In the previous blog in this series, we learned how to pass parameters to Web Intelligence. In this blog, let us look at Script Objects and learn to implement them in an analytic application.

Script objects can be a group of script functions that are used to implement a certain functionality or logic. They can be reused within the application. You can also pass parameters to a script function and get results accordingly.

Let us consider a scenario where you have four charts – Top 10 Super Commodities, Sectors, Entities and Spend Trend. On making selection on any of the first three charts, the selection should be filtered on the Spend Trend chart. Let us learn how script objects can be useful to achieve the functionality shown below.

Adding a Script Object and Script Function

1. A new script object can be added from the left panel using the plus icon.

2. By default, a script function ‘function1’ is created.

3. A pop up opens where you can configure the function.

4. Let us enter the name as ‘Func_Filter’. You can also appropriate description.

5. This function does not need to return any value. So, select Return Type as void.

Script Function Arguments

1. The script function should take two input arguments.

2. Add one argument of type Chart.

3. And another one of a type String array.

4. Once the arguments are added click Done.

Adding Script

Click on the function icon on the left side panel to open the Script Editor.

You can use the script function to copy filters from one chart to another where the name of the chart and dimension is passed as arguments. With the use of APIs copyDimensionFilter() you can replicate the filter conditions. Here the trend chart is filtered according to the filter values available in the main chart.

Calling Script Function

Once the script has been set for the script function, it can be executed /called from any widget. Here the script function needs to be called whenever the results of the chart widgets on the top have changed.

In the onResultChanged event of all the three chart components call the script function by passing two parameters – chart name and dimension name array. Func_Filter has two input arguments – GV_Chart and GV_Dimension – of type Chart and String array.

Add the script to other charts as well.

Now you can run the application and test the scenario.

Reach out to us here today if you are interested in evaluating if SAP Analytics Cloud is right for you.

The post SAP Analytics Cloud – Application Design Series 15 – Script Objects appeared first on Visual BI Solutions.

Looker is a web-based analytic solution that provides real-time operational insights. It can connect with any SQL databases and data warehouses. Dashboards can be created with minimal knowledge in SQL as looker uses LookML which in turn generates SQL queries to fetch data. It provides various types of visualizations and allows them to create custom extensions. The dashboards created in Looker are device responsive as the components will get auto aligned based on the device. The data and reports can be shared using URLs and scheduling can also be done based on time or date change. Looker was developed with usability in mind, Looker meets the needs of startups, mid-sized businesses, and enterprises.

Features

LookML:

LookML is the Looker’s proprietary modeling language. It is used to describe the fields, relationships, and calculations. Looker executes a query based on the model created using LookML. Any SQL databases can be accessed using LookML as It is independent of database dialects. It does not require deep knowledge in SQL, and it is easy to learn.

Ad-Hoc Analysis:

Looker is extremely easy to operate; Business users can easily do their analysis with SQL or LookML.

Basically, anything that can be done with SQL can be done with LookML too.

Version Control:

LookML projects use git repository to manage and store the model and its changes. So multiple users can work on the same project at the same time without affecting the deployed production models.  Once the final changes are done the updated version can be deployed to production.

Operates on DB:

It runs entirely on data in a database and there is no own analytic engine for the Looker. So, the SQL query created by LookML will hit the database directly and gives the result. By this, the speed of the data retrieval is completely depending on the power of the underlying database.

Scheduling Dashboards:

Dashboards can be scheduled to the mails on various time intervals like by the minute, hourly, daily, weekly or monthly basis. We can also configure the scheduling based on our time zone.  The format for scheduling can be as PDF or visualizations (as Image) or as CSV files.

Custom Extensions:

Looker provides various visualization capabilities like tables, charts, and Maps and allows us to create custom extensions. It uses D3 charts, so with the knowledge in java scripts, we can create custom extensions to leverage our reporting capabilities.

Data Alerts:

Looker supports conditional alerts on data which will be very helpful for the users to quickly notice and act regarding it if the numbers become not as desired. Once an alert is set to a tile aka look, it’ll check for the condition periodically and send alerts if it matches the criteria. There are several customizations provided with this feature like Alert conditions, recipients, frequency and visibility.

Looker Blocks:

Looker Blocks are pre-built pieces of code that can be leveraged to quickly build reports based on the frequently used sources by many companies, rather than starting the development from scratch, looker blocks can be used to fast forward the development and our customizations can be made on top of it.

Webhooks:

Webhooks are very useful in delivering the data to 3^rd Party applications. The data can be sent in various formats like CSV, JSON, PDF, etc. These webhook deliveries can again be scheduled at different time intervals or associated with a specific data group.

Pricing

Looker has not publicly announced its pricing structure. The looker pricing team will connect with you to give better information on this because of its customized quote. In their website, they have mentioned that the pricing would change based on users’ count and the size of your business. They have plans that will be suitable for small, medium and large-sized companies.

Security

Unlike other platforms that encourage users to pull the data to their platform for analysis, looker generates SQL that directly queries your database. By enabling a secure connection to the data warehouse for querying, Looker puts the major part of the security handling to your data warehouse.

Support

Looker provides access to their help center where users can raise a ticket and it also has lots of documentation for every feature & functionality. Looker also has an award-winning Support Chat where users can connect with looker analysts within minutes and get the issues sorted.

Conclusion

Looker has taken a modern approach by providing complete semantic modeling inside looker through LookML. However, if your organization has OLAP in a place like Analysis Services. Looker does not provide any connectivity to those systems. If the above-mentioned features add great value to your business, Looker will be a good option to fulfil your reporting needs.

Read more about similar Self Service BI topic here and learn more about Visual BI Solutions Microsoft Power BI offerings here. 

The post Can Google’s Looker help in your Business Engagement? appeared first on Visual BI Solutions.

In the previous blog, we saw the basic transformations of Data Modelling in SAP Analytics Cloud to enhance your data and prepare it for analysis.

Sample Data considered is a pivoted table with columns Region, State, City, Store, Postal Code, Latitude, Product Category and Product showing the monthly sales information.

In this blog let us see how to enrich your data with Hierarchies, Geo Locations, and Formulas.

Create Hierarchies

Though Hierarchy is not needed for creating a data model, it helps the user to systematically organize the data and maintain integrity. Here it is good to have a hierarchy on Region -> State -> City -> Store.

You can also have multiple hierarchies in SAP Analytics Cloud Data Model.

Geo Enrichment

Even though there are Latitude and Longitude information in the data model, you cannot directly map them in Geo Map. You need to create a geolocation dimension from the Latitude and Longitude available in the data.

