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TABLEAU 2019.2 RELEASE
1 February 2021
Team Beinex
Tableau 2019.2 Release – A Deeper Dive
Tableau has released the newest version of the platform that enhances the way people visualize and interact with data. In this article we’ll go through some of the exciting new changes included in Tableau 2019.2 and how business will benefit from these newly enhanced and released features.
The major release of Tableau has on-boarded some impressive features and functionality that will have big influence in data visualization and business productivity. In this post, we will highlight some significant and exciting features that are a part of this release.
1. Upscale your Viz with Vector Maps:
Vector Maps enhances your mapping experience by rendering a crisp output as you pan, zoom in, and zoom out to explore your geospatial data. This new feature also leverages the census data from American Community Survey (ACS) to incorporate demographics data into your dashboard.
Highlights:
Enhanced mapping interaction with sharp rendering
Improved default maps styles: Dark, Normal, and Light
New map styles: Street, Satellite, and Outdoor
New background mapping styles: subway and train stations, building footprints, terrain, and water labels
Demographics data from American Community Survey (ACS)
Usability:
Above recording is a view of the new map styles that is a part of the Tableau 2019.2 release. Here you can see how simple it is to include and switch between the newly added map layers. The maps have become crisper while zooming and panning.
2. Enable Interactivity with Parameter Actions
Improved parameter actions is another powerful feature that powers up interactivity in your dashboards and help your viewers gain deeper insights into the trends. It unlocks the ability of a viewer to visually change a parameter’s value thereby offering you endless possibilities to create a truly interactive dashboard.
Highlights:
Interactive relationships between data, dashboard, objects, other workbook sheets and web
Enhanced interactivity by creating marks on a Viz
Improved user experience by transferring control to the end user
Allows users to dynamically change SQL queries, drive reference lines, calculations and filters
Usability:
With this feature the end users can dynamically view the parameter values. In the above example, we have created a parameter ‘order date’ and placed Day of order date and sales into the view and we have added reference line for sales. When applied in a parameter action, the values get changed dynamically.
3. Left Nav, Favorites, and Recent for Enhanced Navigation
Navigate seamlessly and find the content you are searching with the improved navigation. The new navigation features will intensify the already powerful content browsing experience. You can easily find your favorite and recent content including projects and prep flow at the top.
Highlights:
Improved access to key pages and content
New quick access navigation bar with personalized content relevancy
Welcome screen with navigation walk-through for new users
Completely re-designed homepage
Usability:
4. Talk to your data with AskData
AskData is a fairly new module and growing quickly in popularity with the 2019.1 release. Tableau is continuing to expand on this by adding streamlined and innovative capabilities to AskData. You now have the option to add calculations on the fly without having to build them into the data source. You can also ask more sophisticated questions and get answers with the enhanced AskData.
Highlights:
Improved interface to allow more conversational interactions
Enhanced language processing to edit statements from an existing question
Features to execute calculations on the fly
Usability:
In the above example, you can experience the power of the natural language processing engine built into the latest version of ask data. When we ask ‘How are my sales doing by numbers?’, the system automatically understands our query and displays the sum of sales by segment. We can further segment it by year by asking further questions or adjust the values according to our requirements.
These are only a few of the many exciting updates Tableau has incorporated to continue pushing the boundaries of data analytics and prepare for the future market changes. A complete list of updates and features could be found in Tableau’s release notes: https://www.tableau.com/products/all-features
Beinex is a digital transformation organization en-rooted with ideas, innovation and unparalleled customer service. Our mission is to transform the way individuals and the organizations work with the data through innovation and experience.
If you are interested in learning more about the latest Tableau 2019.2 features and updates, please contact us at training@beinex.com/ info@beinex.com and we would be happy to schedule a Tableau demo or training for you and your company.
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UNLEASH THE POWER OF PYTHON LANGUAGE TO TRANSFORM DATA USING TABLEAU PREP BUILDER
Tableau Prep Builder is a Tableau product that is designed in a way that could help anyone to quickly and confidently combine, shape, as well as clean their data for analysis. For this, you can start by connecting to your data from a variety of files, servers, or Tableau extracts; data from multiple data sources can be combined. You can bring your tables into the flow pane by drag and drop or double-click, and then clean and shape your data by using operations such as filter, split, rename, pivot, join and union.
Each step in the process is represented visually in a flow chart which can be created and controlled. You can check your work and make changes at any point in the flow. Tableau Prep Builder validates each operation.
