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Pingahla Accelerated Data Modernization for Snowflake, Powered by Snowflake, will enable joint customers to modernize legacy databases, data warehouses, and ETL/ELT pipelines in months instead of years New York, August 2026 - Pingahla today announced the launch of Pingahla Accelerated Data Modernization for Snowflake on the Snowflake AI Data Cloud. This new offering, Powered by Snowflake, will enable enterprises to automate the assessment, transformation, and validation of legacy databases, data warehouses, and ETL/ELT pipelines, cutting modernization timelines from years to months and landing on a governed, enterprise-proven foundation ready for analytics and AI. "Every legacy modernization effort ends at the same destination: a modern cloud platform customers can actually build on. By pairing our Modernization Accelerator with Snowflake's AI Data Cloud, we're helping enterprises clear legacy risk and land on a platform ready for the analytics and AI workloads their old environment could never support." Said Chris Evans, CRO, Pingahla. By leveraging the Snowflake AI Data Cloud, Pingahla is joining Snowflake in mobilizing the world's data to help organizations modernize legacy infrastructure without the cost and risk of a full transformation program. Pingahla's Modernization Accelerator - proprietary automation IP that complements technologies including Qlik Talend and Informatica IDMC - converts legacy ETL/ELT mappings into cloud-native SQL, Snowpark-based Python, and modern ingestion pipelines. Source data is staged in Snowflake, transformed using Streams and Tasks, and processed through Snowflake-generated SQL and Snowpark notebooks built with the help of Cortex AI, landing on dimensional models and analytics-ready presentation layers within the customer's own Snowflake environment. Many legacy modernization engagements achieve automation rates approaching 80-90%, with Pingahla's engineering and delivery team completing the remainder end to end, from assessment through migration and cutover. Partnering with Snowflake to launch Pingahla Accelerated Data Modernization for Snowflake, Pingahla enables joint customers to retire end-of-life legacy platforms, consolidate fragmented enterprise data, and reach a Snowflake foundation that's ready to run analytics and AI workloads rather than simply lifted and shifted. For example, Pingahla migrated 20-plus Oracle tables to Snowflake for outdoor products manufacturer Petmate using Informatica IDMC mappings, then built a consolidated Snowflake reporting layer for analytics and business reporting, delivered in phases over approximately four to six months and reducing Petmate's dependence on its legacy Oracle environment. Industry-leading applications are Powered by Snowflake. By building tools, applications, and solutions on Snowflake, product and engineering teams can develop, scale, and operate without operational burden, delivering differentiated products to their customers. Snowflake AI Data Cloud Product Partners help customers maximize Snowflake's flexibility, performance, and ease of use to deliver more meaningful insights. AI Data Cloud Services Partners provide industry experience, technical expertise, and strategic best practices to help customers mitigate risk and drive business value with Snowflake throughout their entire data and AI journey. About Snowflake Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 13,300 customers around the globe, including hundreds of the world's largest companies, use Snowflake's AI Data Cloud to build, use and share data, applications, and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE: SNOW). About Pingahla Pingahla helps enterprises accelerate the modernization of legacy data environments onto Snowflake's AI Data Cloud. Pingahla's Modernization Accelerator, proprietary automation IP that complements technologies including Qlik Talend and Informatica IDMC, speeds the assessment, transformation, and validation of legacy databases, data warehouses, and ETL/ELT pipelines, cutting modernization timelines from years to months. Pingahla holds Qlik Strategic Tier Partner status and was named Qlik New Partner of the Year, and is also an established Informatica partner. Customers, including Array Technologies and Petmate, have used Pingahla to modernize legacy data infrastructure and land on Snowflake's AI Data Cloud, ready to run, not just lifted and shifted. Media Contact: Vishal Vikram, vishal.vikram@pingahla.com

Tariff volatility, FX risk, and fragmented procurement data are breaking AI initiatives before they start. Here's what the foundation actually looks like. Every few weeks, another tariff announcement forces manufacturers, distributors, and retailers to revisit sourcing decisions they thought were settled. Whether it is new trade restrictions, FX shifts on a key currency pair, or a freight disruption in a critical lane, one thing has become clear: companies can no longer afford to wait until month-end to understand their exposure. The organizations that are managing this well are not doing it with better spreadsheets. They are doing it with a data architecture that lets them answer the right questions before the announcement, not after. And the organizations that are struggling are not struggling because they lack talent or ambition. They are struggling because their data is not