with predicted results? Power BI can integrate with Azure Machine Learning? Want to publish SSRS and Excel reports? Power BI supports this. Expect this strength to increase as Cortana Analytics Suite and prepackaged solutions evolve.
Cloud First – I know that many of you might disagree here as on-premises data analytics is currently more common, but I see the cloud nature of Power BI as an advantage because allows Microsoft to push out new features much faster than the typical 2-year major release cadence of other vendors. Recall that Power BI Service is updated on a weekly basis while Power BI Desktop is on a monthly release cadence. And because Power BI is a cloud service, it supports the versatile integration scenarios I mentioned before.
Cost – It’s hard to compete with a freemium pricing model. Implementing a BI solution has been traditionally cost prohibitive. However, now Power BI Desktop is free, Power BI Mobile apps are free, Power BI Service is mostly free. If you need the Power BI Pro features, Power BI is packaged with the Office 365 E5 plan, it has an enterprise license, and I’ve heard customers get further discounts from Microsoft.

Next, I’ll review specific Power BI strengths for different user types.

Business Users

By “business users”, I’ll mean information workers that don’t have the necessary skills or desire to create data models.

Content packs and Get Data – Basic data analytics needs can be met in Power BI without modeling. For example, if the user is interested in analyzing data from Salesforce, the user can use the Salesforce content pack and get predefined reports and dashboards. Further, the user can create their own reports from the dataset included in the content pack. What if your cloud data sources have a lot of data and content packs are impractical? Microsoft is rolling out pre-configured scalable solutions (currently, Salesforce and Dynamics CRM).
Productivity features – Power BI has several features that resonate very well with business users. Q&A allows users to ask natural questions, such as “sales last year by country”. Power BI interprets the question and shows the most suitable visualization which the user can change if needed. Within 20 seconds, Quick Insights applies machine learning algorithms that help business users perform root cause analysis and to get insights that aren’t easily discernible by slicing and dicing, such as to find why profit is significantly lower in December. Such productivity features are missing in competing products.

Data Analysts

Data analysts (power users) are the primary audience for self-service BI. Power BI excels in the following areas:

Data shaping and transformations – Source data is rarely clean. Excel Power Query and Power BI Desktop queries allow the data analysts to perform a variety of basic and advanced data transformations. For example, these features could help the data analyst shape the data without staging it first to a relational database.
Sophisticated data models – Power BI offers much more advanced modeling experience where a data analyst can build a self-service model on a par with semantic models implemented by BI pros. For example, the model can have multiple fact tables and conformed dimensions. Power BI supports one-to-many and many-to-many relationships.
Powerful programming language – The Data Analysis Expressions (DAX) excels any other vendor’s programming language.

BI and IT Pros

Besides the ability to integrate Power BI to implement synergistic solutions, pros can build hybrid solutions:

Hybrid solutions – Want to get the best of both worlds: always on the latest visuals while leaving data on premises? Power BI lets you connect to your data on premises.
Semantic layer – Many organizations are considering a semantic layer to achieve a single-version of the truth. If your staff is experienced in Power BI modeling, you’ll find that they already have 80-90% of the knowledge they need to implement a Microsoft-based semantic layer with Analysis Services Tabular. This gives you a nice continuum from self-service to organizational BI. For more information about why a semantic layer is important, read my newsletter “Why Semantic Layer?”.

Developers

Developers has much to gain from the Power BI open extensible architecture.

Custom visuals – Power BI allow developers implement custom visuals which can be optionally shared to Power BI Visuals Gallery.
Extensibility – Power BI has a comprehensive REST API that allow developers to integrate Power BI with custom apps. For example, Power BI let developers push data into datasets for real-time dashboards and manipulate deployed objects programatically. Power BI Embedded, currently in preview, allows developers to embed interactive reports without requiring installation of tools and with very attractive licensing model.

Improvement Areas

Here are some areas that customers have identified where Power BI needs improvement:

Direct Query – Currently, Direct Query is limited to a single data source. Microsoft should extend Direct Query to support multiple data sources within a single model.
File size limits – Power BI Service (powerbi.com) is currently limited to 1 GB maximum file size. Some customers have indicated that their data analysts require larger file extracts. My personal advise has been that such large extracts should be avoided in favor of a centralized semantic layer.
DAX complexity – Customers have expressed concerns about the DAX complexity that the lack of quick calculations. Microsoft has already started addressing this by adding the Percent of Total quick calculations. Expect other calculations to light up shortly.
Variables – One large organization transitioning from Qlik/Tableau has pointed out that Power BI lacks variables and parameters, such as to dynamically bind several visualizations to a measure that is chosen as a parameter.
Exporting reports and datasets from Power BI Service to Power BI Desktop – This is currently not supported but high on the Power BI roadmap.
Predictive capabilities – Currently, besides using R or integrating with Azure Machine Learning, Power BI doesn’t have native forecasting capabilities, such as to forecast future months. This is a frequently requested feature and very high on the roadmap.
Drillthrough limitations – Different drillthrough options in Power BI has different limitations. For example, exporting to CSV is limited to 10,000 rows, while drilling through a chart data point is limited to 1,000 rows. This limits will probably lifted in Power BI Pro. Meanwhile, you can use the Analyze in Excel feature and drillthrough in Excel pivot reports which gives you an option to drill through 1,048,576 rows.
Data reduction algorithms – Currently, Power BI visualizations employ data reduction algorithms to limit the number of data points to plot. This is high on the roadmap and there is a work underway to address this limitation.
Replacement for paginated reports – Some organizations have hoped that Power BI can be a replacement of other vendors’ products for paginated (pixel-perfect) reports. Power BI reports are designed for quick data exploration and not as paginated reports. However, SSRS is the Microsoft product for paginated reports. Moreover, SSRS 2016 has been extended significantly to fulfill a very important role in the Microsoft on-premises reporting roadmap.
Maturity – I often hear that Power BI is great but it’s not mature. In my opinion, you should view Power BI to be as mature (or even more mature) as other tools. That’s because the Power BI building blocks have been around for many years, including xVelocity (the in-memory data engine where imported data is stored), Power Query, Power Pivot, Power View, Tabular, and Azure cloud infrastructure.