Before the current release of Tableau prep, there were multiple requests and suggestions from users regarding the use of scripting language to transform the data, look up for additional information in the remote sources as well as to run complex machine learning algorithms on top of the inputs. Tableau, as always proved, has looked into the user requirements and has come up with a new feature. With the latest release of Tableau Prep Builder 2019.3.1, Tableau has brought up a Python and R integration feature which can add advanced scenarios to Tableau Prep. Using this feature, users can connect their scripts at any point or step in the flow and use up the full power of their scripting language as per their requirements.
Setting up TabPy
TabPy is an open source tool, used by the Tableau Prep Builder to execute Python code. TabPy, short for Tableau Python Server, is the tool through which the Python integration takes place. Script execution is an advanced feature, so the TabPy needs to be setup. You can click on TabPy installation for the detailed installation process.
Now if everything is followed and put together as expected, you will find a port number at the very bottom which is required for connecting to TabPy server. By default, the port value is ‘9004’ and server name ‘localhost’.
SVM classifier implementation
One of the most popular machine learning classification algorithms is the Support Vector Machine classifier. It is mostly used in addressing multi-classification problems. For example:
Given fruit features like color, size, taste, weight, shape, predicting the fruit type.
By analysing the skin, predicting the different skin disease.
Given Google news articles, predicting the topic of the article. This could be sport, movie, tech news related article, etc.
Classifying the twitter replies to different categories, for Sentiment Analysis.
We are going to use the Iris dataset to implement the SVM classifier. The Iris dataset was first time used in Fisher’s classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems. This dataset has four features of iris flower and one target class. The four features are SepalLengthCm, SepalWidthCm, PetalLengthCm and PetalWidthCm. The flower species type is the target class, and it is having three types- Setosa, Versicolor and Virginica.
The SVM classifier is implemented mainly so that the iris features can be used to train the SVM classifier, and this trained SVM model can be used to predict the Iris species type. Now let’s see how this can be done.
You can be do this by connecting the data to Prep Builder and adding the prewritten python script to the flow. The script will read the data from Tableau Prep and will run the SVM classifier.
The ‘train data’ is used for training the SVM model and the flower species is predicted for the ‘test data’. The result for this is sent back to Prep Builder which we can then use for further analysis.
With this new scripting support in Prep Builder, it’s now easier than ever to implement complex data transformation scenarios which can go well beyond the built-in capabilities of Prep. This scripting feature can be used from simple calculations to complex machine learning models and fetching data from the internet.
BEINEX CONSULTING WINS ALTERYX 2020 PARTNER OF YEAR AWARDS, MIDDLE EAST
Beinex Consulting has been awarded as the Alteryx 2020 Partner of the Year, Middle East at the Alteryx Partner Summit and Awards virtual event along with 15 other winners from North America, LATAM, and EMEA.
During the Alteryx Summit, ‘Your Road to Revenue’, Alteryx celebrated the achievements and commitment of their partners to the Alteryx business and its customers. Beinex Consulting was awarded on the level of engagement in the Alteryx partner program and its efforts around driving innovation, growing revenue, and empowering Alteryx customers to solve our world’s most pressing business and societal issues in the Middle East Region.
Selected among top Middle East Alteryx partners, Beinex demonstrated excellence in delivering end-to-end analytics transformation services that revolutionised multiple industries in the Middle East.
Beinex Consulting Founder and Managing Director, Indumon Das indicates further growth for the digital transformation organisation soon: “Beinex continues to make strategic investments to enhance our association with Alteryx and clients in major Middle East markets. This award is a recognition to our continuous growth strategy and focus to be the best Middle East partner”
“Through their ongoing pledge to the Alteryx Partner Program, our partners have demonstrated their commitment to helping Alteryx customers break down barriers and deliver game-changing insights.” – Josh Lewis, VP, Global Channels, Alteryx
About Beinex Consulting
Beinex is a digital transformation organization with a broad range of analytics modernization and training services. As a pioneer in analytics and cloud transformation, Beinex’s mission is to transform the way individuals and the organizations work with the data through innovation and experience. Beinex offers a broad range of robust and scalable business intelligence and analytics services to drive effective decision-making and create business value.
Specifically, Tableau on AWS lets you process data more quickly and scale up or down resources as needed. Plus, you can access a range of AWS services to optimise your Tableau setup. AWS also provides tools to help you save money and a secure environment to protect against cyber threats and data breaches. Ultimately, Tableau on AWS enables teams to collaborate more efficiently, taking their data analysis and business intelligence to the next level.