If your data doesn’t have Latitude and Longitude information you can create a geolocation dimension based on Area. Region and Sub-Region columns (state, city, county, etc.) can be mapped.

Formula

Another enhancement that can improve the data model is the usage of Formulas. You can create calculations using the predefined formula in the formula editor. For example, consider that the tax amount of 20% must be included in the Sales Amount available in the data model. You can create a simple calculated column as shown below.

While creating a formula you can make use of conditions, operators, and functions (substring, replace, concat, etc.).

Combine Data

Apart from Formulas, Geo Locations and Hierarchies, you can also make use of combine data options. It allows you to combine data from another excel file or model. To learn more visit the blog here.

Transform Log

As you continue modeling your data, the transformations you make are tracked in your transformation log. It can be specific to a column as well. You can utilize the log to change a transformation that you made if there are no dependencies based on the transformation.

Currently, the transform log supports Unpivot, Smart transformations (split, concatenate, etc.), Formula and Combine Data.

To learn more about SAP Analytics Cloud, check out the series of blogs here.

The post Leveraging Data Modelling in SAP Analytics Cloud – Part 2/2 appeared first on Visual BI Solutions.

A data model is the foundation of every analysis you create. It is a high-level design that uncovers the analytic requirements of the customer. In Data Modelling you enhance your data and prepare it for analysis. SAP Analytics Cloud provides a range of options for Data Modelling.

You can create Planning and Analytic models in SAP Analytics Cloud.

Planning models allows you to set up budgets and forecasts, create private versions of data, copy-paste data, spread, distribute, and allocate.

Let us consider the below sample data. It is a pivoted table with columns Region, State, City, Store, Postal Code, Latitude, Product Category and Product showing the monthly sales information.

Let us see how this sample data can be modeled to be used in Story or Analytic Application.

Import your Data and Identify the Issues

SAP Analytics Cloud allows you to import data from various sources. You can check the full list here. You can create a model from the main menu in SAP Analytics Cloud. Since the sample data is an excel file, you need to choose the option Import a File from your computer and upload the sample data. Once the data is imported you can preview a subset of the data either in grid or card view.

The card view gives you an overview of all the columns imported. When you select a card the Details panel shows the details of the selected column.

• Column type
• Number of unique values (dimensions)
• The mean value (measures)
• Data quality indicated by a status bar

You can use the display option to search for a column and sort the dimensions and measures.

There are two major issues in the imported sample data.

1. Since the table is pivoted, Sales for each month are listed as separate measure columns.

2. Numeric Dimensions like Postal Code, Latitude and Longitude are identified as measures.

Unpivot the Columns

Select all the Monthly Sales columns and unpivot them. A new column for the month is created and the sales values are grouped in a single column which then can be renamed appropriately.

Identify Measures and Dimensions

Select the column that is wrongly marked as measure and change the type to Dimension.

Transformations

With the use of the formula bar like excel, you can transform your data and remove any inaccuracies. The following are the five functions available.

1. Concatenate
2. Split
3. Extract
4. Replace
5. Change

You can also make use of the smart transformations suggested specifically for each column based on the preview data.

Validate the data and once there are no issues you can create the model and consume it in Story / Analytic Application. Apart from the basic transformations you can enrich your data with Hierarchies, Geo Locations, Formulas, etc. which is discussed in the next blog here.

To learn more about SAP Analytics Cloud, check out the series of blogs here.

The post Leveraging Data Modelling in SAP Analytics Cloud – Part 1/2 appeared first on Visual BI Solutions.

For an SAP customer who is using SAP BW as their primary tool for analytics and reporting, SAP BW/4HANA is the next recommended and logical upgrade in their journey for building an EDW platform.

There are many questions that customers are curious about before they embark on the upgrade journey to BW/4HANA.

In this blog, we present few of those questions and answers to enable customers to make the right decision for their upgrade path,

Question: I have BW 7.00 running on DB6, is it possible to migrate to BW/4HANA directly?

Answer: Yes, absolutely!

In this case, Shell conversion can be used. For that, the Green Field installation of BW/4HANA is required and Transfer Cockpit can be used to transport models without data (Meta Data Only) into the BW/4HANA system.

SAP has provided different migration options for BW4/HANA, you can find more details on this note 2383530-Conversion from SAP BW to BW/4HANA

Question: Is there any pre-check to install the Transfer cockpit?

Answer: It is recommended to be at the recommended support package level (refer to the table below) to avoid the implementation of a large number of OSS notes.

Question: Is there a way to do compatibility checks for my BW Objects?

Answer: Yesss!!

Not just BW objects you can also check Compatibility of your Add-Ons, Relevant Simplification Items and HANA DB Sizing details as well using SAP Readiness check tool 2575059 – SAP Readiness check for BW/4HANA

Question: So, all my models are converted into HANA Optimized objects automatically through the transfer cockpit?

Answer: Not all objects but Most of them are converted into their equivalent in BW/4HANA (HANA Optimized).

Below table shows equivalent objects in BW/4HANA for each 7.x Objects

Question: APDs are used heavily in our system, will they still work in BW/4HANA?

Answer: Big Nooo!!

APDs are not supported in BW/4HANA. Along with the Transfer cockpit, SAP provides an additional tool for migrating the APDs (to process chains in the BW/4HANA system). Additional details on how to use the tool can be found in the conversion guide, which is made available to the customer when they install the transfer cockpit. If you prefer to redesign your APD solution with the new options available in BW/4HANA, please refer to the blog from the below link, which provides various options for redesigning APDs in BW/4HANA.

How to migrate APDs to BW/4HANA This blog explains different options for migrating APDs to BW/4HANA

Question: We have multiple planning applications on our BW system using BW-IP, will these planning applications work in BW/4HANA?

Answer: Yes Of course!!

In BW/4HANA, Integrated Planning is now called as BPC Embedded. After the migration of planning objects and the dependent BW model, the planning applications will work as-is. But Planning applications based on BPS are not supported in BW/4HANA, it should be converted manually/redesigned to use BPC Embedded in BW/4HANA.

Question: We have used many customer exits (CMOD) for enhancements, is it still supported in BW/4HANA?

Answer: Nope!!

Function Module EXIT_SAPLRRS0_001 is no longer supported in BW/4HANA so all CMOD exit must be converted to Enhancement Spot (BAdi)

Check here for more information about the conversion of CMOD Exit to Enhancement Spot.

Question: Is 3x flows supported in BW4HANA?

