IMAGE IN BAR CHART – NEW LEVELS OF TABLEAU VISUALIZATION!

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How AI Decision-Making is Improving Enterprise Outcomes

The following blog depicts the technique created by Alex Jones (@jusdespommes) blog about “How to… create a chart from an image in Tableau!”.

Show/Hide Containers

Tableau 2019.2 has introduced an option to show/hide containers (when the container is a floating type). Many experts have come up with different use cases such as hiding and showing the filters, parameters, or even a worksheet with a click which occupies much space in the dashboard using this feature.

This feature is pretty simple to use and comes in handy when we want to save some space in our dashboards.

How to Swap image in Bar Chart?

1.Create a simple bar chart in Tableau 2019.2. We have used “Top 5 Grossing Games Worldwide 2018” to create a bar chart and sorted based on the revenue.

2.Remove the label, header and export the worksheet to image. Worksheet > Export > Image (Choose ‘view’ in Export Image dialog box)

3. Open Photopea, which is an online photo editor and open the saved worksheet image.
File > Open > Image

4. Now, download 2 images from online which we are going to use for bar chart.

5.Copy and paste image 1 on top of the bar image. Now we will be having two layers of images one above another.
(We can even re-position, crop the image if needed)

6.Choose ‘Magic Wand’ from the tool bar at left side. Click on the image, and we can see the selection in dotted lines.

7.Choose the top layer (image 1) and click delete. Now we can able to get the below image.

8.The background of the image needs to transparent. So, delete the below layer (bar chart image) using delete icon at the bottom right corner.

9.Now, choose File > Export as > .PNG to save the image as .png format (this will retain the transparent layer)

10.Repeat the same steps with image 2 to get the same effect.
Note: Keep the same resolution and image size as the previous one.

10.1.Now, open Tableau Desktop and bring in the image as floating type.

11.We have to position the bar chart over the image. To make it easy, decrease the opacity of the bar chart and give border.

12. Add labels and tooltip to the bar chart.

13.Now the dashboard looks like this. We can able to hover on bars to see tooltip.

14.Now, comes the trick of using ‘Show/Hide Containers’ to swap image. Bring a container to floating and add image 2 to the container. (Set the padding to zero)

15.Next, we have to position the both images and the bar chart one above another. To make it easy, use x, y axes and size w, h to position accurately.

Note: Make sure the position of the 3 objects (Use Floating Order) follows the below order as all are in floating.

Image 1 (Below layer)
Image 2 inside container (Middle layer – where show/hide option is to be used)
Bar Chart (Top layer)

16.Choose the container and right click at the top right corner. Enable ‘Add Show/ Hide Button.’ Now we can see a floating button.

17.We can customize the button by adding image and tooltip to buttons.

18.We have used below customized images (created with PowerPoint) for buttons to swap between 2 images.

19.Another simple trick is that we can control the transparency of the image by adjusting the opacity of the bars.

20.Take a look at the final dashboard….

SWAPPING IMAGE IN BAR CHART – NEW LEVELS OF TABLEAU VISUALIZATION! Many of us are getting bored of seeing normal plain bar charts. What if we could add image to a bar chart – Pretty cool right? We can now swap between images with a little bit extended trick using ‘Show/Hide Containers’ feature introduced in Tableau 2019.2

Snowflake's Evolution: A Look at the New Features in 2024

1. Snowpark Container Services:

This innovative feature allows you to deploy and manage containers directly within Snowflake. Imagine leveraging secure and scalable infrastructure, similar to Kubernetes, without leaving the Snowflake environment. Use containers to build and run data products like large language models and full-stack applications. You can even utilize containers from the Snowflake marketplace or create custom ones for sharing.

Getting started is simple. Create a Docker image with your code and dependencies, push it to a Snowflake registry, and then create a service, job, or function using the Snowpark API. Snowflake handles the rest, including provisioning, scaling, and monitoring your containers.

2. Snowpark Model Registry:

Data scientists and ML engineers require a secure and efficient way to manage and deploy machine learning models. Snowpark Model Registry, currently in public preview, addresses this need by providing a native solution within Snowflake. This integrated registry allows you to register, manage, and use models and their metadata directly in the platform.
The benefits of the Snowpark Model Registry are numerous:

Centralized Management: Consolidate all your models in a single location, simplifying discovery and access.