connected in a way that supports real-time decisions. I see this pattern repeatedly across manufacturing, retail, and consumer goods. Leadership invests in an AI initiative, builds a pilot, and six months later the results are inconsistent, hard to explain, or impossible to act on. The AI tool is rarely the problem. The problem is not the AI. The problem is the data underneath it. AI does not eliminate data problems. It amplifies them. That observation drives everything Pingahla and Qlik built together. And it is why we think the most valuable conversation a supply chain leader can have right now is not about which AI tool to choose. It is about whether the data foundation is ready to support it. Why Supply Chain Data Is Harder Than It Looks Ask most supply chain teams to answer any of these questions in under an hour: Which products carry the most tariff exposure right now? What happens to contribution margin if tariffs on goods from a specific country increase by fifteen percent? Which suppliers represent the greatest concentration risk? Which trade lanes should we consider rerouting given current FX conditions? In most organizations, answering those questions requires a meeting, a data pull from IT, a spreadsheet reconciliation across three systems, and a finance review. By the time the answer arrives, the conditions have changed. This is not a people problem. It is a structural one. Supply chain data typically lives in at least five to eight separate systems: an ERP for orders and inventory, a TMS or freight forwarder portal for logistics, a procurement platform for supplier costs, government APIs for tariff schedules, third-party FX feeds, and supplier portals that export data in formats that do not match anything else. For organizations running SAP, Oracle Fusion, Dynamics, Infor, SAP Datasphere, Snowflake, Databricks, or Salesforce, the integration challenge compounds across each additional system. None of these systems share a common definition of a SKU, a landed cost, a contribution margin, or a trade lane. Finance is working from one set of numbers. Procurement is working from another. Supply chain operations are working from a third. And when an executive asks what the tariff exposure looks like right now, nobody can answer without running a meeting first. That is the environment in which most organizations are now trying to deploy AI. What Agentic AI Actually Needs to Work Qlik recently published a guide on operationalizing agentic AI, and the opening observation is worth sitting with: most companies are not struggling to try agentic AI. They are struggling to get real value from it. The guide identifies exactly what happens when AI runs on a poor data foundation: inconsistent data quality, lost business context, systems that strain at scale, slower performance, and rising costs. Every one of those failure modes is a supply chain problem that shows up in the market regularly. This matters because agentic AI operates differently from a dashboard or a reporting tool. It does not just surface information. It reasons across data, recommends actions, and in some configurations initiates workflows. If the data it reasons on is fragmented or untrustworthy, the decisions it supports will be fragmented and untrustworthy. You cannot patch a data architecture problem with a better AI model. The good news is that Qlik's guide makes a point worth remembering: you are probably closer than you think. You do not need to rebuild your stack. You need your systems to work together, the data, the logic, and the workflows that connect them. The Architecture in Plain Language The reason Pingahla partnered with Qlik is that solving this problem requires three things working together simultaneously, not one after the other. Luis Bernal, our Solution Architect at Pingahla, wrote earlier this year about the mathematical foundation behind supply chain optimization, arguing that trial and error lacks the rigor needed to run proactive what-if scenarios. The next step in that progression is applying trusted, integrated data as the foundation for AI-driven recommendations. You cannot skip the data architecture step and expect the AI layer to compensate. Why This Matters Right Now Tariff policy continues to shift in ways that are difficult to predict more than a few weeks in advance. FX volatility is running at levels that directly affect landed cost calculations. Supplier concentration risk, particularly for organizations with significant sourcing from a small number of countries or trade lanes, is a board-level conversation in ways it was not two years ago. The organizations managing this well share a common characteristic. They are not faster at reacting. They are set up to model scenarios before the change arrives. They can answer the question 'what happens if tariffs increase by twenty percent on goods from this country?' before the announcement, not after. That capability is not a function of better spreadsheets. It is a function of having the data architecture in place to run those scenarios in real time. Companies that are still consolidating data manually before they can even begin analysis are a full decision cycle behind organizations that have unified their data foundation. In a stable environment that gap is manageable. In the current environment it is a competitive and margin disadvantage. The organizations managing tariff volatility