There are several other benefits to using Tableau on AWS beyond scalability, cost, and security. Here are some additional insights:
1. Faster Deployment: With Tableau on AWS, you can deploy new instances of Tableau in minutes rather than days or weeks as you would with on-premises infrastructure. This is because AWS has pre-configured templates for Tableau that make it easy to spin up new instances quickly.
2. Better Performance: Tableau on AWS is designed to exploit AWS's high-performance infrastructure. Tableau runs faster and more efficiently on AWS than on traditional on-premises infrastructure.
3. Integration with Other AWS Services: Tableau on AWS integrates seamlessly with other AWS services, such as Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon EMR for big data processing. This makes building a complete analytics solution easier by using Tableau and other AWS services.
4. Improved Disaster Recovery: With Tableau on AWS, disaster recovery is built. AWS provides automated backup and recovery services to quickly recover your Tableau environment and data if there is a disaster or outage.
5. Global Reach: AWS has data centres worldwide, meaning you can deploy Tableau in the region closest to your users for better performance. This is especially important for organisations with a global presence.
Overall, Tableau on AWS offers several advantages over on-premises infrastructure. By leveraging AWS's scalability, cost-effectiveness, and security, organisations can run Tableau more efficiently and with better performance. Additionally, AWS's integration with other services and global reach make it an attractive option for organisations looking to build a comprehensive analytics solution.
Tableau Server on AWS deployment options
The following list outlines the available options for deploying Tableau Server on AWS: 1. Self-Deployment on EC2 Instance: This option involves users provisioning and configuring an EC2 instance and deploying Tableau Server. This provides the most significant control over the deployment process and can be customised to specific needs. However, it also requires more expertise and effort from the user. 2. Quick Start Deployment: The Tableau Server on AWS Quick Start provides an automated deployment process using AWS CloudFormation templates. This simplifies the deployment process and ensures that best practices are followed. However, it may be less customisable than self-deployment. 3. AWS Marketplace Deployment: Tableau Server is also available on the AWS Marketplace with pre-built AWS CloudFormation templates. This provides a quick and easy way to deploy Tableau Server, with different pricing and instance options public. However, users may have less control over the deployment process than over self-deployment.
Users should evaluate their specific needs and expertise when selecting a deployment option. Self-deployment provides the most significant control and customisation, while Quick Start and AWS Marketplace deployment offer simplified and quick deployment options.
Dynamic highlight bar chart with slicer
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.
Best Cloud Data Management Tools Fit for Businesses of All Sizes
What is Cloud Data Management?
Cloud data management refers to the framework that allows businesses to store, manage, and access their data using cloud-based services and applications. It encompasses the entire data lifecycle, from collection and storage to processing and analysis, while ensuring that data remains secure and compliant with regulatory standards. The flexibility of cloud data management allows organizations to scale up or down based on their needs and optimize data operations, which in turn leads to better decision-making and actionable business insights.
The Importance and Benefits of Cloud Data Management
Cloud data management has become a necessity in the data-centric world. Organizations are immersed with vast amounts of data, which must be efficiently stored, processed, and analyzed. Here are the key benefits:
Scalability and Flexibility: One of the biggest advantages of cloud data management is its ability to scale as needed. Traditional data management systems often require substantial infrastructure investment, but cloud solutions allow businesses to pay only for the resources they use, making it cost-effective.
Enhanced Data Security: With stricter regulations on data privacy (such as GDPR and HIPAA), cloud data management ensures that data is securely stored and compliant with global standards. Cloud service providers offer tools to protect data from unauthorized access and breaches.
Improved Collaboration and Accessibility: Cloud data management allows users to access data from anywhere at any time, enabling remote work and collaboration across geographically dispersed teams.
Disaster Recovery and Business Continuity: Cloud-based data management systems offer advanced disaster recovery options. By replicating data across multiple locations, organizations can ensure that their data is safe and accessible, even in the event of hardware failure or a catastrophe.
Cloud Data Management vs. Traditional Data Management
In contrast to traditional on-premises data management (TDM), cloud data management (CDM) provides enhanced flexibility and scalability. Traditional data management systems require a significant upfront investment in physical servers, storage, and IT staff, whereas CDM enables rapid scaling with minimal financial and physical overhead.
CDM also offers superior disaster recovery by distributing data across multiple locations, a benefit that is difficult to achieve with TDM’s centralized approach. Furthermore, CDM allows team members to access data remotely, enhancing collaboration—something that traditional systems often struggle to provide.