Improved Governance: Enforce version control, manage dependencies, and track lineage for better model transparency and auditability.

Simplified Deployment: Seamlessly deploy models into production environments within Snowflake.

With the Snowpark Model Registry, data science teams can focus on building and iterating models, while Snowflake ensures secure and streamlined management.

3. Streamlit in Snowflake for Azure:

Snowflake's commitment to platform flexibility is evident in its expansion to Azure. The general availability of Streamlit in Snowflake for Azure empowers Python developers to create data applications directly within the Snowflake environment. Streamlit simplifies the creation of interactive dashboards and data visualizations, bridging the gap between data and actionable insights for business teams.
Here's how Streamlit in Snowflake for Azure benefits users:

Simplified Development: Leverage Python skills to build data applications without the complexities of traditional web development.

Seamless Integration: Data applications reside within Snowflake, ensuring secure access to governed data.

Empowered Business Teams: Business users can interact with data visualizations and gain insights without relying on IT teams.

Streamlit in Snowflake for Azure empowers a wider range of users to leverage the power of Snowflake data.

4. Security Enhancements in Snowflake Horizon:

Security is paramount for any data platform. Snowflake Horizon takes data security to the next level with a range of enhancements:

Network Isolation: Network isolation for internal stages using AWS PrivateLink ensures secure and isolated data transfer between your environment and Snowflake.

Granular Network Rules: Define network access based on identifiers, offering more control and flexibility for security admins.

Enhanced Authentication: New authentication features allow for customization of the login experience and support for federated authentication using multiple identity providers.

These security enhancements within Snowflake Horizon empower organizations to meet stringent compliance requirements and safeguard their valuable data assets.

5. Snowflake Unistore:

Snowflake Unistore is a game-changer for working with both transactional and analytical data within a single platform, often referred to as Hybrid Transactional/Analytical Processing (HTAP). This feature introduces the 'hybrid' table type, supporting fast single-row operations.

Hybrid tables utilize a new row-oriented store within Snowflake, enabling functionalities typically associated with transactional data stores:

Primary keys for enforcing uniqueness

Additional unique constraints

Referential integrity through foreign key relationships

Declaring secondary indexes

Snowflake's global services layer and query engine seamlessly manage the underlying row and column stores, allowing you to join hybrid and standard tables natively.

6. Snowflake Iceberg:

The Apache Iceberg format acts as a metadata layer for data files stored in open formats like Parquet and ORC. It enables querying this data using SQL, regardless of the specific query engine (Spark, Hive, Impala).

'Snowflake Iceberg' leverages this open format, allowing you to directly query data files stored in cloud storage services like S3 buckets. This eliminates the need to move or copy data into Snowflake while maintaining interoperability for users already working with that data location.

Snowflake Iceberg offers significant advantages over the existing external table type:

Advanced features like snapshot isolation, efficient metadata management, time travel, and schema evolution.

7. Document AI:

Typically, data systems struggle with unstructured data formats like PDFs. Snowflake's answer to this challenge is Document AI, a tool currently in private preview. It allows you to process any document and answer questions using natural language without requiring machine learning expertise.

Document AI draws power from Snowflake's first-party large language model (LLM) built on Applica's generative AI technology. This, combined with Snowflake's support for unstructured data (announced in June 2023), empowers you to store, query, and analyze all data types within the platform.

Document AI represents just one facet of Snowflake's vision for generative AI and LLMs, a dominant trend in 2023 that is poised to continue its dominance in 2024.

Looking Ahead

Snowflake started strongly in 2024 with exciting releases like Snowpark Model Registry, Streamlit in Snowflake for Azure, and security enhancements in Snowflake Horizon. These anticipated features position Snowflake as a frontrunner in the data cloud landscape, offering a comprehensive data management, analytics, and application development platform.