well share one characteristic: they model scenarios before the change arrives, not after. That capability comes from data architecture, not from spreadsheets. Five Questions Every Supply Chain Executive Should Be Able to Answer Today Here is a simple benchmark. If your organization cannot answer these questions within the same business day, without a data pull from IT or a spreadsheet reconciliation, the data architecture work is not done yet: Which suppliers represent our highest tariff exposure, and what percentage of COGS do they represent? What happens to contribution margin if tariffs increase another ten percent on our top three sourcing countries? Which products or SKUs lose margin first under current FX conditions? Which trade lanes or countries should we be evaluating as alternatives, and what is the landed cost difference? Which FX pairs represent the greatest exposure to our procurement cost structure this quarter? If those questions take days to answer today, the goal is not to answer them faster with the same process. The goal is to build the infrastructure where the answers are available on demand, and where scenario modeling runs automatically when inputs change. That is the problem Pingahla and Qlik built this solution to solve. And it is the foundation that makes everything downstream, including AI-driven recommendations and eventually autonomous procurement decisions, actually reliable. Where to Start If any of the above resonates, a few resources worth your time: Watch the webinar replay: https://www.youtube.com/watch?v=O8AAvYHbEQU&t Talk to us directly at sales@pingahla.com In 20 minutes, we will show you: Where your tariff exposure actually sits, by supplier, SKU, and trade lane Which suppliers create the most margin risk under current conditions How quickly your current architecture could support real-time scenario modeling Whether your data foundation is ready for AI-driven supply chain decisions TALK TO THE PINGAHLA TEAM — sales@pingahla.com

Every supplier and manufacturing company experiences the same challenge in one way or another: how can I buy, transport and sell my raw materials and goods from point A to point B as fast and optimal as possible? Variables such as the price of the raw materials, source, target, lane, currency exchange, and lately one of the most influential the tariffs start to stack one over the other, making it very complex to answer this question. Usually what companies do is iterate over their options based on previous experience, in other words, by trial and error. This is a method that is not inherently bad, but it lacks a strong mathematical foundation, which is necessary to explore different questions inside the business and, most important of all, to run what-if scenarios that allow the customer to be on top of future events and have a more preventive than reactive strategy. As mentioned before, a strong mathematical model allows to include all the major variables involved during the supply chain of a company and to test different configurations of the process. Questions like: what happens if I buy my raw materials from India instead of China? Or what if the Yuan experiences a sudden spike in exchange by the end of the year? Or even, what if the tariffs from China increase from 10% to 30%? We can see that not only the economic and geographical variables come into play, but also political events play a major role in the formulation of the phenomena. A very simplified but powerful version of the mathematical model looks as follows: Where is the function we want to optimize, which means finding the maximum or minimum of the function. In this case it is finding the maximum revenue of the company, but in other cases it can be finding the minimum delivery time or even the shortest route. is the weight for all the different possible factors that affect the function and is the actual variable of the function. So we can appreciate that we can basically add any type of event that can affect the revenue of the company and assign a specific weight to each of the events, so we can quantify its effect in the final outcome. This type of formulation is very common in many different areas of physics, economics and, in this case, also on supply chain modelling. With this flexible and powerful tool we can add parameters and constraints to the model and test what-if scenarios on the optimization model. For this model to work we need to have the most up-to-date data. One example is the value of the currency exchange. This is one of the most fluctuating variables but, fortunately, one with the most available data. Having a real time API that connects to the values posted by the global market allows us to have the most precise exchange data down to the minute. More challenging variables such as the tariffs are more unpredictable and can change from one day to another based on the decision of one single person. One of the strategies to keep up with the tariff variation is being alert to the news to constantly monitor any type of developments. Of course, this cannot be done manually as it would take a great amount of effort; instead, web monitoring tools can be implemented to keep up with the latest developments from trusted news web sites and transfer the updated values of the tariffs into the model. Now, if we wanted to find out what is the shortest possible route between a given list of