Time-series Analysis in Tableau and 6 Real-world Success Stories

What's New in Snowflake Summit 2025

The main innovations and improvements revealed at the Snowflake summit 2025 are highlighted in this blog, but there is more to come.

1. Get Started with Agentic AI with No Code

Agentic AI refers to AI systems that proactively assist users by understanding context, initiating actions, and automating tasks. Snowflake's agentic AI experiences enable both technical and non-technical users to utilise and interact with AI and data using natural language, without writing a single line of code. The key features highlighted are:

• Data Science Agent (coming soon in private preview): Your ML copilot that can automate feature engineering, training, and more using plain English.
• Cortex AISQL (public preview): Cortex AISQL enables users to utilize SQL to analyze documents, images, and other unstructured data types. Now includes schema-aware table extraction from complex PDFs.
• Cortex AI Agents (public preview): You can use natural language to ask questions, analyze structured and unstructured data, and take action, all within the Snowflake platform.

Bonus: All of this operates within Snowflake's secure perimeter, maintaining governance and privacy by default.

2. Unified Data Engineering and Interoperability

Snowflake is solving rigid data pipelines and slow ingestion issues with new tools built for flexibility and speed. Let's have a look:

• Snowflake OpenFlow (powered by Apache NiFi): A managed, extensible, multimodal low-code ingestion platform. It includes hundreds of connectors to eliminate data silos. To facilitate smooth ETL processing for AI, OpenFlow transforms data movement within Snowflake.
• Native dbt Integration (coming soon): With Git and AI Copilot built-in, you can develop and run dbt models natively within Snowsight UI.
• Iceberg Table Enhancements: You can work effortlessly with external catalogs using the Iceberg REST API. It supports VARIANT data types and Merge-on-Read for semi-structured data.
• DevOps-Ready: Snowflake Workspaces now support custom Git URLs and Python 3.9. Terraform provider is now generally available.

3. Analytics with AI-Driven Acceleration

Analytics just got smarter, faster, and easier to scale with the following updates:

• Snowflake Semantic Views (public preview): It provides excellent support for BI teams, and you can create consistent, reusable business metrics and entities within Snowflake.
• Gen2 Warehouses: Generation 2 (Gen2) is a modernized version of Snowflake's standard warehouse, featuring enhanced hardware and improved performance capabilities. It provides a 2.1x performance boost for analytical queries with enhanced scans, DELETEs, and MERGEs.
• SnowConvert AI: A free, automatic tool for auto-converting legacy ETL/BI code to Snowflake. It reduces risk and saves migration costs.

4. Smarter Marketplace to Share, Sell & Collaborate Easily

Snowflake is making it easier to monetize AI apps and datasets using:

• Cortex Knowledge Extensions: While maintaining IP protection, embed real-time info from Stack Overflow, AP, Packt, and more into your apps.
• Semantic Model Sharing (private preview): Rely on natural language to explore datasets across internal teams or external partners.
• Agentic Snowflake Native Apps: You can easily build and sell no-code, AI-powered apps directly through the Snowflake Marketplace.

5. The Future of Platform: Adaptive, Governed & Secure

Snowflake isn't just innovating; it's redefining what a modern data platform should be in the following ways:

• Adaptive Compute (Adaptive Warehouses): You can automatically adjust resources based on workload, delivering speed without overspending.
• Horizon Catalog (private preview): It enables users to easily discover and govern external data sources, such as dashboards and relational databases.
• Copilot for Governance (coming soon): Users can utilize this feature to manage metadata, access policies, and security using natural language.
• Security Upgrades: To improve account protection, enhanced MFA, password policies, and a revamped Trust Center are being implemented.
• Snowflake Trail: You can debug pipelines and optimize your generative AI agents and apps using new observability features.

Summing Up

Snowflake Summit 2025 showcased the company's push toward an Agentic AI enterprise data platform. Snowflake's advanced features, including Cortex AISQL, OpenFlow, dbt integration, and native app frameworks, are democratizing AI and making it easier for users. Whether you're a data scientist, engineer, or business leader, Snowflake's latest innovations promise faster time-to-insight, reduced complexity, and deeper control over your data universe.