A Hybrid Approach to Data Management
For businesses looking to maintain control over sensitive data while leveraging cloud-based tools, a hybrid approach to data management combines the strengths of both cloud and traditional systems. A hybrid model allows organizations to store sensitive data on-premise while utilizing cloud resources for dynamic, less sensitive data. This approach offers scalability, cost-efficiency, and disaster recovery while keeping critical data secure and compliant with industry regulations.
Top Cloud Data Management Tools
Several leading cloud data management tools dominate the market, offering comprehensive solutions for businesses with various data needs. Here are the top three tools:
1. Amazon Web Services (AWS)
Amazon Web Services offers an extensive range of cloud-based tools and services that allow businesses to manage their data effectively. Notable AWS services include:
• Amazon S3: A scalable storage service designed for temporary and intermediate data storage.
• Amazon S3 Glacier: A low-cost cloud storage service ideal for long-term data archiving.
• Amazon Redshift: A fully managed data warehouse that makes analyzing large datasets using SQL simple.
• Amazon Athena: An interactive query service that allows users to analyze data in Amazon S3 using SQL.
• Amazon QuickSight: A scalable, serverless business intelligence service for building interactive dashboards.
AWS Pricing: AWS follows a pay-as-you-go pricing model, making it highly flexible for businesses of all sizes.
2. Microsoft Azure
Microsoft Azure provides a wide range of cloud-based tools for data management, making it a popular choice for enterprises. Key Azure services include:
• Azure Blob Storage: A massively scalable object storage solution for unstructured data.
• SQL Databases: Managed SQL database services that simplify data management without the need for complex infrastructure.
• Azure Data Explorer: A real-time data analytics service that can handle large datasets with minimal preprocessing.
• Private Cloud Deployments: For businesses looking for more control over their infrastructure.
Azure Pricing: Like AWS, Microsoft Azure also offers flexible pricing based on the services and resources used.
3. Google Cloud Platform (GCP)
Google Cloud Platform offers a range of cloud-based data management services, known for their strong integration with Google’s ecosystem and ease of use. Prominent services include:
• Google Cloud Storage: A fully managed service for storing unstructured data.
• Google BigQuery: A fully managed data warehouse that allows users to run SQL queries on large datasets.
• Cloud BigTable: A NoSQL database service designed for large-scale workloads.
• Google Data Studio: A business intelligence platform for building intuitive dashboards and visualizing data.
• Cloud Datalab: A powerful tool for machine learning and data science projects.
• Cloud Pub/Sub: A messaging service designed for real-time data ingestion and processing.
GCP Pricing: Google Cloud Platform offers competitive pricing with a flexible pay-as-you-go model that caters to various business needs.
Conclusion
Cloud data management is essential for modern businesses looking to stay competitive in the digital era. By offering scalability, enhanced security, improved collaboration, and disaster recovery, cloud data management tools like AWS, Microsoft Azure, and Google Cloud Platform provide a comprehensive solution to managing data efficiently and effectively. As data grows in volume and complexity, leveraging these tools will be key to driving innovation and maintaining a competitive edge.
1 February 2021 
Team Beinex 
During the Alteryx Summit, ‘Your Road to Revenue’, Alteryx celebrated the achievements and commitment of their partners to the Alteryx business and its customers. Beinex Consulting was awarded on the level of engagement in the Alteryx partner program and its efforts around driving innovation, growing revenue, and empowering Alteryx customers to solve our world’s most pressing business and societal issues in the Middle East Region.
Selected among top Middle East Alteryx partners, Beinex demonstrated excellence in delivering end-to-end analytics transformation services that revolutionised multiple industries in the Middle East.
Beinex Consulting Founder and Managing Director, Indumon Das indicates further growth for the digital transformation organisation soon: “Beinex continues to make strategic investments to enhance our association with Alteryx and clients in major Middle East markets. This award is a recognition to our continuous growth strategy and focus to be the best Middle East partner”
“Through their ongoing pledge to the Alteryx Partner Program, our partners have demonstrated their commitment to helping Alteryx customers break down barriers and deliver game-changing insights.” – Josh Lewis, VP, Global Channels, Alteryx
About Beinex Consulting
Beinex is a digital transformation organization with a broad range of analytics modernization and training services. As a pioneer in analytics and cloud transformation, Beinex’s mission is to transform the way individuals and the organizations work with the data through innovation and experience. Beinex offers a broad range of robust and scalable business intelligence and analytics services to drive effective decision-making and create business value.
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.
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.