MS BI Events in Atlanta

Atlanta BI Group: “Cloud, cloud, cloud…and Azure” presentation by Julie Smith on 6/27/2016
Atlanta BI Group: “20 minute Tabular Model” presentation by Bill Anton on 7/25/2016
Atlanta BI Group: “SSRS 2016 Mobile Reporting” presentation by Patrick LeBlanc on 8/29/2016

Regards,

Teo Lachev
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

Prologika Newsletter Spring 2016

March 13, 2016/1 Comment/in Blog, Newsletter/by Prologika - Teo Lachev

What’s New in SQL Server 2016 for BI?

On a personal note, I’m excited to announce the launch of the new Prologika website (https://prologika.com), which adds a slew of new features to connect better with customers and readers, including site-wide search, responsive web design, case studies, book and blog discussion lists, and more to come. Although the old blog feed should still work, please update it to https://www.prologika.com/feed/. Continuing on the list of announcements, Microsoft added a Prologika Power BI case study to the Power BI partner showcase. Speaking of Power BI, I definitely see a lot of interest from customers in Power BI-based solutions, ranging from self-service BI to white-labeling and report embedding. Last but not list, our Atlanta MS BI group is an official Power BI group! So, if you’re interested in Power BI, check our monthly meetings which now feature more Power BI content.

Spring is here and it brings again a new version of SQL Server. Microsoft launched SQL Server 2016 on March 10^th. Its product page include nice videos covering some of the new features. The great news is that the “box” has seen a renewed interest and Microsoft has made significant investments in all the bundled services to help you implement cost-effective and modern data analytics solutions on premises. In this newsletter, I’ll enumerate my favorite BI new features in SQL Server 2016. Feel free to also check my slides on this topic on my LinkedIn profile page.

Tools

The days of waiting years for the next SQL Server release are coming to an end, as you first witness with the client tools.

SSMS – You no longer have to run the SQL Server setup just to get SQL Server Management Studio (SSMS). SSMS is now available as a free and standalone download here. Moreover, it will be updated on a monthly basis and it will be backward compatible for all SQL Server supported versions!
SSDT – Also, to everybody’s delight, the BI add-on to SQL Server Data Tools (SSDT) is gone. Instead, you just download and install SQL Server Data Tools, which includes the BI projects. No more installing three setup packages to get to the BI stuff. To make your joy complete, SSDT is backward compatible. Actually, SSRS and SSAS have been backward compatible for a while, but now SSIS joins the list so that you can use SSDT to work with legacy SSIS packages.

Database Engine

There are many new features in the Database Engine but the following will be of particular interest to BI practitioners:

Updatable columnstore indexes – They will allow you to speed up aggregated queries without having to drop and recreate the columnstore index.
Live query statistics – How many times you had to troubleshoot the performance of massive query with many joins? Live query statistics will now show you which joins slows the query down.
Temporal tables – Anyone who’s implemented ODS knows that maintaining Type 2 changes is no fun. Temporal tables can maintain changes on any column for you. This feature is also great if you need data change auditing.
Integration with R – Leveraging the Revolution Analytics acquisition, the R Server allows your data analysts to run R scripts on top of the SQL Server data. Moreover, DBAs can configure resource limits so that these scripts don’t impact the database performance.

SQL Server Integration Services (SSIS) and Master Data Services (MDS)

I’m somewhat disappointed that the Power Query integration and Lineage Statistics didn’t make the cut. Anyway, here are my favorites:

Incremental project deployment – you can just deploy changed packages to the catalog instead of deploying the entire project.
Package parts – you can refactor some control flow tasks in reusable package parts that you can manage separately. This could be very beneficial for SSIS “frameworks” so that you don’t have to update all packages if some changes are introduced later in the development cycle.
Cloud tasks and connectors – Lots of attention to moving and transforming data in Azure. For example, there is a task that will allow you to move data to Azure Blog storage in the most efficient way. Continuing this line of thought, the fastest way to move the data to Azure SQL DW would be to use Polybase which supports HDInsight and Azure Blob Storage.
MDS Entity Sync – Allows you to reuse entities among models. For example, you can implement a Common model with entities, such as Geography, that you can configure for auto synchronization with other models.
15x performance increase in MDS Excel add-in.

SQL Server Reporting Services (SSRS)

As per the Microsoft’s updated reporting roadmap, SSRS comes out of the closet to fulfill its new role of becoming the on-premises platform for paginated (pixel-perfect), mobile, and Power BI Desktop reports (support for Power BI Desktop files in SSRS will happen after SQL Server 2016). SSRS saw a lot of attention in SQL Server 2016 and brings major new enhancements:

Better mobile support – SSRS reports now render in HTML5. Users can use the Power BI native apps for iOS, Android and Windows devices to render both SSRS and Power BI reports. ActiveX print control has been replaced with PDF printing that works on all modern browsers.
Facelift – SSRS 2016 brings a new report portal (aka Report Manager). Report Builder has a new look too. Charts and gauges have a new modern look. New chart types (Sunburst and Treemap) have been added. You can now add KPIs directly in the Report Portal.
Mobile reports – Thanks to the Datazen acquisitions, you can now have in the box reports that specifically target mobile devices, that have similar features as competing vendors, such as PushBI (now part of Tibco) and RoamBI.
Parameter area – You can now control the parameter placement. Personally, I expected also more control over parameters, such as parameter validation, but the alas, the wait is not over.
Prioritized native report mode – Microsoft now prioritizes SSRS in native mode which is a great news for customers who previously had to adopt SharePoint Enterprise just for BI. In fact, all the new features are available only in SSRS native mode.