geographical points that our raw materials need to cross before getting to their final destination, this is known as the travelling salesman problem (TSP), which is one of the simplest problems to describe but incredibly hard to solve optimally, as its total number of possibilities grows exponentially with the number of points the route needs to include. No exact solution exists for this problem, but many different algorithms offer approximations and heuristics like Nearest Neighbor, Dynamic Programming, Lin-Kernighan heuristic, etc. Next, all of this formulation of the problem is worthless if there is no way to communicate it to the actual analysts and stakeholders of the company. That’s why having an Alerts and Exceptions section in a BI visualization platform is key to inform events such as "supplier late >2 shipments this month" or "Random inspection rate increase, clearance +0.3 days", to keep the relevant people inside the company informed and to understand why specific configurations of the supply chain are selected over others. One of the best visualizations to appreciate most of the supply chain process is the following sankey chart: The nodes represent the different suppliers, country of origin, materials, destination region and product family, while the thickness of the lines that connect each of the nodes represents the revenue. In conclusion, we can see that the supply chain optimization problem is a complex challenge that involves many different variables, from raw materials and currency exchange to tariffs and political events. Instead of relying on trial and error, what companies can do is build a mathematical model based on weighted variables, which allows them to quantify the effect of each factor and run what-if scenarios so the business can be on top of future events instead of just reacting to them. Of course, the model is only as good as the data behind it, and that’s why having real time FX feeds and web monitoring tools for tariffs is so important to keep the values up to date. On top of this, algorithms like the ones used for the travelling salesman problem help to find good approximations for routing problems that are too complex to solve exactly. Finally, all of this work is worthless if there is no way to communicate the results to the actual stakeholders of the company, and that’s where BI alerts, exceptions and visualizations like the sankey chart come into play to keep the relevant people informed and to understand why specific configurations are selected over others. With this approach companies can stop reacting and start anticipating, which is the real competitive advantage in a global market where tariffs, currencies and political events can change from one day to another. Talk to Pingahla's supply chain analytics team. We'll show you exactly where your tariff, FX, and sourcing exposure sits and what it's costing you per quarter (fill the form at the bottom of the page) Or feel free to send a custom message to info@pingahla.com.

Introduction Applications created and built to use cloud computing platforms are known as “cloud-native” applications. Cloud-native testing is a specialized approach to software testing that focuses on applications and services designed for cloud-native architectures. It includes testing of microservices, orchestration tools, and other cloud-specific components. Cloud-native testing includes various types of testing, such as unit, integration, security, performance, and scalability. It plays a crucial role in phases of the software development lifecycle.   Differences between Traditional Testing and Cloud-Native Testing Environment : Traditional testing often occurs in controlled, static environments, while cloud-native testing is designed for dynamic and scalable cloud environments.   Scope : While traditional testing usually concentrates on large systems, cloud-native testing uses orchestration tools and microservices.   Automation : Cloud-native testing heavily relies on automation to test frequently changing cloud-native components, whereas traditional testing may involve more manual processes.   Scalability : Cloud-native testing involves testing for scalability and resilience in response to fluctuating workloads. Traditional testing does not address this feature.   Tools : Cloud-native testing often requires specialized tools designed for the cloud-native ecosystem, whereas traditional testing uses more traditional testing terminologies.   Security : Security testing in cloud-native applications must address data container vulnerabilities and cloud-specific security concerns, which are less important in traditional testing.   Dynamic Nature : Cloud-native testing must adapt to the dynamic nature of microservices and orchestration, while traditional testing deals with more static application structures.   Objectives of Cloud-Native Testing The primary goals and objectives of cloud-native testing include -   Reliability : Ensure the reliability and stability of cloud-native applications, especially in dynamic and distributed environments.   Performance : Verify that applications can handle varying workloads efficiently and without decrement in performance.   Security : Identify vulnerabilities and security weaknesses specific to cloud-native components, including data containers and microservices.   Scalability : Testing the application's ability to scale up or down to meet changing demands effectively.   