Answer: Nooo

3x data flows aren’t supported in BW/4HANA so it must be converted into 7x flow before migration. From BW 7.3, SAP Delivered a tool(RSMIGRATE) to migrate 3x flows to 7x, this can be used to migrate multiple flows at one go.

SAP has provided different migration options for BW4/HANA, you can find more details on this note 2383530-Conversion from SAP BW to BW/4HANA

Watch out this space, my next blog shares in-depth details on the BPC Embedded functionality in BW/4HANA.

The post SAP BW/4HANA Migration FAQs appeared first on Visual BI Solutions.

Search engines like Google or Bing give you the ability to find the answer for anything that’s available on the internet. But what do you do when you need an answer from your company’s data that you’re working with? A search engine to just answer questions about your data? That’s exactly what SAP Analytics Cloud is offering you with Search to Insight!

How to access Search to Insight?

Until the version 2019.10 of SAP Analytics Cloud, Search to Insight was included within the actual search option of the tool.

Now, the Search to Insight option has been separately given to the users with the “Bulb” icon as shown in the screenshot below.

Apart from the icon, SAP has also positioned this feature in the best possible way by placing it right on the home screen of SAP Analytics Cloud. The moment you log on to SAP Analytics Cloud, you have this welcome message along with a Search bar that intimidates you to ask a question.

We do love asking questions. And when you have humungous datasets in and around your plates, the probability of you having a question is even higher.

Your data is indexed

The one primary criterion for any of your models to be under the radar of the Search to Insight feature is for it to be indexed.

Indexing – The process where the metadata of the models within the Cloud system gets indexed to be identified by the machine learning process of SAP Analytics Cloud.

The imported models are indexed the moment they are brought into the system. Though live models are not indexed when they are consumed, a few live models like the ones from SAP HANA, SAP BW, and SAP S/4 HANA can be manually indexed after being brought into the system.

Once you click on the “Ask a question” bar at the home page, you will be presented with the options as shown below.

You can perform Search to Insight either by choosing a model or by checking out for models suggested by SAP Analytics Cloud.

Choose a model

You can choose any model available on the system. When you choose a specific model, the fields available in that model pop up at the bottom of your screen.

You can type in a question about the data that is available in the model or simply click on these available measures and dimensions to gain insights.

Show suggested models

If you are not familiar with the name of the model, you can ask the SAP Analytics Cloud system to show a list of suggested models based on your activity history on the system (mostly used report, recently developed story, etc.,) by clicking on the Show Suggested Models button.

These suggested models keep changing. For instance, when you are clicking on the Search to Insight icon when you are viewing a Story, the suggested models will be based on the data in the Story.

And when you click on any one of the suggested models, the list of fields available on the models, both measures and dimensions come up on the screen.

Type in your query

You can also randomly start typing in your question directly into the Search to Insight bar and the various fields available across different data models will get listed.

This also helps you to choose the right data model for the answers that you need.

The Search to Insight for rescue

Another critical scenario where Search to Insight might be of a hefty value is when you’re into an application but you want to look out for a minor detail in the data which is not available in that Story / Analytic Application.

You can click on the Search to Insight icon anytime and you’ll be brought to the screen where you can quickly find the answers to your questions and get back to the Story / Analytic Application.

According to SAP roadmap, Search to Insight may be available for the entire spectrum of live data sources in the future. Maybe even voice-enabled search to insight would be brought into the Cloud system which doesn’t seem to be too far from where we are today.

To know about the Analytic Applications of SAP Analytics Cloud, please check out our blog series here.

The post Search to Insight – Data Search Engine of SAP Analytics Cloud appeared first on Visual BI Solutions.

In the new October 2019 release of Power BI, we have a visual that has the Q&A incorporated into our reports and Dashboards. Before, the Q&A was just an optional feature that can be enabled on dashboards.

Now we will be able to use the Q&A in our reports directly as a visual. We can also customize the visual with the dimensions and measures on which Q&A is needed.

Themes that are selected on the Power BI Desktop tool for the report canvas will also be reflected in the Q&A visual. The natural language processing is one of the cool features that is now incorporated in Power BI as a separate component.

You also have the ability now to teach new words and meanings to the Q&A visual that the users might use and Power BI might not recognize.

This can be done in the Q&A Setup option as shown below.

We can also view the questions that were asked on this visual. It’s also possible for us to view the date on which these questions are asked. This can be done on the Review Questions tab in the Q&A Setup.

Q&A is also linked with Bing, The Microsoft search engine. Q&A visual is an extremely powerful and useful visual because we are moving towards an era of digitalization with the usage of NLP, ML and AI. Power BI clearly is taking the right step forward with the incorporation of this new visual.

Know more about Microsoft Power BI services offerings from Visual BI solutions here.

The post Q&A your Data in Power BI appeared first on Visual BI Solutions.

In the previous blog in this series, we learnt about Script Objects. In this blog, let us look at the Slider and Range Slider Widgets that can even be used as measure filters in analytic application.

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Slider can be used by the end user to dynamically change a number while Range Slider allows them to select a range using two-way slider. You can only map numbers to both the widgets.

Slider & Range Slider Widgets in Action

Here the Slider is used by the end user to change the discount percentage which is then used to calculate Sales, Profit/Loss value and percentage to simulate different what-if scenarios.

Slider Widget

Here the Range Slider is used to filter the Stores that has Gross Margin within the range selected.

Add and Configure Slider Widget

Insert a Slider widget and configure it in the Builder Panel.

Slider Widget Configuration

• Min Value and Maximum Value defines the upper and lower limit.
• Current Value is the default value on application start up.
• Step Size – Minimum increase/decrease when you drag the slider. For step size 1, the possible values are 0, 1, 2, 3… For step size 5, possible values are 0, 5, 10, 15…
• Enable Value Input so that the user can double click on the slider handle and manually enter the value.
  

Link Script Variable in Calculated Measure

Create a script variable of type Number. Then in the onChange event of Slider Widget assign the selected value of the Slider to the script variable. Here Discount is the script variable in which the getValue() is used to assign the selected integer.

Slider onChange event script

Now use the script variable Discount in Calculated Measures so that the values change dynamically.

Calculated measure using script variable

Add and Configure Range Slider Widget

Insert and configure the Range Slider.

Range Slider Configuration

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In Range Slider default start and end value can be configured. In both the widgets, step size can be a fraction too.

Add Hidden Table and Script to Filter Stores

Add the following script in the onChange event of the Range Slider.