How can You Use Alteryx and Tableau for Advanced Analytics

1. Data Preparation with Alteryx

Alteryx provides powerful data preparation capabilities, including data cleaning, data integration, and data transformation. You can make use of it for:

1. Importing data from various sources such as databases, spreadsheets, or APIs.
2. Creating data preparation workflows, connecting different tools to cleanse, filter, aggregate, and manipulate your data. Use Alteryx's visual workflow interface.
3. Deriving additional insights from your data to leverage Alteryx's advanced analytics tools like predictive modelling, time series analysis, or clustering.

2. Advanced Analytics with Alteryx

Alteryx offers a list of advanced analytics tools, such as predictive analytics, spatial analytics, and text analytics, that can be utilised for:

1. Building machine learning models and performing regression analysis or classification tasks.
2. Analysing geographic patterns, performing spatial clustering, or conducting network analysis.
3. Performing sentiment analysis or topic modelling and extracting insights from unstructured text data by using Alteryx's text mining tools

3. Data Visualization and Reporting with Tableau

Once your data is prepared and enriched in Alteryx, you can connect Tableau to the output data and create interactive visualisations, and perform the following:

1. Use Tableau's drag-and-drop interface to create charts, graphs, dashboards, and reports to visualise your data.
2. Leverage Tableau's advanced visualisation features like calculated fields, table calculations, or trend lines to enhance your analysis.
3. Combine multiple data sources, including the output from Alteryx, to create comprehensive dashboards that provide a holistic view of your data and insights.

4. Integrating Alteryx and Tableau

When it comes to pushing data from Alteryx to Tableau, there are indeed a couple of approaches you can consider ensuring a smooth integration between the Alteryx and Tableau platforms. Alteryx allows you to export the prepared and enriched data as a Tableau Data Extract (.tde) or Tableau Hyper Extract (. hyper) file. You can make use of it for the following functions:

Publishing Data Source Directly to Tableau Server:

Connect Tableau directly to the files to visualise and explore the data.

Alteryx allows you to publish data sources directly to Tableau Server, making it accessible to Tableau users.

You can connect Alteryx and Tableau, enabling Tableau users to access and analyse the data from Alteryx in real time.

You can set up a workflow where Alteryx automatically exports updated data to Tableau on a scheduled basis, ensuring your visualisations are always up to date.

Writing Data in Tableau’s hyper Format:

To integrate Alteryx with Tableau, you can:

Write the data in Tableau's proprietary hyper format and

Directly import it into Tableau for visualisation and analysis.

Beinex partnership with Tableau & Alteryx

As the premium partner of Alteryx and Tableau, Beinex offers a unique advantage in leveraging the combined power of these two tools for your business. Our experts can help you unlock the full potential of your data through sophisticated data preparation, advanced analytics, and compelling visualisations that provide deeper insights into your business operations.

With our expertise, you can effectively make data-driven decisions and communicate complex analytics. Whether you need help with implementation, training, or ongoing support, Beinex is your go-to partner for all your data analysis needs. Get in touch with us today and see how we can help you transform your business with the combined power of Alteryx and Tableau.

Learn how Tableau has aided the business in finding lucrative new sources of income and ensuring that airport operations run like clockwork even in challenging circumstances.

The pre-Tableau era in Aviation

The aviation industry used to spend a lot of time doing the same operations on a daily, weekly, or monthly basis before Tableau was introduced and used because most of the data processes lacked the ability to be automated. They had to manually enter this information into Excel and re-create the same report each week to give senior management the most recent traffic data from the airport, for example. Additionally, it may be difficult to find key insights buried in spreadsheets of flight and traffic data, which means that sometimes the companies might have missed opportunities to improve their operations altogether.

Tableau as a problem solver

Now that most of these previous manual operations are fully automated, hundreds of man-hours can be saved each month. Data is prepared in Alteryx, staged in SQL server databases, and then connected straight to Tableau, where it can be instantly refreshed. A new dashboard can be created at once, and it will never require manual upkeep or updating again. Because data is available whenever they need it, employees become more independent and can effectively address their own queries and analytics requirements. Different teams, including those in finance, capacity planning, operations, engineering services, investment planning, and more, use Tableau in this fashion.