Compatibility : Ensure our cloud-native application works seamlessly across various cloud providers and platforms.   Continuous Feedback : Provide ongoing feedback to developers and operations teams to improve the application continuously.   Compliance : Validate that the application complies with industry standards and regulations, especially when handling sensitive data.   Cost Efficiency : Ensure that the application's resource utilization is optimized over time to minimize cloud infrastructure costs and IPU consumption.   Automation : Implement automated testing processes to keep pace with frequent code changes and deployments in a cloud-native environment.   Key Advantages Of Cloud-Native Applications Agility : Cloud-native applications make rapid development, deployment, and iteration possible. With the help of infrastructure, developers can easily bundle and deliver new features or bug fixes. Businesses may react to market changes more quickly, publish updates more frequently, and gain a competitive advantage.   Cost-effectiveness: Cloud-native applications maximize resource use by scaling up or down in response to real demand. Thanks to elastic scaling, organizations can distribute resources as needed, avoiding the needless costs associated with over-provisioning. Additionally, cloud-native architectures lessen the need for expenditures in on-premises infrastructure by utilizing cloud provider services.   Better management : Testing cloud-native applications also helps to simplify infrastructure management, which is an additional advantage. Serverless platforms such as AWS and Azure have eliminated the need for businesses to worry about things like allocating storage, establishing networking, or provisioning cloud instances.   Collaboration and Communication : Cloud-native testing promotes teamwork and communication among development, testing, and operations teams. Effective communication channels and collaborative tools aid in the timely sharing of test plans and results and the resolution of concerns.   Automation and Continuous Testing : Test automation and continuous testing are the main focus of cloud-native testing. Because automated tests can be run quickly and often, every change can be completely tested before being pushed to production. Cloud-native applications are designed to withstand failures. Owing to its distributed architecture, the application can function even in the event of a failure in one of its services, providing a higher degree of fault tolerance and lessening the effect of failures on the system as a whole.   Scalability : Scalability is a huge advantage of testing cloud-native applications. Cloud-native applications are designed to scale with ease. Applications can dynamically distribute resources based on demand by leveraging containerization and orchestration platforms. This allows programs to function at their best by efficiently handling different workloads.   Flexibility and Portability : Cloud-native applications are platform-independent. They can be implemented on a variety of cloud providers or even on-premises. Because of this flexibility, businesses can choose the cloud provider that best meets their requirements and even switch providers as needed. Cloud-native applications are now exploding in the tech industry. Considering its vast benefits, most enterprises are moving towards the cloud as fast as possible.   Common Challenges in Cloud-Native Testing Testing Serverless Functions: It might be difficult to precisely estimate and evaluate response times for serverless functions because they sometimes have varied cold start periods. Since the local environment frequently varies greatly from the cloud, testing serverless functions locally can be challenging.   Handling Stateless programs: Testing becomes more difficult because stateless programs rely on other databases or services for data storage. Testers must consider the application's statelessness to ensure that each request may be handled separately.   Complex Interactions Among Microservices: When there is asynchronous communication, it can be difficult to coordinate testing across several microservices. It might also be difficult to confirm that microservices operate in a union since modifications made to one service may impact others.   Diverse Cloud Environments: Vendor Lock-In—Because every cloud provider offers different features and services, it might be challenging to guarantee cross-platform compatibility. Service Dependencies—Testing can become challenging when an application uses third-party APIs or various cloud services.   Best Practices for Efficacious Cloud-Native Testing Shift Left : Perform testing as soon as possible during the development phase to identify problems early and lower the cost of addressing them later.   Leverage Automation : Invest in automated testing to stay up with rapid deployments and changes in cloud-native settings. Make consistent use of infrastructure as code while establishing test environments.   Chaos Engineering : Utilize chaos engineering to find weak points in your system and ensure it can fail gracefully. To continuously increase system resilience, conduct chaos experiments regularly.   Monitor and Observe : To acquire knowledge about the performance and behavior of applications and implement strong monitoring and observability procedures.