Range Slider onChange event script

Here in the script getData() function is used to get the Gross Margin of each Store and then the Stores that has values within the range is filtered. Since you need to check the values for all the Stores without the influence of filters, add a Table and hide it. This hidden table(Table_Hidden) can be used to check the values of each Store. The stores with Gross Margin within the range is assigned to a local array variable(filtered_stores) which is then used to filter the table(Table_Main) that is visible to the end user.

Slider and Range Slider can also be used to filter dimensions that are numbers(years, months, bottle pack, etc.) filter measure values, dynamic calculations or script logics.

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Reach out to us here today if you are interested in evaluating if SAP Analytics Cloud is right for you. To know about the Analytic Applications of SAP Analytics Cloud, please check out our blog series here.

The post SAP Analytics Cloud – Application Design Series 16 – What-If Analysis using Slider and Range Slider appeared first on Visual BI Solutions.

Input control, a widget available in SAP analytics cloud Story can be used to view the given data from various perspectives by allowing filtering on dimensions or accounts. This is not directly available out of the box in Analytic Application, however, you can make use of scripting combined with selector widgets for dynamic interaction to achieve a similar effect. SAP Analytics Cloud offers multiple ways to control your data. More than just being a radio button or checkbox, there are a lot more functionalities that input control has to offer to make your stories a lot more meaningful. In this blog, let us see the top 5 ways in which you can leverage the Input Control widget available in SAP Analytics Cloud Story.

1. Time Filters

Every dataset must have a time dimension if it is meant to show trend analysis. Metrics are analysed across different time slices like Month-To-Date, Year-To-Date, Same Period Last Year to gauge the performance of the organization. When a time dimension is mapped to the input control widget, all the above filter options are available directly out of the box for instant filtering and insights. Other frequently viewed time periods like current and previous month/quarter/year are also readily available.

Instant time filters

Dynamic Time Range Filters allow to select a rolling date filter ranging from Selected period – n  to Selected period + m where m and n can be defined by the user at run time and/or design time. The Selected period also known as the custom current period can also be changed by the user according to discretion.

Dynamic Time range filter

Dynamic Metrics

A lucrative feature that adds a self-service effect to stories is the ability to use input controls to dynamically slice and dice through data shown in a chart/table using dimension/measure input control. This allows the user to select the desired dimension, measure or cross calculation in runtime, greatly reducing the number of charts and tables required to arrive at a particular insight.

Dimension and Measure Input Controls

2. Calculation Input Control

Input Controls can also be configured to help in what-if scenarios. For example, you can let the user choose the discount percentage and show how it affects the profit. Calculation Input Controls can be created within a calculation and used in formulas. Calculation Input Control can be of type string or number. You also have an option to directly bind dimension members.

Calculation Input Control

3. Linked Widgets

The newly added ability to control the link between Input Control and widgets gives the story designer control over the widgets to be affected by a particular input control, although, by default, Input Controls behave as Page Filters. Once you select the existing widgets, you also have the option to link any newly created widgets by default. To know more about how you can leverage Linked Analysis feature in SAP Analytics Cloud visit this blog.

4. Advanced Filters

Advanced Filters can have multiple ‘AND’ and ‘OR’ conditions to filter the data according to your needs. For example, when you need to compare regional sales value after restricting the top-selling categories of each region you can add an advanced filter to do the same. Even though the advanced filter is available under Widget and Page Filtering, the ability to link specific widgets and the ability to allow the end-user to select the ‘AND’ and ‘OR’ conditions make the input control more attractive.

Advanced Filtering in Input Control

Input Control allows us to easily handle various filter scenarios out-of-the-box and avoids the hassle of scripting that Analytic Application would have needed otherwise. To learn more about SAP Analytics Cloud visit our series of blogs here.

Check out our blog series here to learn more about Analytic Applications in SAP Analytics Cloud.

The post Top 5 Ways to Leverage Input Controls in SAP Analytics Cloud appeared first on Visual BI Solutions.

In the previous blog in this series, we learnt how to swap, sort and rank R widgets. In this blog we will see how to leverage slider widgets in SAP Analytics Cloud analytic applications to interact with R visualization.

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The scenario is to analyse the customers that have highest debt ratio (here the debt is considered doubtful when overdue amount is > 120 days). Slider is used to control the minimum doubtful debt, and Range Slider is used to control the percentage of doubtful debt within total amount due.

You can see how the Slider and Range Slider widgets are used as a measure filter for R Visualization in action below.

Slider and Range Slider in action

Add Slider and Range Slider

Add a slider to the canvas and configure it with minimum, maximum and default values as shown below.

Slider configuration in SAP Analytics Cloud

Add a range slider and configure it. You can modify the look and feel in the Styling panel.

Range Slider configuration in SAP Analytics Cloud

Passing input variables to R

Add the following scripts in the OnChange event of Slider and Range Slider widgets to pass the values to R Visualization.

Slider OnChange event script

Range Slider OnChange event script

The getValue() function is used to get the values from the slider. Similarly, getStartValue() and getEndValue() does the same job for range slider component. Debt_value, Precent_debt_min and Precent_debt_max are R input variables, and these are set using the setNumber() API.

Add the following script in the OnInitialization event to initialize the variables to R Widget and avoid errors on application start up.

Application OnInitialization script

Initializing the Data Frame in R Widget

Add R Visualization widget to the canvas and choose a data source. After which you will be able to add rows and columns from the Input Data panel. You can find detailed explanation on creating and adding data frames here. Select the fields required, in this case Company, Customer dimensions and Doubtful Debt, % Doubtful Debt measures.

R Data Frame initialized with rows and columns

plotly  – Scatter Plot Visualization

This blog uses the plotly library to produce enhanced visuals. You can refer to the blog Adding Interactivity and Customizations to  R widgets for more detailed customizing options in plotly.

The R script in the snapshot below explains how to create a scatter plot chart and modify the axis limits based on the user selection made from Slider widgets.

R script to plot a scatter chart in SAP Analytics Cloud

Save and run the application. When you make a selection in the sliders, the corresponding values get passed to R Visualization and the R script gets executed visualizing a scatter plot. You can try adding the functionality to multiple chart and filter types as well.

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Check out our other blogs on R Visualization series here. Reach out to us here today if you are interested in evaluating if SAP Analytics Cloud is right for you.

The post SAP Analytics Cloud – R Visualization Series 4 – Leveraging Measure Filters using Slider Widgets appeared first on Visual BI Solutions.

Sharing and collaboration are essential in any given team, but it becomes critical when it involves diversified multifunctional teams. The analysis doesn’t stop when you complete your application – the conversation continues in meetings, over email, and during casual chats. Thus, SAP Analytics Cloud provides a wide range of features for seamless collaboration that in turn enhance teamwork and productivity. This blog lists out the predominant features (in no particular order) that make SAP Analytics Cloud a great platform for synergy.