Beinex + Snowflake Partnership

Beinex is a Snowflake Services Partner Premier Tier, and the partnership reaffirms Beinex’s commitment to delivering exceptional data solutions and positions the company at the forefront of industry advancements. Harnessing the true potential of the data, partnership drives innovation and success in the digital era. Belonging to Snowflake Services Partner Premier Tier, Beinex leverages Snowflake’s advanced capabilities and seamlessly integrates them into its comprehensive data solutions.

What’s New in Tableau 2025.1: AI-Powered Insights, Multilingual Support, Private Connectivity, and More

Tableau 2025.1 at a Glance

• Converse with Tableau Agent in six new languages using conversational AI.
• Connect Tableau Cloud to AWS data securely with Private Connect.
• Track KPIs on the go with a redesigned Pulse mobile homepage.
• Programmatically access published data sources using VizQL Data Service.
• Explore new features early with Tableau Cloud preview websites.

Let’s dive into the new updates in Tableau 2025.1:

1. Tableau Agent Multilingual Support

Perform multilingual data analysis. It is now possible for users to converse with Tableau Agent (and Tableau Pulse!) in French, Italian, German, Spanish, Japanese, and Portuguese. More people can now access conversational analytics in their native language.

With conversational AI, the Tableau Agent helps users navigate from data curation to exploration and accelerates all phases of analysis with generative AI. Additionally, because it is based on the Einstein Trust Layer, businesses can be assured that their data will remain safe, secure, and accessible only as intended.

2. Private Connect for Tableau Cloud

Use a dedicated, private connection to safely link Tableau Cloud to AWS data. Private Connect establishes a secure connection between Tableau Cloud and AWS data, ensuring your data remains private and is not made publicly available.

3. Revamped Tableau Pulse Homepage on Mobile

The redesigned Tableau Pulse mobile homepage enhances users' ability to engage with data on the go. With a side-by-side view of at least three key metrics, it allows for instant comparison and faster insight generation.

The clean, intuitive interface enables users to easily spot patterns, track trends over time, and take action quickly, without needing to dig through dashboards. Whether you're monitoring sales, operations, or customer engagement, Pulse ensures your most important KPIs are always within reach.

4. API for VizQL Data Services

VizQL Data Service is an innovative API that allows you to access published data sources in Tableau through programmatic queries rather than visualizations. Designed to maximize the versatility of your Tableau assets beyond traditional visualization, VizQL Data Service helps you tap into the analytic power of your data model anywhere.

It facilitates automating data processes by allowing you to run programmed queries directly on your data sources. By utilizing pre-existing Tableau connectivity and data models, you may more effectively design sophisticated, data-driven apps. You can now utilize your data models in any context, breaking free from the confines of visual tools.

5. Tableau Cloud Preview Websites

Tableau administrators can stay ahead of the curve by gaining early access to upcoming updates through release preview sites. This allows teams to test new features in advance, understand functional changes in a hands-on environment, and evaluate how updates will affect their organization’s unique dashboards and workflows—ensuring a smooth transition when the full release goes live.

In a Nutshell

The Tableau 2025.1 release brings a bunch of exciting updates to make working with data easier, smarter, and more secure. Now you can chat with Tableau Agent in multiple languages, get help with data prep and analysis using natural language, and connect securely to your AWS data using Private Connect. The refreshed Pulse homepage on mobile lets you track and compare key metrics at a glance, helping you make faster decisions wherever you are. Plus, with the new VizQL Data Service, developers can now access and use data directly, without relying on traditional dashboards. And if you're a Tableau admin, you get early access to test new features before they go live, so your team is always ready for what’s next.
Source: https://www.tableau.com/en-gb/2025-1-features

Artificial Intelligence (AI) Model Accuracy Explained: Why It Doesn’t Guarantee Business Success

Artificial intelligence has moved from experimentation to enterprise-wide adoption. Today, organizations across industries are investing heavily in AI. Yet many still misunderstand one critical reality: high AI model accuracy does not automatically translate into measurable results.

AI models can achieve impressive technical scores during model accuracy testing and still fail to generate measurable outcomes after deployment. This paradox is becoming increasingly common. Enterprises should evaluate AI success beyond traditional model accuracy metrics. Understanding why this happens and how to address it is now a foundational competency for any entrepreneur serious about AI adoption.