SQL Server Analysis Services (SSAS)

As you know by now, I’m a big fan of classic BI solutions that feature a semantic layer (Multidimensional or Tabular). SSAS gets many new features, including:

Tabular many-to-many relationships – You can now implement M2M relationships by setting the relationship cross filtering direction to Both, as you can in Power BI Desktop.
Tabular Direct Query enhancements – Microsoft put a lot of effort to lift previous Direct Query limitations in Tabular so that you can build Tabular models on top of fast databases without having to cache the data. Direct Query now have better performance, support for row level security, support for MDX clients such as Excel, support for Oracle, Teradata, and Azure DW.
New Tabular scripting language – Tabular models are now described in a new lightweight JSON grammar. This speeds up scheme changes, such as renaming columns. In addition, a new Tabular Object Model (TOM) is introduced to help developers auto-generate Tabular models.
DAX new functions – Many new DAX functions (super DAX) were introduced.
Multidimensional – support for Power BI and Power BI Desktop. Support for Netezza as a data source. Distinct count ROLAP optimization for DB2, Oracle, and Netezza. Drillthrough is now supported with multi-selection, such as then the user filters on multiple values in Excel.

MS BI Events in Atlanta

Atlanta BI Group: “Azure Data Factory” presentation by Luis Figueroa on 4/25/2016
Atlanta BI Group: “What’s New in SQL Server 2016” presentation by Paco Gonzalez on 5/23/2016
SQL Saturday Atlanta #521: A free training event for all things SQL Server on May 21^st.

Regards,

Teo Lachev
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

Prologika Newsletter Winter 2015

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

Power BI and You

book1 I’m excited to announce the availability of my latest (7th) book – Applied Microsoft Power BI! Currently, this is the only book on Power BI. The book page has more information about the book, including the front matter (with foreword by Jen Underwood), source code, and a sample chapter (Chapter 1 “Introduction to Power BI”). You can order the paper copy on Amazon, and soon on other popular retail channels. I predict that 2016 will be the year of Power BI and I hope that this book will help you to take the most out of it and bring your data to life! And if you’re looking for instructor-led training on Power BI and Microsoft BI, please check our training classes.

Let’s face it. Without supporting data, you are just another person with an opinion. But data is useless if you can’t derive knowledge from it. And this is where Power BI can help you. While writing the book and helping customers use Power BI, I’m astonished by its breath of features and the development momentum Microsoft has put behind it. The Power BI cloud service gets major features every week, while Power BI Desktop is updated every month! Although this makes it hard for people like me who are writing books, it’s a great value proposition for you.

Not to mention that Power BI has the best business model: most of it it’s free! Power BI Desktop and Power BI Mobile are free. Power BI Service is free and has a Power BI Pro subscription option that you could pay for, following a freemium model. Cost was the biggest hindrance of Power BI, and it’s now been turned around completely. You can’t beat free! In this newsletter, I’ll revisit how Power BI can benefit different users in your organization.

Power BI for Business Users

To clarify the term, a business user is someone in your organization who is mostly interested in consuming BI artifacts, such as reports and dashboards. Business users can utilize Power BI to connect to popular cloud services, such as Salesforce, Marketo, Google Analytics, Dynamics CRM, and many more. With a few clicks, a business user can use content packs to connect to cloud data and gain insights from predefined reports and dashboards, and create custom reports. Other cloud-hosted providers build profitable businesses around SaaS cloud BI but Power BI does it for free!

With a few clicks, business users can analyze data from files and cubes without having to create data models. And they can also view Power BI dashboards and reports on mobile devices so they are always informed while they are on the go. Again, all of this for free!

Power BI for Data Analysts

A data analyst or BI analyst is a power user who has the skills and desire to create self-service data models. Leveraging the Microsoft’s prior investment in Power Pivot, Power View, and Power Query, Power BI lets business analysts import data form virtually everywhere and create sophisticated self-service models whose features are on a par with professional models and cubes. And now that we have a native support for many-to-many relationships, there shouldn’t be a requirement you can’t meet with Power BI.

As a data analyst you have a choice about your toolset because you can create models in both Excel or in Power BI Desktop. While other vendors charge hefty licensing fees for desktop modeling tools, Power BI Desktop is free and it gets updates every month! Think of Power BI Desktop as the unification of Power Pivot, Power Query, and Power View. Previously available as Excel add-ins, these tools now blend into a single flow. No more guessing which add-in to use and where to find it! Because many data analysts use R to data analysis and statistics, Power BI recently added support for R scripts and visualizing data using the R plotting capabilities.

Power BI for Pros

BI pros and IT pros have much to gain from Power BI. An IT pro can establish a trustworthy environment that promotes sharing of BI artifacts. To do so, IT can set up Power BI workspaces that allow authorized users to see the same BI content. If IT needs to distribute BI artifacts to a wider audience, such as the entire organization, she can create an organizational content pack and publish it to the Power BI Content Gallery. Then her coworkers can search, discover, and use the content pack. And IT can set up an organizational gateway to centralize and grant access to on-premises data.

The scenario that BI pros will probably be most excited about is hybrid BI solutions, where the report definitions (not data) is hosted in Power BI but corporate data remains in relational databases and cubes. This is a scenario that Prologika is planning for a cloud-averse Fortune 10 company in order to empower their users with mobile reports and dashboards. But that’s not all! BI pros can also implement predictive and real-time solutions that integrate with Power BI, and book has the details.