Discussions

Want to share your analytic application and get feedback from your peer through real-time chatting? Open the discussion panel and invite him/her. You can share your application, assign a task or even create a new process within the discussions panel while you chat. The attachments are not just limited to elements within the SAP Analytics Cloud. You can even share your local document like Wireframes. There are options to create a group, archive or delete a discussion as well.

Discussion

2. Comments

Are you a social media lover who actively comments on social feeds to share your insightful opinion? Have you ever wondered how great it would be if you could do the same in BI analytics applications? Then this feature from SAP analytics cloud is for you. Commenting allows you to start a real-time conversation with the collaborators of the shared application, around your key business metrics, facilitating collaborative decision making. You can post comments on widgets to start a specific conversation.

You can also ask your sales manager why a product is not performing well in his region by commenting on the actuals data cell of that product in that region and tag that Sales manager. It doesn’t stop here; you can even display those important comments in a dedicated column for easier visibility.

Comments

3. Calendar Tasks & Processes

Managing a big team? Keeping your tasks organized just got easier. You can split your project work into processes and tasks. Assign those to responsible users, add reviewers and set deadlines. You can even add context to the tasks making it easier to zero in on the data that an assignee needs to work with. This just made monitoring project progress a cakewalk.

Featuring two modes of process monitoring – Calendar and Gantt, SAP analytics cloud allows you to choose a mode of viewing the project plan based on your needs and time frame. The calendar view is the standard calendar layout (Day, Week, Month). Gantt view, just as its name suggests is a Gantt kind of chart that allows you to visualize and create tasks for up to years in the project timeline. You can create general tasks, data locking task, task/process by driving dimension and process in Calendar/Gantt view. You can also add filters based on style, status, roles, due and type. Calendar tasks are even downloadable.

Calendar Task

4. Input Tasks

Are you forecasting the sales for the next quarter and need additional information from your colleague for that? You can create an input task for that table which is using planning model and assign to them to get additional info. Also, if there is a value driver tree based on the same model as the table, they can also give input by performing simulations on the value driver tree.

You can view the created input tasks and its progress in Files -> Input Forms and in the calendar. You will also have a summary page in the story that lists the input tasks for that story.

Input Task

5. Users & Teams

Consider you are a resource manager and want to create users and teams for your project groups. Don’t worry it is not a Herculean task, SAP analytics cloud provides a user-friendly interface which allows creating users manually by providing details or by importing from a file. When users work together, they might need access to the same documents. You can organize the group’s analytic content as teams to quickly collaborate and manage their permissions by creating a folder for your team to stay organized.

In the next part of this blog, we will discuss a few more significant features of sharing and collaboration in SAP Analytics Cloud.

Check out our blog series here to learn more about Analytic Applications in SAP Analytics Cloud.

The post SAP Analytics Cloud – Sharing and Collaboration Part 1/2 appeared first on Visual BI Solutions.

Following an exemptional Success of  Power Platform World Tour 2019 at Calgary Visual BI has partnered with Microsoft for the upcoming Power Platform World Tour at Sydney, Portland and Anaheim happening on Nov 21st- 22nd, Dec 3rd – 4th and Dec 4th -5th respectively.

Powered by the local Power Platform User Groups, the Power Platform World Tour brings unprecedented access to premium Power BI, PowerApps, and Flow content presented by Microsoft and local industry experts.

This year Power Platform world tour will be visiting more cities with a line-up of great content; showcasing modern data visualization, app customization and innovation at its core. This event series brought to you by Power Platform User Groups gives the opportunity to come and meet both Microsoft and industry experts, explore the latest business challenges and solutions across various industries and see the innovative world of technology in action.

Join our team on this exploration around the world, gaining peer-to-peer networking opportunities and building a local support system sure to provide value 365 days a year.

Read more about the event here. Look out this space to know more about the updates about the Visual BI’s  Speaker session and updates on our Sponsorship for Power Platform World Tour.

Reach out to us at marketing@visualbi.com for any further inquiry.

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In this blog, we will see some of the interesting features that SAP Analytics Cloud table component has to offer in Story/ Application and the ways to leverage its power.

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When it comes to making the most of your business, setting your data right really matters. This not only translates to having the right data but also creating meaningful visualization. Thanks to modern BI, dashboarding has made its way into mainstream decision making as opposed to endless reports and contextless numbers.

Though charts consume the major chunk of your BI diet, the need for tables on your plate is sometimes irrefutable. Unlike charts that show trends, patterns or a bird-eye view of the data, tables present data close to its raw form, letting you dig deeper into the numbers to examine and analyse.

With SAP Analytics Cloud table component, you can add your own flavours and cater to your data needs with much ease. This blog explains the top features and analyzes business case studies that showcase the capabilities of SAP Analytics Cloud table component.

The Panacea

It’s vital to make sense of your data in this data-rich and noisy world. With SAP Analytics Cloud under the hood, featuring sort, rank, linked analysis, hierarchies and whatnot, you can get to the crux of what matters. And that’s not the end of it! Unlike Excel, you can dazzle users with impressive and customized UI, making their life easier and easy on the eye too. SAP Analytics Cloud gets this done with properties like freeze row/ column, fonts, commenting, export options, styling and other customizations.

Numbers that speak

Our digital era is being flooded with data from our phones, homes, office – virtually everything. Biologically, our brains are hard-wired to process images. And thus, it gets messy to make decisions out of this humongous amount of data. Used well, formatting data using visuals can bring patterns out of apparent randomness. Analytics Cloud table lets you transform your numbers to stunning In-cell Charts as Bar/ Column, Variance Pin and Variance Bar. And if that’s not enough, you are given the luxury of creating your own customizations using conditional formatting and thresholds.

Aesthetics made better with Conditional formatting and In-cell charts options

Insights on-the-fly

Imagine visiting the ice cream parlour and treating yourself to ice cream. Rather than choosing a flavour or two, it would be great if you can create your own special flavour. Now, how about doing the same with your table calculations. That too, with a single click and on-the-fly! Analytics Cloud lets you leverage Moving Averages, Rank Number, Sum and many more. Hang on! You can also manipulate data just as excel does – formula bar, pull-handle functionality and so on.

Save your time by calculating on-the-fly

X-Ray Vision

Unless you are Superman, you wish you had the superpower to see through walls. Most analysts long for the same in a BI tool. Analytics Cloud allows you to exploit data having the X-Ray vision glasses on. Smart Insights in the Analytics Cloud table component allows you to see through data and derive decisions without having to invest a lot of time. Another add-on is the Explore feature that enables you to slice and dice seamlessly.