Explore further: https://www.beinex.ai/generative-ai

Top Cloud Computing Trends 2025: Driving Organizational Success

What is Cloud Computing?

Cloud computing delivers computing resources, servers, storage, databases, and applications, over the internet, replacing traditional local infrastructure. These resources can be accessed anytime, anywhere, and on any device with an internet connection, providing businesses flexibility and agility.

Types of Cloud Services

1. Software as a Service (SaaS): SaaS providers host software applications and make them accessible to users via web browsers on a subscription basis. Popular examples include email services, CRM tools, and project management platforms. 2. Infrastructure as a Service (IaaS): IaaS offers virtualised computing resources like servers, storage, and networking. Businesses can build, deploy, and manage their applications on this infrastructure while maintaining control over the software. 3. Platform as a Service (PaaS): PaaS provides a platform for developers to create, test, and deploy applications without worrying about managing the underlying infrastructure. It offers more control than SaaS and requires less maintenance than IaaS.

Top Cloud Computing Trends for 2025

1. Rise of the Citizen Developer The concept of the citizen developer is reshaping how applications are built. Non-technical users can now create apps using drag-and-drop tools, eliminating the need for extensive coding knowledge. Tools like Microsoft’s Power Platform and AWS’s HoneyCode are leading the way, enabling businesses to streamline workflows and innovate faster. 2. Enhanced AI and Machine Learning Capabilities Cloud providers embed advanced AI and ML features into their services, making intelligent applications accessible to businesses without requiring in-house expertise. Companies like AWS, Google, and IBM are pioneering this space. For instance: • AWS’s DeepLens camera supports machine learning integrations. • Google Lens uses AI to provide real-time information from images. • IBM continues to invest in enterprise AI solutions to revolutionise computing processes. 3. Increased Focus on Automation From enhancing team efficiency to reducing downtime, automation tools are becoming more intuitive and robust. Investments in AI and citizen developer tools further simplify automation, allowing businesses to achieve greater operational efficiency. 4. Continued Investment in Data The need for large-scale data analysis continues to grow, with a shift toward distributed computing environments powered by GPUs. This architecture allows businesses to run real-time analyses on vast datasets, revolutionising how data is processed, stored, and utilised. 5. Heightened Competition The battle between AWS, Microsoft Azure, and Google Cloud Platform is intensifying, driven by competitive pricing, enhanced reliability, and innovative offerings. Expect these giants to continuously improve cost transparency and introduce new features to capture market share. 6. Kubernetes and Docker for Cloud Deployment Kubernetes and Docker are transforming cloud application management. Automating deployment, scaling, and containerised application management enables developers to streamline workflows and quickly deploy scalable solutions. 7. Cloud Security and Resilience As more businesses migrate to the cloud, providers heavily invest in security features like encryption, access controls, and disaster recovery solutions to ensure robust data protection and operational resilience. 8. Multi and Hybrid Cloud Solutions The adoption of multi-cloud and hybrid cloud strategies is growing, allowing businesses to leverage the strengths of multiple providers while maintaining control over their data and applications. These solutions provide flexibility and minimise the risks associated with vendor lock-in. 9. Cloud Cost Optimization Managing cloud costs is a top priority for businesses. Providers are developing tools for cost monitoring, budgeting, and optimisation, helping users make the most of their investments through reserved instance options and sizing recommendations. 10. Edge Computing Edge computing minimises latency and bandwidth requirements by processing data closer to its source. This trend is crucial for real-time applications, enabling faster and more efficient data processing. 11. Disaster Recovery With the rise in natural disasters and cyberattacks, disaster recovery solutions have become a vital focus. Cloud providers offer robust solutions that enable businesses to recover operations quickly and minimise downtime. 12. Innovation and Consolidation in Cloud Gaming Cloud gaming is a booming market, with major players acquiring smaller companies to expand their portfolios. This trend is reshaping the gaming industry, offering more accessible, high-quality gaming experiences. 13. Serverless Computing Serverless computing allows developers to focus on writing code without worrying about infrastructure management. This approach reduces operational costs, increases scalability, and accelerates development cycles.

Summing Up

As these trends demonstrate, cloud computing is transforming dynamically, unlocking new opportunities and redefining how businesses operate. Staying ahead of these trends will be crucial for organisations aiming to harness the full potential of the cloud in 2025 and beyond.

