ميزات تابلو 2020.2 الجديدة (الإصدار التجريبي)

Let’s introduce one such tool.

Beinex has a set of tools to automate the migration testing process, and the devices can also process syntax changes between the legacy database and Snowflake. Well, Syntax Migrator is one tool developed by our experts that follows a structured methodology that helps minimise migration risks. It is an error-free, timesaving, automated migratory tool that converts SQL syntax into Snowflake queries.

How does it work?

With the help of a user-friendly tool like Syntax Migrator, we can quickly convert SQL syntax into Snowflake queries by entering the SQL syntax in the console and then pressing the convert button. It is handy, and even persons with no technical expertise can easily use it.

Syntax Migration Platform can aid in:

1. Automatically translating DDL and DML
2. Selection of best possible data type
3. Intelligent usage of Temp and Transient Tables

Automatically translate DDL and DML.

• Creation of different tables views, and procedures can be quickly changed into Snowflake-compatible queries without any help from Snowflake syntax.
• No matter how the complex procedure is, it converts the syntax to Snowflake and maintains the logic and structure of the procedure.

Selection of the best possible data type

1. Syntax migrator selects the best data type available in Snowflake concerning the source data source even if the datatype is disparate.
2. No need to worry about any mismatch in the data while converting to the compatible datatype. All the data properties will be preserved during conversion.

Intelligent usage of Temp and Transient Tables:

Query logic and syntax will be preserved in the migration, which results in the expected results same as of the source system

About Snowflake

The cloud data platform from Snowflake enables a variety of data workloads, including data warehousing and data lakes, as well as data engineering, data science, and data application development across numerous cloud providers and geographies from any location inside the company.

Due to Snowflake's distinctive architecture, almost any concurrent user in the Data Cloud can benefit from near-infinite storage and real-time processing.

The Many Benefits of Migrating to Snowflake Data Cloud

Even though migrating from an on-premises solution to a cloud can be a tedious process, with Snowflake Data Cloud, it is not laborious, and it can reap benefits like the following:

1. Infinite elasticity
2. Highly concurrent
3. Exponential cost saving
4. Superior data security
5. Modern data cloud platform with your data
6. Reduced maintenance overhead
7. Continue to use on-premises transactional platforms
8. Move to pay for what you use instead of heavy AMCs
9. Conversion of queries into a best Snowflake-compatible format

But this does not apply to a slicer visual, which only has cross-filtering and no interaction feature. This made me wonder if we can achieve cross-highlight with a slicer visual through some work-around that allows me to compare one category with other easily through cross-highlight. After some research, I may have found a way to do this. Below are the steps in detail to achieve this. Normal filtering of data with slicer: The below example shows normal slicing of data where only the selected value will be reflected in the bar chart. This does not allow me to compare other values easily or focus in on the selected value through highlighting. Dynamic highlighting with Slicer: The below example shows the dynamic highlighting where I can choose the categories in the slicer to highlight for comparison with the other categories. I can easily focus on the selected categories and compare the measure values with other categories. Solution: First, I have created a disconnected table with the categories. This can be easily done with the following dax formula.

Selected Category = VALUES(Orders[Category])

Had made sure there is no relationship in the model view between the source table and new category table. Created a measure which will be added to the conditional formatting in data colour section in the format pane.

Selected Colour bar = 
var selected_category = VALUES ('Selected Category'[Category]) 
var category_to_highlight = SELECTEDVALUE (Orders [Category])
var filtered = ISFILTERED ('Selected Category'[Category])
var result = 
    SWITCH(TRUE(),NOT(filtered),"#0055cc", category_to_highlight in selected_category && filtered,"#0055cc","#9cd0ed")
return result

Explanation: Selected Colour bar =

var selected_category = VALUES ('Selected Category'[Category]) // taking values from category table

var category_to_highlight = SELECTEDVALUE (Orders [Category]) // Using selected value function 

var filtered = ISFILTERED ('Selected Category'[Category]) // checks if the column is filtered and will return true or false

var result = 
SWITCH(TRUE(),NOT(filtered),"#0055cc", category_to_highlight in selected_category && filtered,"#0055cc","#9cd0ed") 

/* The first condition checks if the there is no filtration will return all values, Then will be checking if the selected set of values is contained in the category column. The selected value will be returning a specific dark colour while the unselected value will be giving a lighter colour. */

return result

Created a bar chart with total sales given in the value section and the category column from the source data will be given as axis. Gave the highlight effect by adding a measure in the field value section of the data colour conditional formatting. Added the measure in the field value section. Added category slicer from the Selected Category table Finally arranged them and saw the magic happen. Conclusion: This goes to show the hidden features of Power BI one can explore with a little bit of tinkering with a dash of DAX. This blog is a first in a series of many nifty blogs. Hope you like it and looking forward to your feedback.