Venture the possibilities and go beyond with the Explore feature

Venture the possibilities and go beyond with the Explore feature

Back to the Future

Dive in for a ride to the future! Who wouldn’t be excited to glimpse at what the future holds? Foreseeing the future gets less of fantasy with SAP Analytics Cloud as your crystal ball. Manoeuvring the proprietary machine learning algorithms housed within SAP Analytics Cloud, it is now possible to predict your business trends, finances, stocks and much more with just the table component. Compound these Predictive capabilities with Planning and capabilities to write-back to pre-existing planning systems and Voila! You have all you need to become a data-driven enterprise.

See the unseen with the Predictive capabilities

Want to make the most of SAP Analytics Cloud? Click here to evaluate if SAP Analytics Cloud is right for you. To know about the Analytic Applications of SAP Analytics Cloud, please check out our blog series here.

The post SAP Analytics Cloud – Leveraging Table Features appeared first on Visual BI Solutions.

Advanced Gauge charts always remind me of a speedometer that measures and displays the instantaneous speed of the vehicle which always indicates if we are crossing the speed limit and keep us in control. Likewise, the Advanced gauge helps visualize a single value within a given scale as pointed by the needle on the coloured data range or chart axis. This chart type is often used in executive dashboards to show key business indicators.

Now let us look at how to configure this visual in 7 simple steps

Step 1: Assign the Actual and Target value
Assume that we are looking at a supply chain dashboard and need to visualize the On-time Delivery % – compared with our target value.

Step 2: Set the max/min value from the property sheet directly
Once we have the actual and target values of the On-time % is mapped, we could input the minimum and maximum directly from the format tab – Gauge Options.

Step 3: Conditional formatting (Apply to Track background)
This custom visual allows the user to apply conditional formatting providing more insight into the visualization quickly using the advanced editor

Step 4: Data labels
Allows the user to customize the data labels of both primary and secondary values

Step 5: Number Formatting
This visual allows the user to Set the scaling display and customize it, add separators, prefix and suffix values could be added, even semantic formatting is available

Step 6: Axis Formatting
This visual provides more customization option for axis such as reverse axis, Show/hide labels & the ticks within the axis

Step 7: Data & Track Colors
We could set a color theme for both target and actual values if no conditional formatting is added to the visualization, including a needle pointer for the actual values

We have covered the key features of xViz Horizon Chart so far but there is more. To get the latest version of the custom visual, reach out to us here.

This blog has been originally published on xViz website. Click here to visit and know more about custom visuals for Microsoft Power BI.

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SAP Data Warehouse Cloud is SAP’s next enterprise Data Warehousing solution on Cloud. It is capable of integrating data from various types of sources into one place and enables to perform advanced analytics using the built-in SAP Analytics Cloud powered by SAP HANA Cloud services as well as any other third party reporting tool (not available in the trial).

The product is still under development and SAP has recently launched the trial, to explore the features of the product. You can register for the trial at – https://saphanacloudservices.com/data-warehouse-cloud/

The scope of this blog covers features pertaining to trial account only and they are subject to change when the product is GA.

SAP Data Warehouse A Quick Guide on Trial Account Features

The Interface

The User Interface of the SAP Datawarehouse Cloud looks very neat, intuitive and extremely user-friendly. The Home page is loaded with options to browse through the community, help documents and guided videos. The collapsible panel on the left has distinct buttons that are boldly arranged. These buttons can be used to navigate to various tabs. The tabs are as follows.

Administration

The Administration tab enables you to register Data Provisioning Agents. It basically helps to establish connections/secure tunnel between SAP HANA database on which SAP Data Warehouse Cloud runs and various on-premises systems in your landscape.

Security

The Security tab enables you to manage Users and Roles. Under the ‘Users’ option, you can add/edit users and assign them roles as required. Roles’ option gives you an overview of multiple roles that are available in the system. Interestingly, you have the ‘Activities’ tab, in which the system keeps track of all the activities you carry out that could be used for audit purposes.

Connections

The Connections tab enables you to add connections to data sources including remote data sources as well. In the trial, you can add SAP HANA, SAP ABAP and OData Sources only. The SAP HANA and SAP ABAP source systems are connected via the DP agent that is already registered.

Space Management

The Space Management tab facilitates the creation and maintenance of Spaces. ‘Spaces’ is a concept that introduces a logical partition/area in which you can assign specific connections. The objects that are exposed to SAP Data Warehouse Cloud through connections, along with tables that are created in using CSV files, would act as sources for model creation. The resultant models act as a base, on top of which SAP Analytic Cloud stories can be created. You can assign users with roles to view, edit or delete objects in Spaces. Although few of the features are under development in the trial version, the UI is exciting with the summary on the status of the Spaces to understand if Space(s) is(are) hot, cold, active or in hibernate mode.

Data Builder

The Data Builder is a place for creating objects that include tables, Graphical/SQL views and ER models. The tables can either be remote (created using connections) or local (created using CSV files). The graphical view development features are limited to join, union, filter, renaming and creation of calculated columns in the trial version. However, we can expect extensive features to be added in creating graphical views similar to HANA calculation views in the near future. The data modelling gives an all-new user experience with an intuitive and simple design.

Story Builder

As part of the SAP Analytics Cloud strategy for embedded analytics, it is an out-of-the-box solution that is tightly integrated with SAP Data Warehouse Cloud. The Story Builder is the go-to place for the SAP Analytics Cloud story creation on top of the models that are classified as ‘Fact’. In the trial version, the front-end capabilities are limited to SAP Analytics Cloud only and we can expect the connectivity to multiple front-end tools in future.

Business Catalog

The Business Catalog acts as a dictionary to all the available objects in the system. Users can navigate as required from this point.

The solution aims to provide flexibility and agility for both business and IT users. The first impression of the tool looks very promising and has created quite a buzz among SAP’s customers and partners. The product is expected to release in Q4 this year.

For more information and insights on SAP Data Warehouse Cloud click here.

The post SAP Data Warehouse Cloud: A Quick Start on the Trial Account Features appeared first on Visual BI Solutions.

Advanced Gauges are a great way to represent a metric against a scale with optional qualitative representations illustrated by colors. They are also sometimes referred to as a speedometer or tachometer chart and are most commonly used to measure values like volume, temperature, speed, etc.

The Advanced gauges are good for following use cases:

• Display the health of the KPI with the help of the qualitative scale
• Show progress toward a goal.
• Display simple information which one can quickly scan and understand.