Power BI for Developers

Power BI has plenty to offer to developers as well because it’s built on an open and extensible architecture that embraces popular protocols and standards, such as REST, JSON, and oAuth. For years, Microsoft didn’t have a good solution for embedding interactive reports in custom apps. Power BI enables this scenario by allowing developers to embed dashboard tiles and interactive reports. Soon it will also support custom authentication.

Microsoft has also published the required “custom visuals” interfaces to allow developers to implement and publish custom visuals using any of the JavaScript-based visualization frameworks, such as D3.js, WebGL, Canvas, or SVG. Do you need visualizations that Power BI doesn’t support to display data more effectively? With some coding wizardry, you can implement your own, such as the Sparkline visual that I published to the Power BI visuals gallery!

In summary, no matter what data visualization or data analytics requirements you have, Power BI should be at the forefront and you ought to evaluate its breath of features. Remember that Power BI is a part of a holistic vision that Microsoft has for delivering cloud and on-premises data analytics. When planning your on-premises BI solutions, consider the Microsoft public reporting roadmap. Keep in mind that you can use both Power BI (cloud-based data analytics) and the SQL Server box product on-premises to implement synergetic solutions that bring your data to life!

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

EVENTS & RESOURCES

Prologika: “Applied Microsoft Power BI Service” book by Teo Lachev
SQL Saturday BI: “What’s New for BI in SQL Server 2016” presentation by Teo Lachev and “Introduction to R” presentation by Neal Waterstreet on 1/9/2016
Atlanta BI Group: Power BI presentation by Patrick LeBlanc on 1/25/2016

Prologika Newsletter Fall 2015

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

Is ETL (E)ating (T)hou (L)ive?

Before we get to the subject of this newsletter, I’m happy to announce the availability of my latest class – Applied Power BI Service. As you’ve probably heard by now, Power BI Service (or Power BI 2.0) is the Microsoft latest cloud-based analytics service with a simple promise: 5 seconds to sign up, 5 minutes to wow! If you’re ready to disrupt how your organization is analyzing data, please contact me to schedule this class at your convenience and get immediate value.

What Not To Do

Back to the newsletter subject, let’s start with a story that was inspired by true events as they say in the movies. Not a long time ago, a man wanted a nice and modern house for his family. He hired a couple of well-known builders but they didn’t deliver what the man really wanted. Then, the man hired a third builder who built him a great house (or a close approximation of the grand vision). Everyone was happy and they lived happily ever after…or at least until the man sold the house to another man.

The second owner had more pressing needs and another vision about the house. Not only the house had to accommodate his family but now the house had to entertain hordes of guests so it had to be expanded. But to cut down cost, the second man decided to maintain the house on his own or outsource whatever he can’t do to a cheap builder. The new owner put hastily new rooms and did other renovations as necessary. Expansion and new construction were his highest priorities and there was never time for proper maintenance or to reinforce the house infrastructure so that it can accommodate the new demands. Needless to say, not much time had passed until the infrastructure gave up. For example, it took days for clogged pipes to drain and guests were not happy. Did I mention the man sold his guests the sun and the moon?

What does this have to do with Extraction, Transformation, and Loading (ETL)? Data is rapidly growing nowadays while ETL processing windows are shrinking. You must do more with less. And, ETL usually becomes a performance bottleneck that stands in the way of your current and future BI initiatives

What To Do

How did the story end? The story didn’t end and it will never end. If you have a house, you can just focus on renovations and additions. You must also maintain it and you must budget for it. One day a member of the man’s family did something out of ordinary and the entire infrastructure collapsed. There wasn’t a way to find out why and the family was scurrying around trying to apply quick fixes. Finally, the second man hired hastily the original builder to assess the situation. Among other things that the builder did to resolve the crisis, he recommended changes and proactive maintenance along the following ten tenets:

Parallelism – The chances are that you have an ETL framework that orchestrates package execution, log errors, etc. And, the chances are that the framework executes packages sequentially. With all the bandwidth modern servers have, there is no excuse if your framework doesn’t support parallel execution. That’s because many ETL tasks, such as ODS loads, loading dimensions and independent fact tables, can benefit greatly from parallel execution. For example, at Prologika we use ETL framework that supports a configurable number of parallelism. Once you configure which packages can run in parallel, the framework distributes the packages across parallel flows.
Incremental extraction – If you have small data volumes, you might get away with fully loading the source data but most systems would require incremental extraction. Again, this is something the ETL framework is best suited to handle.
Volume stats – ETL must log in important data volume metrics, such as number of rows extracted, inserted, updated, and deleted. It should also load how many days were processed since the last incremental extraction and additional context that might be useful for troubleshooting purposes, such as what parameters were passed to stored procedures.
Targeted execution – I recommend you add a target execution duration for each package. Then, ETL will log in the actual duration so that you can detect performance deviations from the norm.
Daily monitoring – I suggest you implement and publish a dashboard, such as using Excel Power Pivot, and monitor this dashboard daily. For example, the dashboard should include a Package Execution KPI that flags excessive executions in red based on the performance metrics you established in step 4.
Regression analysis – Once things “normalize”, create an one-time Extended Events session (assuming SQL Server) to capture the query plans for all significant queries. If during daily monitoring you discover a performance deviation, run the session again focusing on that slow package and compare the query plan with the baseline. Analyze both query plans to find if and why they have changed. To make this easier, when SQL Server 2016 ships, consider upgrading to take advantage of the new Query Store feature.
Cold data archiving – If you lots of source data, e.g. billions of rows, consider archiving historical data that no one cares about, such as by uploading to Azure Table storage.
Project deployment – Consider upgrading to SSIS 2012 or above to benefit from its project deployment so that you can get task-level performance analysis in addition to easier development.
Avoid locking – Use “SET TRANSACTION ISOLATION LEVEL READ UNCOMMITED” at the beginning of your stored procedures of freeform SQL to avoid read locks. I prefer using this statement instead of the NOLOCK hint for its brevity and so that I don’t miss a table.
ELT pattern – I saved the best for last. I’m a big fan of the ELT pattern. I usually try to get out as fast as I can from the SSIS designer. Instead of transformations in the ETL data flow, consider ETL pattern for its performance and maintenance benefits. For more information about the ELT pattern, read my blog “3 Techniques to Save BI Implementation Effort.