The Primary Means to Narrate Stories in Tableau

We can represent the visuals using the following three formats on Tableau:

1. Sheets: Spaces where we can build individual visuals are called sheets. A worksheet has a single view in its sidebar, as well as shelves, cards, legends, and the Data and Analytics panes. A workbook is a sheet file structure along the lines of Microsoft Excel. It includes sheets that can function as a worksheet, a dashboard, or even a story

Source: https://help.tableau.com/current/pro/desktop/en-us/inspectdata_describe.htm

2. Dashboards: Most commonly used reporting format, a dashboard is a layout where sheets are arranged in a meaningful manner. It is a collection of views that helps you to compare a variety of data at the same time. If you have a set of views that should be reviewed every day, then you can create a dashboard that displays all of the views at once rather than navigating to separate worksheets. Think of the efficiency gains that this can bring about.

Source: https://www.tableau.com/about/blog/2020/5/6-dashboards-tableau-partners-help-you-mitigate-covid-19

3. Story:Sheets or dashboards arranged in a sequence to convey information. A story is a collection of visuals that work together to convey information. Stories can be created to tell a data narrative, provide context, show how decisions affect outcomes, or simply make a compelling case.

Source: https://help.tableau.com/current/pro/desktop/en-us/stories

Default Charts in Tableau

Through a set of default charts created with Tableau, data sets can be displayed in a comprehensible way. Let's have a look at a few types of charts:

1. Area Chart:An area chart is a line chart with a colour shaded area between the line and the axis. These charts constitute the most common approach to illustrate stacked lines and are often used to represent accumulated totals over time.

Source: https://help.tableau.com/current/pro/desktop/en-us/qs_area_charts.htm

2. Bar Chart:Place a dimension on the Rows shelf and a measure on the Columns shelf to make a bar chart or vice versa. We may compare numerical data such as integers and percentages using bar charts. Each variable's value is represented by the length of each bar. Bar charts, for example, might demonstrate how much money a small business spends on various expenses.

Source: https://www.tableau.com/data-insights/reference-library/visual-analytics/charts/bar-charts

3. Box-and-whisker Plots:When demonstrating the distribution of data points across a specified metric, box-and-whisker plots, also known as box plots, are an excellent chart to employ. The ranges within the variables measured are represented in these graphs. These graphics are useful for comparing the distributions of multiple variables.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_boxplot.htm

4. Bubble cloud: In bubble clouds, data is displayed in a cluster of circles. Individual bubbles are defined by dimensions, while individual circles are defined by measures. A bubble chart's design allows it to display multiple variables. Individual bubbles represent dimension field values, while measure field values define the size and colour of the bubble. As a result, we can examine a plot with at least three variables, one dimension and two measure fields.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_bubbles.htm

5. Bullet Graph:Bullet graphs are a type of bar graph that was created to replace dashboard gauges and metres. When comparing the performance of a major metric to one or more other measures, a bullet graph is beneficial. A bullet graph can help you visualise your objective, the current data set, and past data sets; all in one visualisation if you have a target goal that you need to meet on a regular basis

Source: https://www.tableau.com/data-insights/reference-library/visual-analytics/charts/bullet-graph

6. Cartogram:Choropleth Maps, also known as Filled Maps, are a powerful tool for studying geographic data, especially for maps with a lot of detail (e.g., US by counties or ZIP codes). They make it simple to detect geographical hotspots and then drill down into these areas using several visualisation options.

Source: https://www.pluralsight.com/guides/build-filled-maps-in-tableau

7. Click View:The circle view is a useful representation for comparative analysis. It's the same as using the circle marker on a scatter plot. Every mark is in the shape of a circle and can be used for subsequent actions.

Source:https://interworks.com/blog/ccapitula/2014/10/17/tableau-essentials-chart-types-circle-view/

8. Gantt Chart:Gantt charts are used in project management to depict the length of time between events or activities. As a project management tool, it highlights the interdependencies between activities and illuminates the workflow timeline.

Source:https://help.tableau.com/current/pro/desktop/en-us/buildexamples_gantt.htm

9. Heat Map:In a heat map, data is displayed along with colours. Using one or more Dimensions members and the Measure value, a heat map can be created. Heat Map helps to compare data by colour. For example, how many products have failed to meet the company's expectations, and how many products have exceeded expectations, and so on.

Source:https://help.tableau.com/current/pro/desktop/en-us/buildexamples_highlight.htm

10. Histogram:A histogram is a graph that depicts a distribution's form. It divides values for a continuous metric into bins and segregates a set of data points into user-specified ranges. The histogram, which resembles a bar graph in appearance, condenses a data series into an easily interpreted visual by grouping many data points into logical ranges or bins.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_histogram.htm

11. Scatter Plot (2D or 3D):Scatter plots are a type of graph that is used to show the correlations between numerical data. They are used to depict the link between three variables by plotting data points on three axes. Each column on the X, Y, and Z axes is represented by a marker, whose position is determined by the values in the columns.

Source: https://www.dataplusscience.com/TabCharts/scatterplotsize.html

12. Streamgraph:Streamgraph shows how a number value (Y-axis) changes in response to another numeric value (X-axis). It is a sort of stacked area chart. The relative proportions of the entire can be studied using a stream chart.