The xViz is an enterprise-grade custom visuals suite exclusively for Microsoft Power BI. In case you are not familiar with our visuals yet, you can find some introduction material here. The xViz suite consists of 2 types of gauges-

1. Advanced Gauge
2. Linear Gauge

Now, let’s look at some of the use cases for Advanced gauge using xViz for Power BI

Single-axis with Target Value

The most common use case of the Advanced gauge is to not only view the actual value on the scale but also compare it against the given target. With xViz Advanced gauge for Power BI, you can both assign a measured value as part of the fields tab or enter the desired target value as part of the chart properties field.  Further, to increase the chart accuracy and make it ready for dynamic scenarios. You could also assign a chart min and max value either using the fields Tab or by entering the desired value in the chart properties.

Conditional formatting

Another commonly used property is conditional formatting which helps in visual alerting and conveys the state of the KPI whether it is doing good or bad. There are 2 ways in which you can configure the conditional formatting – first, you can apply conditional formatting to the axis scale (fig 1) or second, you color code the fill area (fig 2) which would dynamically change color on reaching a certain set threshold. Based on the values provided to the chart, the conditional formatting can be set based on the following –

1. Percentage of Gauge Max value
2. Percentage of the Gauge Target value
3. Value – User-defined absolute value

Semi-circle or full dial

Depending on the use case and real estate available the user can customize the type of Gauge required. In case you were wanting to use it for measuring gross margins for your business, you would prefer using a semi-circular gauge where the axis range is limited. Whereas, in case of a speedometer you would prefer using a circular gauge where you have a wider range of values.

Dual Axis

In case you would like to measure more than one KPI, especially when both the KPIs are closely linked like Sales and Avg Selling Price of the material. You could use a Dual Axis Gauge.

Just like the primary axis, the secondary axis has the same properties, where you can assign the Actual, Target, Min and Max values

Reverse Axis

In cases, where the customer would like to read from left to right especially in the middle eastern countries, we have reverse axis property to reverse the direction of the axis.

We have covered the key features of xViz Advanced Gauge Chart so far but there is more. To get the latest version of the custom visual, reach out to us here.

This blog has been originally published on xViz website. Click here to visit and know more about custom visuals for Microsoft Power BI.

The post 5 Advanced Gauge Customization for Power BI appeared first on Visual BI Solutions.

Introduction

This month we release 3 new visuals as part of our xViz suite of visuals for Power BI in the Microsoft App source-

1. Advanced Gauge
2. Hierarchical Filter
3. Hierarchical Tree (Coming Soon).

Of which my personal favorite is the Hierarchical Filter.

The Hierarchical Filter is a selector component like a slicer, just that it can display multiple category values in an expandable tree view used to displayed hierarchical nodes for filtering. Each node can be expanded and collapsed for optimal navigation through the hierarchy and single/ multiple selections can be made to select the desired nodes.

Now let’s look at some of the key features of the xViz hierarchical filter which would help you easily slice and dice your data by providing a simple hierarchical tree view

Filter Settings – Category Display

You can configure the filter interaction and category display settings using the filter settings.

1. Single select/ Multi-select node selection- The user can either configure the filter to act as a single select or multi-select option.

2. Category Display Settings – The default category level to be displayed along with no of the hierarchical attributes to be displayed can be configured using the following two properties.

Display Measure value

Along with the categories, the xViz Hierarchical filter also gives the option to display measured values. This way one can have a quick snapshot of the most important KPI for each of the category values. For e.g. you could display sales values for the hierarchical filter and conditional format is based on whether it has met the target or not. This way you could quickly focus on the categories which haven’t met the target.

Appearance Settings

The xViz provides a robust set of appearance settings for better styling and interaction capabilities which are listed as follows:

1. Alternate row Color – Just like the Table, the hierarchical filter provides alternate row coloring for better readability.

2. Selected value customization – It becomes a lot easier as an end-user to quickly identify the selected filter value if it is styled differently from the rest of the items.

3. Hover customization – Ability to define hover color

4. Icon Colors – Filter, Search, Search text

Conditional Formatting

Just like the charts, the hierarchical filter can also be conditionally formatted. This property comes in very handy as it helps end-user to spot the outliers quickly and navigate to the pain points. Conditional formatting is part of the pro feature and can be done by assigning a color to both font and background.

Search Capability

The xViz Hierarchical filter provides search functionality, which allows users to search for a particular value across the complete hierarchy. There are 2 different types of search operations you can choose from

1. Filter – Filters the dashboard based on the searched value

2. Find and Seek – Displays the values being searched without affecting the dashboard

Runtime options

1. Clear filter – Clear all select nodes

2. Custom context Menu – The context menu provides you the option to expand/ collapse the hierarchical tree view for easy navigation

Tooltip

If you feel you are limited with having only one measure value in the hierarchical filter. You could try using the tooltip option in order to display additional information for each of the category fields.

We have covered the key features of xViz Hierarchical Filter Chart so far but there is more. To get the latest version of the custom visual, reach out to us here.

This blog has been originally published on xViz website. Click here to visit and know more about custom visuals for Microsoft Power BI.

The post Hierarchical Filter: Custom Visuals for Microsoft Power BI appeared first on Visual BI Solutions.

We often see employees in an organization get access information to which they should not have access as per the company policy. It happens because restrictions are not imposed on the Power BI Dataset that’s has been made available. In order to enforce rules to the data that’s available to all the users, we need to implement RLS on the dataset.

Row-Level Security (RLS) enables you to use group membership to control access to rows in a table.

It simplifies the design and coding of security in your report. RLS will help you keep restrictions on data row access in datasets. For example, you can ensure that users access only the data that belongs to their department. Another example is to restrict customers’ data access to only the data relevant to their company.

Let’s see how to implement RLS in PowerBI.

In the below example, consider we have users in many regions. We will restrict data for the users to make sure they can access data belonging only to their region.

Roles and rules

Open the Manage Roles option from the modelling tab, to create regions as roles.

1. We can see three sections Roles, Tables & Expressions.
2. Click on create to, create a role, we will be able to see the list of tables used in the dataset.
3. Select the role name and the table to enter the relevant Filter expression to create appropriate rules for the roles created.

View as roles

1. We can see how the report might look like to the users of respective roles easily.
2. Click on view as roles in modelling tab
   
3. We can choose the roles from the list as shown
   
4. Data is now filtered based on the rules set up for specific roles.

View as roles are used to check how the report might look for each role that was configured. Once we see satisfying results, let’s publish the report and head to PowerBI service.