Because of Tableau's focus on visual analytics, particularly mapping, it is much simpler to spot important patterns and trends in the data, such as newly popular airline destinations. Complex metrics are much easier to comprehend when you have that visual feel of the marketplace. Tableau is made to maximise geographic data, so you can understand both the "where" and the "why." Without the need for specialised software, everyone can conduct geospatial analysis thanks to out-of-the-box geocoding and beautiful interactive maps of Tableau.

Tableau drives new revenue streams

The Business Development team's main goal is to increase airport activity and attract additional airlines. To do this, companies must create a strong business case showing that there are unmet passenger demands. For instance, Tableau discovered sizable passenger traffic volumes that were travelling indirectly through other hubs to Dubai. They were able to identify and develop new market prospects for passengers who weren't previously served thanks to this information.

It used to take a lot of figures to be done crunching to uncover these insights, but today the teams can investigate the data using an automated Tableau dashboard that is updated monthly with ticketing data from the International Air Transport Association (IATA) database. To examine the most recent trends, Tableau can quickly explore the data on a global heatmap. This method saves companies days of manual analytics effort by providing them with the evidence that they need to develop airline offers quickly and persuasively.

Tableau and day-to-day operations

The airports that are open 24 hours a day, 7 days a week, must function flawlessly 24 hours a day. The weather is one of the biggest risks to this since it can abruptly cause serious interruptions. Fortunately, Tableau may be used to lessen the worst consequences. When the meteorological division issues a weather warning, Tableau examines the planned itinerary for the impacted timeframe and looks for airlines that operate several flights to/ from the same location. Then, to lessen the effects of this interruption while maintaining connectivity and customer service standards, Tableau collaborates with these airlines to proactively reschedule or cancel flights and combine the demand with fewer flights.

Beinex being Tableau Premium Partner provide sustainable analytics solutions and help organisations to build superior data visual analytics capabilities internally through our bespoke training programs. We have 100 years of combined experience in Tableau and are 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 who are passionate about Tableau and delivering a world-class experience. We have successfully implemented Tableau in various industrial sectors in the Middle East and in other countries too.

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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.

An actual representative and one fascinating example of digital transformation is the use of "digital twins," which are virtual reproductions of real-world things that have been given artificial intelligence and real-time data. The ‘thing’ can be anything under the moon, from a jet engine to a car. The physical asset's connected sensors gather data that can be transferred onto the virtual model. Now, anyone seeing the digital twin can see essential details regarding how the physical object is faring in the real world. They can, however, be interpreted in various ways, which tends to conceal their accurate, practical application.

A digital equivalent for a physical entity serves as the foundation for digital twins. Every business connection with its clients involves physical elements, from the automotive to the agricultural industries. With the help of digital twins, businesses will be able to extend the advantages of the software world to their physical assets, better meeting the needs of their digital customers.

How do digital twins get to know everything?

The digital data, twins gather from specialists with in-depth topic expertise from other similar assets, helps them learn on their own. After being created, the twins have sensors that enable it to take in any input from its physical twin. This can be used to identify potential problems, gain knowledge, gather feedback on a product, and more. Additionally, they include and use past data to polish their simulations.

The digital twin's architecture

Customarily, digital twins have three layers:

1. A connectivity layer that uses SCADA, the Internet of Things, or historians
2. A modelling and simulation layer may include a wide range of tools, including artificial intelligence (AI), industry simulators (thermodynamic, fluid-dynamic, chemical, and more), and AI.
3. A layer for insight and visualisation that can be created online, with analytics software, or even with mixed reality

The final layer of these three is called "learning feedback," which enables the use of expert feedback and historical data to alter the behaviour of digital twins and the dependability of the physical twin.

Who stands to gain?