As you’d probably agree the BI landscape is fast-moving and it might be overwhelming. As a Microsoft Gold Partner and premier BI firm, you can trust us to help you plan and implement your data analytics projects.

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

EVENTS & RESOURCES

Prologika: Applied Power BI Service training by Prologika (online or instructor-led):
Atlanta BI Group: Enhancing Data Analysis and Predictive Analytics with NoSQL by Cornell A. Emile on September 28th
Atlanta BI Group: ETL Architecture Reusable Design Patterns and Best Practices by Stephen Davis on October 26th

Prologika Newsletter Summer 2015

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

What’s New for BI in Office 2016?

office

While you might be still trying to convince management to upgrade to Office 2013, the next version of MicrosoftOffice (Office 2016) is on the horizon and scheduled to be released sometime in the Fall. Meanwhile, you can download and test the Public Preview. Naturally, you might wonder what’s new for BI given that Excel is the Microsoft premium BI desktop tool. In this newsletter, I’ll share my favorite Excel 2016 BI features (most significant listed first).

Power View Connectivity to Multidimensional Cubes

I don’t know why it took more than two years for Microsoft to add this feature (initially released for Power View in SharePoint), but you will be finally able to use Power View in Excel 2016 to connect to OLAP cubes, just like we can use Power View in SharePoint as a front end to cubes. What this means to you is that you can preserve your OLAP investment by allowing business users to use Power View to build dashboardsand perform ad-hoc data analytics.

Native Power Query Integration

If you follow the Power Query story, you know that it has an increasingly popular place in the Microsoft BI strategy. In fact, all data roads in Power BI 2.0 go through Power Query because of its integration with Power BI Designer. Besides not having to go through extra steps to download and install Power Query, the native integration allows you to manipulate programmatically Power Query objects using VBA, C#, or PowerShell code and record macros, as explained in more details in this document.

Continuing down the integration path, you can now easily enable all “Power” features from File > Options > Advanced > Turn on data analysis features. In the past you had to enable add-in separately using File- >Options >Add-Ins > Manage “Com Add-ins” > Go.

Power Pivot Improvements

As you probably know, your business users can use Power Pivot to build sophisticated self-service BI models that are on a par with organizational models. Moving forward to Office 2016, the Power Pivot models will support many-to-many relationships. Unfortunately, this feature didn’t make it to the Office 2016 public preview but when released it should work in the same way as in the Power BI Designer. Continuing further down the list, you can now rename tables, columns and calculated fields/measures in the Power Pivot add-in and they will automatically propagate to Excel and existing reports. In another words, metadata changes are not breaking changes to existing reports anymore.

And it gets easier to define relationships. When you are building a Data Model PivotTable working with two or more tables with no relationships defined, you get a notification to run Automatic relationship detection. This will detect and create all relationships between the tables that are used for the Data Model PivotTable so that you don’t have to do any of the work yourself. DAX added many new functions which are documented here.

Usability Improvements

Another long anticipated feature made the cut. The Excel Field List added a Search feature to allow the end user to filter names of tables, columns, and measures. This works across all data structures: Power Pivot, Tabular, and Multidimensional.
Similar to native pivot tables, pivot reports connected to Power Pivot models now support Time grouping. For example, this feature allows the end user to group a report at a day level to any time interval, e.g. year and then quarter.

Similar to native pivot tables, pivot reports connected to Power Pivot models now support Time grouping. For example, this feature allows the end user to group a report at a day level to any time interval, e.g. year and then quarter.

Excel regular slicers now add a multi-select option so that the user doesn’t need to remember to hold down the Ctrl key to select multiple items.

slicer

Forecasting Functions

Recognizing the need for time series forecasting, Excel adds forecasting functions, which are documented here. For example, the Forecast.ETS function predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm. In the example below, Forecast.ETS calculates the forecasted value for the date in cell O16, using the historical values in the range P5:P15 across the dates in the time series from the range O5:O15).

=FORECAST.ETS(O16, P5:P15, O5:O15)
The function also supports arguments also for confidence interval and seasonlity.

As you’d agree, the BI landscape is fast-moving and it might be overwhelming. As a Microsoft Gold Partner and premier BI firm, you can trust us to help you plan and implement your data analytics projects.

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

EVENTS & RESOURCES

Atlanta BI Group: Microsoft and Revolution Analytics: What’s the Add-Value? by Dr. Mark Tabladillo on June 29th
Atlanta BI Group: Sentiment Analysis with Big Data & Machine Learning by Paco Gonzalez on August 31th

Prologika Newsletter Spring 2015

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

dw1 A while back I met with a client that was considering overhauling their BI. They asked me if the traditional data warehousing still makes sense or should they consider a logical data warehouse, Big Data, or some other “modern variant”. This newsletter discusses where data warehousing is going and explains how different data architectures complement instead of compete with each other.

QUO VADIS DATA WAREHOUSE?

The following diagram illustrates the “classic” BI architecture consisting of source data, data staging, data warehouse, data model, and presentation layers (click to enlarge the image).

Yes, not only is the data warehouse not obsolete, but it plays a central role in this architectural stack. Almost always data needs to be imported, cleansed and transformed before it can be analyzed. The ETL effort typically takes 60-80% percent of BI implementation effort. And, currently there isn’t a better way to store the transformed data than to have a central data repository, typically referred to as a “data warehouse”.