Assigning users to roles

In PowerBI Desktop, we have set the roles and rules for the report, In PowerBI Service we can define the users to the roles.

1. 1. Open the workspace to which the report was published.
   2. Head to the Datasets section and click on the ellipses near the dataset of the report that was published
   3. Click on security
      
   4. We can enter the user names one by one or use a group by selecting the roles that were set in PowerBI Desktop
   5. RLS will be now applied to the dataset and will be utilized in the report when users access the report.

   Now, if the users of central bohemia try to access the report, they won’t be able to see the data of east Bohemia.

   It is important to note that workspace settings should be set with “Members can only view Power BI content” privacy mode and for the RLS to be effective for the users, they should have only member access.

   Note: This security implementation for analysis services is done in model level.

   We can see how easy it is to set up RLS in PowerBI. This feature will be helpful to make sure that users can get access only to the data they are allowed to analyse.

   Know more about Microsoft Power BI services offerings from Visual BI solutions here.

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   The post Row Level Security in PowerBI appeared first on Visual BI Solutions.

Reports make end-user to consume data in an efficient manner. Also, it gives a significant impact on organizations decision. One can create a report for several reasons some of them were to monitor resource utilization, productivity assessment, shows business performance across the different period, reveal hindrance of the business process, shows key performance indicator. To achieve this, Alteryx provides a variety of tool to develop the Reports.

A report encompasses elements like logo, header, footer, text, images, maps and visuals. Let discuss the tools available for creating these reporting elements in Alteryx

1. Basic Elements

Header

• Report Header tool, you can create a header with text, logo, date, image.

Footer

• Report Footer Tool, you can create a footer with page number, copyrights text and text.

Text

• Report Text tool can be used to add text, hyperlink to the report. It supports the formatting of selected text

Image

• Image tool used to add an image to the report. Let’s you to attach the image dynamically by fields from the input file

Note: Every component/functionality referred to as Tools in Alteryx environment

Available tools:  Report Header, Report Footer, Report Text, Image

2. Visualization

Using the charts available in Alteryx you can find distribution and correlations, to do a comparison, drill-down analysis, trending and trading analysis and then behaviour analysis. All these analyses done through the Interactive chart tools.

Charts supported by Interactive Chart tool(Line, Bar, Area, Pie, Scatter, Box and whisker, Candlestick, Heat Map)

Available tools:  Interactive Chart Tool

3. Detailed Analysis

To do this analysis requires a table. In general, the table used to show transaction data, product tracking details, comparing different objects, generate a statement, time analysis. Using the below tool, you can configure the width of the table, adding bar graph to table, apply conditional formatting to the fields and arranging the fields.

Available tool: Table

4. Geography Analysis

Using the maps, we can show sales across a different territory. By applying heat layer, we can calculate demands in certain area. Finding a trading area around the business radius.

Available tool: Report Map, Map Legend Build, Map Legend Split

5. Notify Feature

By providing the SMTP URL to Email tool you can send email to a group of user or user. Also, it supports attaching the file through browsing the local file or selecting the field from the input which contains the location of the files.

Available tools: Email

6. Structure Maintenance

To create a final report, you must organize reporting elements to produce a valuable report.

Following tools can help to arrange, resize, formatting the reporting elements:

Available tools: Visual Layout, Layout, Overlay

7. Viewing the Report

Alteryx allows a user to save the reports in the following format PDF, RDF, Excel, Powerpoint, Word, PNG, HTML, MHTML.

Available tools: Render, Browse

8.Scheduling Report

We don’t have the option to schedule a report because a report is created under workflow. By scheduling the workflow, you can get report through email.

Click to see more detail about scheduling a workflow

Using Reporting in Alteryx, one can easily generate the same report for a different region, departments through the concept of batching. It is easy to create a dynamic report also simple selecting pertinent field from the input.

Read more about similar Self Service BI topics here and read more blogs from Alteryx category here.

The post Reporting in Alteryx appeared first on Visual BI Solutions.

The choice of data warehouse depends on data volume, data variety and analysis requirements. In this blog, we are trying to compare what would be an ideal warehouse for Power BI reporting. So, the results of our testing will only reflect the performance of Azure and Snowflake with respect to PowerBI reporting.

What data did we use?

We took an open-source stock data, mocked and duplicated it to be at 100 GB, which would be our fact. We have the date, time and security code dimensions. Together we have a star schema with one fact and 3 masters. We have loaded the same data into both Snowflake and Azure Data Warehouse.

What did we do with the data?

We built a similar PowerBI dashboard on top of Snowflake and Azure Data Warehouse. Both are connected on direct query mode. We have built a semantic star schema on the PowerBI Modelling perspective. So now if we run any query on these dashboards, we will have similar queries fired on both systems. We will compare both these query performances to draw conclusions.

What systems did we use?

We have chosen a data warehouse which has similar costs. In Azure, we have chosen DW200c Gen 2 which would cost around $3.02/hour in pay as you go subscriptions. You could avail these lot cheaper at longer subscriptions.

In Snowflake we have chosen XS tier, which could charge you between $2-4 depending on the subscription type. You can secure price discounts with pre-purchased Snowflake capacity options.

How did we tune the data warehouses?

In azure, we have performed Round Robin partitioning on the fact, so the 100 GB of data is evenly distributed across all partitions. We have used duplicated partitioning for the master table to minimize inter partition data movement.

In Snowflake, we have built clusters on the columns on which filtering will happen in the fact table. Other than this, we couldn’t find any specific optimization techniques from Snowflake. Snowflake claims to be already storing data for the best possible performance.

What did we observe?

In most cases, we identified that Azure was able to fetch faster results than Snowflake. On analyzing the Query plan, seemed like snowflake was spending most of the time in IO. Seems like Snowflake needed an extra bit of time to get the data into the computer. But, the auto-resume feature of Snowflake would overtake any performance advantage of Azure. Because Azure Data Warehouse doesn’t have an auto-resume option, we must keep the system running and billing. But, in snowflake cluster would come alive only automatically at the event of a query and hence doesn’t need to be kept running. Similarly, you have the option to auto-scale the number of clusters in Snowflake, whereas this must be programmed or manually upgraded in Azure. Other than this, we felt both systems of competitively charged for their service.

The most difference between the qualitative and not quantitative. Azure is built for tunability whereas snowflake is for ease of use. It’s best to demo both these products and choose what works best for your organization.

Read more about similar Self Service BI topics click here and learn more about Visual BI Solutions Microsoft Power BI offerings here. 

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