Spatial Parameters: Enhancing Geospatial Analysis and Adding Interactivity to Your Maps

What Are Spatial Parameters?

Spatial parameters in Tableau allow you to dynamically interact with geospatial data within your visualizations. These parameters enable you to select spatial objects like points, polygons, multi-polygons, lines, or collections for calculations. Unlike traditional parameters, which work with values like text or numbers, spatial parameters offer the flexibility to work with geospatial data like coordinates and spatial shapes. You can create spatial parameters in two ways: 1. From a data source: If your data contains spatial fields (like latitude and longitude), you can load these as spatial parameters. 2. Using Well-Known Text (WKT) : This method allows you to manually input spatial data in text format to create custom spatial parameters. Spatial parameters can be used in the same way as other parameters in Tableau, including parameter controls, actions, and dynamic values. Note: Spatial parameters can only be created from spatial data fields (such as latitude and longitude). Creating spatial parameters from text string fields, like a "Country" field, is not feasible as it might be assigned as a geographic role but remains a text field.

Top Benefits of Using Spatial Parameters

Spatial parameters have offered immense possibilities for Tableau experts to analyze and visualize geospatial data. Key benefits are listed below: 1. Cross-Data Source Spatial Exploration With spatial parameters, you can explore spatial relationships between data sources that don't support joins. Unlike traditional data sources, which can be limited by join constraints, spatial parameters allow you to compare spatial regions from different datasets. This means you can dynamically analyze relationships across multiple data sources without the need for complex joins. 2. Skip Long Joins Previously, working with large spatial datasets required time-consuming spatial joins. Now, spatial parameters enable you to bypass these long joins and conduct spatial analysis much faster. You can use parameters to compare regions across multiple data sources instantly, even with vast datasets. 3. Interactive and Dynamic Analysis Spatial parameters allow users to visually interact with geospatial data in real time. Like other parameter types, you can use parameter controls to input points, lines, or polygons and adjust your analysis on the fly. This flexibility allows you to dynamically change spatial boundaries and relationships during your analysis. 4. Distance-Based Queries With spatial parameters, you can perform distance-based queries without complex calculations. For example, you can control the size of a buffer and see what lies within that buffer directly from the worksheet. This makes it easy to conduct proximity analysis without the need for complicated formulas. 5. Create Custom Regions Spatial parameters allow you to create custom regions by selecting points, lines, and polygons and combining them into a single parameter. For instance, you can create a sales region by combining multiple states into one spatial parameter. This enables you to tailor regions for specific analysis scenarios.

Spatial Calculations in Tableau 2024.3: What’s New

Tableau 2024.3 also introduces three new spatial calculations to help you evaluate spatial relationships: • SYMDIFFERENCE: Identifies areas that are unique between two regions. • INTERSECTION: Shows the overlap or intersection between two regions. • DIFFERENCE: Displays areas present in one region but not in the other. These new spatial calculations provide powerful tools to analyze and compare geospatial data.

Union Aggregation for Spatial Data

In previous versions of Tableau, the only aggregation available for spatial data was Collect, which groups spatial elements together. Union Aggregation has been introduced in Tableau 2024.3, allowing you to dissolve the boundaries between regions and providing even greater flexibility in working with spatial data.

Validating Spatial Calculations

Tableau 2024.2 introduced the Validate Calculation feature, which can help you detect and correct errors in your spatial data. This tool is handy when working with complex spatial datasets. Tableau's new Spatial Parameters and related features, such as spatial calculations and union aggregation, provide powerful capabilities for geospatial analysis. Whether comparing spatial regions, performing proximity-based queries, or creating custom areas, these features enable dynamic and flexible analysis beyond traditional methods.

How Beinex Can Assist You

Beinex, a premier Tableau partner, provides sustainable analytics solutions to organizations and helps to build superior data visual analytics capabilities internally through our bespoke training programs. Our team of Tableau-certified consultants are real-life Tableau business users passionate about Tableau and delivering a world-class experience. Connect with us for a free demo: https://www.beinex.com/free-tableau-software/