That’s because a data warehouse database is specifically designed for analytics.
Ideally, most or even all of dimensional data should be centrally managed by business users (data stewards) outside the data warehouse. However, the unfortunate truth is that not many companies invest in master data management. Instead, they kick the data quality can down the road and pay much higher data quality later but this is a topic for another newsletter. A well-thought architecture should also include a data model (semantic) layer, whose purpose and advantages I’ve outlined in my “Why Semantic Layer?” newsletter.

BIG DATA

Irrespective of the huge vendor propaganda surrounding Big Data, based on my experience most of you still tackle much smaller datasets (usually in the range of millions or billions at worst). This is not Big Data since a single (SMP) database server can accommodate such datasets. This is a good news for you because Big Data typically equates big headaches. For those of you who truly have Big Data, its implementation should complement, instead of replace, your data warehouse. Even though the advancements in the popular Big Data technologies are removing or mitigating some of the Big Data concerns, such as slow queries, these technologies are still “write-once, read many”, meaning that they are not designed for ETL and data changes.

Moreover, a core tenant of data warehousing is providing a user-friendly schema that supports ad-hoc reporting and self-service BI requirements. By contrast, BI Data is typically implemented as a “data lake” where data is simply parked without any transformation. For more information about Big Data and how it can fit into your BI architecture, read my Big Data newsletter.

LOGICAL DATA WAREHOUSE

Recently, logical data warehouses (LDW) have gained some traction and backing from vendors, including Composite (acquired by Cisco), Denado, and others. Logical data warehousing is also known as data federation and data virtualization. The idea is simple – consolidate and share the data in a controlled manner to all users and applications across the enterprise. Data is made available as virtual views on top of existing data sources, with additional features, such as discovery and caching. Again, the goal here is not to replace the traditional data warehouse, but make its data, plus the data from other systems, readily available for self-service BI and/or custom applications.

Logical data warehousing is at a very early stage of adoption. In my opinion, the companies that will benefit most of it are large organizations with many data repositories, where data availability is a major barrier for enabling self-service BI. If you believe that your organization might benefit from a Logical Data Warehouse, you might not need to make a major investment. If your organization has an Office 365 Power BI subscription, your first step could be leveraging the Power Query capabilities for dataset sharing and discovery. This process can work like this:

Designated users promote virtual views in the form of Power Query queries to Office 365.
A data steward verifies and approves these datasets.
Once signed in to Office 365, other users can search, preview these queries, and import the associated data in self-service BI models.

The following snapshot shows how a business user can search and preview a published query that returns Product List data.

One caveat is that these shared queries are currently Power Query-specific and they can only consumed by Microsoft self-service BI tools, which currently include Excel and Power BI Designer. I recommended to Microsoft to expose shared queries as ODATA feeds to enable additional integration scenarios.

SELF-SERVICE BI

Self-service BI allows power users to create their own data models by using specialized tools, such as Power Pivot, Tableau, QlikView, and so on. Despite what some “pure” self-service BI vendors proclaim, self-service BI is not a replacement for data warehousing. In fact, a data warehouse is often a prerequisite and a major enabler for self-service BI. That’s because a data warehouse provides consumers with clean and trusted data that can be further enriched with external data

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

EVENTS & RESOURCES

PASS Data Analytics Conference, April 20-22, Santa Clara, California
Atlanta BI Group: Overview of R by Neal Waterstreet on March 30th
Friendlier Data Profiling with the SSIS Data Profiler Task by Julie Smith on April 27th
SQL Saturday Atlanta on May 16th

Prologika Newsletter Winter 2014

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

powerbi After an appetizer of embedded Power View reports , Microsoft proceeded to the main course that is a true Christmas gift – a pubic preview of what’s coming in its Power BI offering. For the lack of a better term, I’ll call it Power BI.NEXT. In this newsletter, you’ll see why Power BI.NEXT is much more than just an incremental release of the Office 365 Power BI, as you might have thought after reading the Microsoft announcement. As a participant in the private preview, I’m really excited about the new direction and the capabilities it opens.

WHY POWER BI.NEXT?

What do the United States of America and Microsoft BI have in common? They both decided to become independent for the welfare of their community. USA by choice. Microsoft BI by market conditions.

After more than a decade working with BI technologies and a variety of customers, I do believe that Microsoft BI is the best BI platform on the market. However, you and I both know that it’s not perfect. One ongoing challenge is that various product groups have had a stake in Microsoft BI since Microsoft got serious about BI (circa 2004). Microsoft management promoted SharePoint as a platform for sharing BI artifacts. Major effort underwent to extend SharePoint with SSRS in SharePoint integration mode, PerformancePoint, Power Pivot, etc. But the hefty price tag of SharePoint and its complexity barred the adoption of BI on premises. Power BI for Office 365 alleviated some of these issues but many customers find its kitchen sink approach too overwhelming and cost-prohibitive if all they want is the ability to deploy BI artifacts to the cloud.

Similarly, seeking more market share and motivation for customers to upgrade, Excel added Power Pivot and Power View and was promoted as the Microsoft premium BI tool on the desktop. But adding new BI features to Microsoft Office turned out to be a difficult proposition. A case in point – it’s been two years since DAXMD got released and Power View in Excel 2013 still doesn’t support Multidimensional. In addition, because self-service BI was added to Excel later on, business users find it difficult to navigate the cornucopia of features. For example, there are at least three ways to import data in Excel (Excel native data import capabilities, Power Pivot, and Power Query).

Meanwhile, new self-service BI players entered the marketplace which was previously dominated by mega players. Although lacking technologically in their offerings, they gain in agility, simplicity, vendor neutrality, and aggressive pre-sales support. It appears that these market conditions caused a major shift in the Microsoft BI cloud vision that culminated in POWER BI.NEXT.