Digital twins can contribute to increased productivity in massive engines and intricate machinery. Like industrial settings with cooperative machine systems, digital twins are excellent in enhancing process efficiency. Those sectors that work on large-scale items or projects have the most success with digital twins. Digital twin technology has been used in Formula 1 racing to streamline the competition. Any racing or sports team could employ the digital twin to use a virtual twin in determining areas for strategy and progress.

Consider real estate as another example; a digital twin would link all systems and provide accurate insights and the capacity to evaluate the process. Managers would then be able to refine their plans, improving the structure's viability and effectiveness. Additionally, it would result in lower expenses.

Last but not least, the digital twin notion in healthcare refers to the development of computer models of diseases or even a virtual human body. Customised medications or therapies might be created using a medical twin for each patient. The following industries are going to reap the maximum benefits of digital twins:

1. Engineering (systems)
2. Automobile manufacturing
3. Aircraft production
4. Railcar design
5. Building construction
6. Manufacturing
7. Power utilities
8. Real estate
9. Sports and Racing
10. Healthcare

Let’s discuss some benefits of digital twins, but these are only the tip of the iceberg while the others are up and coming:

1.Enhance the user experience

Data is essential to comprehend the past, know the present, and anticipate the future. The foundation of any effective user experience programme is effective data management. Digital twins use IoT to collect real-time data from the physical environment. The information gathered is continually analysed, examined, and learned to provide valuable insights. With real-time analytics, businesses may successfully implement user-centric programs.

2.High-quality, innovative products

A competitive advantage that separates the leader from the followers is innovation. Physical asset innovation necessitates significant R&D expenditures. Design, testing, and operation require specialised knowledge due to the high cost of failures. These creative roadblocks can be solved with the help of digital twins. Enterprises can work with the user community to create high-quality offerings in a simulated environment that combines real-time information.

3.Enhance business processes

In terms of consumer annoyance, broken processes and bureaucracy would be at the top. The orchestration, knowledge management, and technological architecture are fragmented and siloed due to the complexity of modern business operations. The numerous systems and processes are brought together under one roof using digital twins, which act as a meta-layer. Digital twins are essential for knowledge management, training, and process optimisation in the complicated future. Additionally, simulations and visualisations support better process management and human learning.

4.Operative flexibility

Operational agility will affect an organisation’s top and bottom lines in a highly competitive marketplace. Black-box algorithms, the enormous amounts of information gathered, and the need for quicker judgments all work against human operators. Digital twins allow a range of diagnostic and prognostic capabilities by utilising enormous amounts of data, technology, and scenario. The human operators can re-enter the process and find strategies for being competitive and flexible.

5.Information security

Information security is a challenge that comes with all the data. Open source, collaborative learning, and knowledge sharing have never had a more compelling argument. We can't advance if data breaches are happening more frequently. Trusted stakeholders could collaborate on a platform provided by digital twins to share information and gain from it. Digital twins can also act as a layer of concealment to protect the confidentiality of the data.

6.Upgraded R&D

Utilising digital twins produces a wealth of data regarding expected performance results, facilitating more efficient product research and creation. Before beginning production, businesses can use this data to gain insights that will help them make the necessary product improvements.

7.Greater effectiveness

Digital twins can aid in monitoring and mirroring production systems even after a new product has entered production to reach and maintain peak efficiency throughout the manufacturing process.

8.Product life cycle

Digital twins can also assist producers in determining how to handle products that have reached the end of their useful lives and require final processing, such as recycling or other actions. They can decide which product materials can be gathered by utilising digital twins.

9.The Future Course

The market for digital twins is increasing, which suggests that even if they are already used in many different industries, demand will persist for a while. The need for digital twins was worth USD 3.1 billion in 2020. It may continue to snowball until at least 2026, rising to a projected USD 48.2 billion, according to specific industry observers.

According to IDC, global spending on products and services that facilitate digital transformation will amount to US$1.97 trillion in 2022, growing at a CAGR of 16.7%. Businesses are transforming the structure of their work to put the client first. Enterprises are taking more significant risks than ever in every aspect of business, from product design to marketing, sales, and even post-sales. Enterprises can use digital twins as a strategy to accomplish the goals of their initiatives for digital transformation.