Source: https://greatified.com/2018/09/17/how-to-build-a-stream-graph-in-tableau-software/

13. Text Tables:Text tables (also called cross-tabs or pivot tables) are created by placing one dimension on the Rows shelf and another dimension on the Columns shelf. Then, on the Marks card, slide one or more measures to Text to complete the view.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_text.htm#:~:text

14. Treemap:Treemaps are used to show data in the form of nested rectangles. Dimensions define the structure of the treemap, while measures define the size or colour of the individual rectangles. It is a simple data visualisation that can provide information in a visually appealing format.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_treemap.htm

15. Word Cloud:The word cloud is an excellent visual for representing the frequency of words in a given volume of Text. In a word cloud, the most important or unique words from the data are arranged in groups. The main goal of making a word cloud is to provide the viewer with a quick understanding of the important and unique words in the data.

Source: https://www.edupristine.com/blog/creating-word-cloud-tableau

Custom Visuals in Tableau

Tableau also provides a range of custom visuals. Creating them is just a question of one's expertise in Tableau.

1. Dot Distribution Map: Maps that help to spot visual clusters are known as point or dot distribution maps. Dot distribution maps are excellent for displaying how data points are dispersed.

Source: https://help.tableau.com/current/pro/desktop/en-us/maps_howto_pointdistribution.htm

2. Network:Nodes and edges make up a network graph. By connecting nodes with similar features, network visualisations show relationships between items. A network graph is a type of data visualisation that allows consumers to quickly grasp data relationships. Nodes are single data points with edges connecting them to other nodes. The relationship between two or more nodes is represented by edges. This enables the user to easily visualise clusters and establish linkages.

Source: https://ladataviz.com/2019/12/15/build-a-network-graph-in-tableau-in-three-steps/

3. Polar Area:The Polar Area Chart, also known as the Coxcomb chart, resembles a pie chart except that all of the slices have the same angle and the length of the slice that extends spirally from the centre represents quantity.

Source: https://tableau.toanhoang.com/creating-a-polar-chart-in-tableau/

4. Radial Tree:A radial bar chart is a type of pie chart. Like a pie chart, it depicts the relationship of parts to the whole, but it can also include subcategories for each part of the total. Each category in the data series plotted in a radial bar chart is assigned a different colour, whereas all subcategories are assigned the same colour.

Source:https://boraberan.wordpress.com/2014/12/31/radial-treemaps-bar-charts-in-tableau/

5. Timeline:The timeline chart, as the name implies, depicts the significant events that occur in the month, year, or even day. The timeline can also be used as a calendar to display forthcoming events.

Source: https://playfairdata.com/how-to-make-a-timeline-in-tableau/

Types of LOD Expressions in Tableau

1. FIXED
2. INCLUDE
3. EXCLUDE

There are three types of LOD expressions in Tableau:

FIXED: The FIXED LOD expression calculates values based on specific dimensions, regardless of the dimensions present in the visualisation. It creates a virtual level of detail for the calculation. For example, {FIXED [Category]: SUM([Sales])} would calculate the total sales for each category, irrespective of other dimensions used in the visualisation.

INCLUDE: The INCLUDE LOD expression calculates values at the specified level while incorporating the dimension(s) used in the visualisation. It allows you to aggregate data for a specific dimension while considering other dimensions in the visualisation. For example, {INCLUDE [Region]: AVG([Profit])} would calculate the average profit for each region, considering all other dimensions in the visualisation.

EXCLUDE: The EXCLUDE LOD expression calculates values at the specified level while excluding the dimension(s) used in the visualisation. It enables you to aggregate data for a specific dimension while ignoring other dimensions in the visualisation. For example, {EXCLUDE [Product]: SUM([Revenue])} would calculate the total revenue for each product, excluding any other dimensions present.

FIXED LOD Tableau Function

In Tableau, the FIXED Level of Detail (LOD) is the widely used function that allows you to perform calculations using specific dimensions, irrespective of the dimensions present in the visualisation. It creates a virtual level of detail for the calculation, providing precise control over your analysis's granularity.

Steps to Create a FIXED Level of Detail Tableau Function

In Tableau, the FIXED Level of Detail (LOD) function allows you to perform calculations using specific dimensions, irrespective of the dimensions present in the visualisation. It creates a virtual level of detail for the calculation, providing precise control over your analysis's granularity.

The steps to create the FIXED LOD function are as follows:

• • Create a calculated field. (This can be done by clicking on the drop-down next to the “Search” bar in the dimension pane or by selecting “Analysis” and selecting “Create Calculated Field”)
• • Type in the FIXED LOD Formula in the calculated field and drop the field into the view.
• • We can obtain the syntax of the FIXED expressions when we search for “Aggregate” functions in the calculated field.

Example
The following FIXED level of detail expression computes the sum of sales per region:

Beinex+ Tableau Offerings

Beinex has 100 years of combined experience in Tableau and is led by professionals who have successfully delivered Tableau projects in the region for large private and public sector organisations. Our team of Tableau-certified consultants are real-life Tableau business users passionate about Tableau and providing a world-class experience. In addition to providing sustainable analytics solutions, Beinex will help organisations to build superior data visual analytics capabilities internally through our bespoke training programs.