WHAT’S DIFFERENT?

In my opinion, the main difference between Power BI.NEXT and Power BI for Office 365 is that Power BI.NEXT removes the Excel, SharePoint, and Office 365 adoption barriers. On the desktop, it introduces the Power BI Designer – a new BI design tool that unifies Power Query, Power Pivot, Power View and Power Map in a single environment that runs completely outside Excel (Excel BI features are still available for Excel users). Power BI Designer targets business users willing to create self-service data models and dashboards. Powered by Power Query, the designer simplifies data acquisition from a plethora of data sources.

Similar to Power Pivot, Power BI Designer imports data (it seems that the preview version always imports data) into the xVelocity in-memory store backed up by a file in a zip format for durable storage (*.pbix file extension). For reporting, it uses integrated Power View reports that now render by default in HTML5 (if you inspect the page source of a deployed dashboard you’ll see that it uses jquery, angular, and the rest of the mobile dev stack) and natural Q&A (first introduced in Power BI for Office 365).

Another welcome addition is new visualizations that have been demonstrated in many public events, including funnel charts, tree maps, gauges, and others. Although the designer still doesn’t support this feature, the deployed dashboards demonstrate navigation to other report views for more detailed analysis. Unfortunately, action-based drillthrough and conditional formatting are not supported at this point).

Yet another great feature is the ability to connect dashboards deployed to the cloud to on-premise organizational Tabular models (support for Multidimensional is expected at a later time). This is great news for Office 365 customers who aren’t willing to move their data to the cloud yet but are OK with having reports in Power BI. This is accomplished by installing a new bridge connector called Analysis Services Connector Preview that runs independently from the Power BI Data Management Gateway which you might have used to refresh Power Pivot models deployed to Power BI for Office 365.

Another indication of the management shift that reflects market realities is the initial focus on native mobile applications for iPad (already available on the Apple Store) and Android.

Last but not least, always catering to developers, Microsoft has big plans. The Power BI Developer page describes the initial REST APIs that are currently in preview. I’m particularly interested in these APIs that will allow us to “integrate rich analytics into your application by embedding Power BI dashboards, tiles and reports” so we don’t have to rely on third-party controls.

WHAT’S NOT TO LIKE?

Although I’m very excited about Power BI.NEXT, there are some important features lacking in the preview. Currently, it’s a cloud-only offering and you can’t deploy it on premise. I hope we don’t have to wait for another management shift for this to happen given that the majority of my customers are not ready for the cloud yet.

Continuing down the list, the preview customization capabilities of Power View are still limited. To meet dashboard requirements more effectively, we need at least basic customization, such as conditional formatting and action-based drillthrough (as supported in SSRS).

As I mentioned previously, the ability to embed reports on web pages, such as to provide customer-facing reporting, is a must too. I hope the new Power BI will be more agile and add these features soon. Meanwhile, give your feedback and vote for the features you need!
As you’d agree, the BI landscape is fast-moving and it might be overwhelming. As a Microsoft Gold Partner and premier BI firm, you can trust us to help you plan and implement your data analytics projects.

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Data Analytics

EVENTS & RESOURCES

“Microsoft BI 2014 Review” presentation by Teo Lachev
Applied Excel and Analysis Services e-learning course by Teo Lachev
Atlanta BI Group: “Success in Business Intelligence requires Emotional Intelligence” by Javier Guillen on Jan 26th

Prologika Newsletter Fall 2014

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

According to Gartner, one main data analytics trend this year and beyond will be predictive analytics. “Increasing competition, cost and regulatory pressures will motivate business leaders to adopt more prescriptive analytics, making business decisions smarter and more repeatable and reducing personnel costs”. Prologika has been helping our customers implement predictive analytics for revenue forecasting, outlier detection, and train data scientists in predictive analytics. This newsletter introduces you to Azure Machine Learning.

WHAT IS AZURE MACHINE LEARNING?

Microsoft unveiled the public preview of Azure Machine Learning (previously known as project Passau) in July 2014. Your humble correspondent has been participating in the private Preview Program to learn it firsthand and provide feedback. Microsoft Azure Machine Learning is a web-based service for cloud-based predictive analytics. You upload your data to the cloud, and then define datasets and workflows to create “experiments”. As you would recall, you can create organizational data mining models using the SQL Server Analysis Services and Excel data mining capabilities. Microsoft Azure Machine Learning to organizational DM models is what Power Pivot to Analysis Services is in a sense that it democratizes predictive analytics. It targets business users willing to create predictive models. The following figure shows a simple experiment to predict the customer’s likelihood to purchase a product.

HOW DOES IT WORK?

The process of creating an experiment is simple:

The user subscribes to the Azure Machine Learning service. For the pricing details of the public preview service, see this page (be aware that with all cloud-based offerings, everything is subject to change). Currently, the user is charged 38 cents per hour to use the service.
The user defines the input data by uploading a file. Supported file formats include CSV, tab-delimited, plain text, Attribute Relationship File Format, Zip, and R Object or Workspace. In addition, Azure Machine Learning can connect to Azure cloud data sources, such as HDInsight or Azure SQL Server.
The user creates an experiment which a fancy term for a workflow that defines the data transformations. For example, the user might want to filter the input dataset or use just a sample of the input data. In my case, I used the Split data transformation to divide the input dataset into two parts: one that is used for training the model, and the second one that is used for predictions.
The user selects a Machine Learning algorithm(s) that will be used for predictions. In this case, I used the Two-Class Boosted Decision Tree algorithm. This is where users will need some guidance and training about which mining algorithm to use for the task at hand.
The user trains the model at which point the model learns about the data and discovers patterns.
The user uses a new dataset or enters data manually to predict (score) the model. For example, if the model was used to predict the customer probability to purchase a product from past sales history, a list of new customers can be used an input to find which customers are the most likely buyers. The Score Model task allows you to visualize the output and to publish it as a Web service that is automatically provisioned, load-balanced, and auto-scaled, so that you can implement business solutions that integrate with the Web service.

WHY SHOULD YOU CARE?

Implementing predictive solutions has been traditionally a difficult undertaking. Azure Machine Learning offers the following benefits to you BI initiatives:

It simplifies predictive analytics by targeting business users.
As a cloud-based offering, it doesn’t require any upfront investment in software and hardware.
It promotes collaboration because an experiment can be shared among multiple people.
Unlike SQL Server data mining, it supports workflows, similar to some high-end predictive products, such as SAS.
It supports R. Hundreds of existing R modules can be directly used.
It supports additional mining models that are not available in SQL Server Analysis Services and that came from Microsoft Research.
It allows implementing automated business solutions that integrate with the Azure Machine Learning service.

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Business Intelligence

EVENTS & RESOURCES

Atlanta BI Group: Atlanta BI Group: DAX 101 on September 29th
Atlanta BI Group: Atlanta BI Group: Predictive Analytics From Theory to Practice on October 27th

Prologika Newsletter Summer 2014

March 8, 2016/0 Comments/in Newsletter/by Prologika - Teo Lachev

powerbi During the Day 1 keynote at the 2014 BA Conference, Microsoft committed to BI on your terms. They said that all new features will be eventually available both on premises and in the cloud. We’ve also learned that hybrid scenarios will be possible in near future, such as hosting Power View and SSRS reports in the cloud connected to on-prem data sources. Based on my coversations with customers, many people are confused which path (on-prem or cloud) to take. I hope this newsletter clarifies.

BI ON YOUR TERMS

The following table should help you understand how the Microsoft on-prem and cloud BI offerings compare at a high level (click to enlarge the image). Choosing between on-prem and cloud BI is essentially a decision about where SharePoint would be installed.

options

Self-service and Team BI

Microsoft markets Power BI as “self-service BI with the familiarity of Office and the power of the cloud”. It’s important to clarify however that the self-service BI tools (Power Pivot, Power View, Power Query, and Power Map) that are shown in green in the diagram below are implemented as add-ins to Excel and thus are available to no additional cost for Excel users.

In other words, you don’t need an Office 365 or Power BI license to use these tools. You can purchase Office from a retailer, such as Amazon, and have all of the Excel “power” components:
Power Pivot: Integrated with Excel 2013 (needs to be downloaded for Excel 2010)
Power Query: Needs to be downloaded for both Excel 2010 and 2013
Power Map: Needs to be downloaded for Excel 2013 (doesn’t support Excel 2010).
Power View: Integrated with Excel 2013 (not available for Excel 2010
but available in SharePoint Server integrated with SQL Server 2012)

The orange box on the right is the SharePoint cloud infrastructure that enables team BI for exploring and sharing BI artifacts. You can think of it as an upgrade to Office 365 that is available on a subscription basis. For example, while Office 365 (Midsize Business, E3, E4) allows Excel workbooks with pivot and Power View reports to be rendered online, it limits the workbook size currently to 30 MB (a more detailed Office 365 feature breakdown is available online).By contrast, Power BI supports models up to 250 MB in size and on-prem SharePoint supports file sizes up to 2 GB in size. Note that the cloud data size limits are likely to change upwards. So, the model size is one factor to consider when planning your team BI environment.

Office Click-to-Run

Most corporate Office installations are performed by downloading and running the MSI installer. The MSI setup is a perpetual one (you pay for a version once and you’re entitled to fixes for it). If you have an Office 365 subscription, you can install the Click-To-Run version of Office. This is a subscription-based Office 365 setup. You continuously pay for using the software and you’re entitled to fixes and the latest features within the O365 plan you’re subscribed to. I wrote more about the Office C2R setup in this blog.

Organizational BI

At this point, both Office 365 and Power BI are all about self-service and team BI delivered with Excel workbooks that embed Power Pivot modes. The typical scenario is that a power user would create a BI model and reports on the desktop and share these artifacts by deploying to the cloud. The rest of the BI tools that are typically used for organizational BI (SSIS, SSRS, SSAS, PerformancePoint Services) are not yet available in the cloud.

As I mentioned in the beginning, at the BA conference Microsoft said that they plan to host SSRS and Power View in the cloud in near future to enable hybrid scenarios. Meanwhile, the only way to build a MS cloud-based organizational solutions would be to deploy them to Azure virtual machines, with the exception of SQL Server Database Engine and Reporting Services which as available as Platform as a Service (PaaS) Azure offerings.

Additional Features

As an upgrade to Office 365, Power BI has additional features that are currently only available in Power BI, such as Q&A, data stewardship, and so on. I discussed these features in more details in my previous newsletter. Microsoft plans to release new BI features first to Power BI and then to on-prem SQL Server and SharePoint. An example of such a feature is Power View Time Series Forecasting which just got released to Power BI.

As you’d agree, the BI landscape is fast-moving and it might be overwhelming. As a Microsoft Gold Partner and premier BI firm, you can trust us to help you plan and implement your BI projects.

Regards,

Teo Lachev

Teo Lachev
President and Owner
Prologika, LLC | Making Sense of Data
Microsoft Partner | Gold Business Intelligence

EVENTS & RESOURCES

Atlanta BI Group: Microsoft BI for Financial Reporting by Justin Stephens on June 30th
Atlanta BI Group: Integrating Data Mining by Mark Tabladillo on July 28th
Atlanta BI Group: Making Your SSIS Fly Beyond the Sky by Carlos Rodriguez on August 25th

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