Imagine you would like to do an analysis for your current job or business. For this analysis you would like to use graphs, tables, maps and add a layer of interactivity so the users can obtain different insights from your visualizations and obtain valuable information from the business that will help you take informed decisions in the future. Now, the question arises: which business intelligence (BI) visualization tool should I use? Tableau or Dash Plotly? This article will try to attempt to help you answer this question and which visualization tool is more suitable for you. I am a Sr. Data Engineer for the data integration consultant company Pingahla. Throughout my career, I’ve helped companies to ingest, process and present their data in such a way that helps managers and stakeholders take informed decisions in their businesses. I’ve used Tableau and Dash Plotly to present these results in more than 10 different projects. Additionally, I possess the Tableau Desktop Specialist Certification and the Tableau Certified Associate Consultant Certification. I have my fair share of experience using both tools and I will share to you what are the benefits and downsides of using each of them. Nevertheless, this is just my opinion so take with a grain of salt what I am about to present to you. The main difference between using Tableau and Dash Plotly is its complexity. Tableau is designed in such a way that the “drag and drop” mechanic plays a huge role while creating visualizations. For example, if you want a sales over time analysis you just drag the time in the column shelve, the sales in the rows shelf and there you have it, a visualization made in less than thirty seconds. You can also select easily what type of graph you would like to use and format it in the style you want. With Dash Plotly things are not so simple. As these types of visualizations are constructed using the libraries Dash and Plotly from Python, all the frontend and backend of the dashboards are made by writing Python code, that could be a big condition for many people who do not know yet how to program. As discussed before, speed is also a factor to consider. In Tableau, complex visualization and dashboards may be created in a matter of seconds if you have a clear idea on what you want to create as many of the functionalities are intuitive. This means that if you don’t know exactly how to make something, you can experiment and look around the window and will probably find the answer quite quickly. In Dash Plotly the story is different. You need to keep writing and testing your code to check its functionality frequently (as with any piece of code). This process is prone to make mistakes easily as just one wrong character can damage your visualization. Patience and attention to detail are key skills that are needed for programming and especially if you are making dashboards. Trouble shooting can also be a challenging task in which seamlessly unimportant things such as indentation of the code can make the difference on running the code successfully or crashing it. If you would like to learn more on how to use the Dash Plotly framework, there is a huge community surrounding Python scripting and specifically for Dash Plotly in Youtube that is the Charming Data channel from Adam Schoreder. Nevertheless, the process is undoubtably slower than it would be in Tableau. Another factor to consider is the interactivity that Tableau can offer on its dashboards. Suppose you have a dashboard that presents the sales of different products in the USA. The dashboard has a map of the USA on the top and at the bottom is presented the number of sales by product. Imagine you would like to know the number of sales from just one city and not the entire country, normally you would do this using a filter in which you pick a city from a list. However, Tableau offers the option of selecting one city on the map and automatically affects the amount of sales graph by filtering it by the selected city. This process is called a dashboard action, and there are several of them in which you can alter the results of one or several graphs depending on an action made on the target graph. This offers an interesting layer of interactivity that the user can work with to obtain more specific insights during the analysis. Unfortunately, Dash Plotly does not offer this kind of options and different graphs in a dashboard cannot be affected be the action of another graph. For any data analysis project, it is necessary to do some kind of data processing whether it is light or heavy. In Tableau Desktop and Tableau Public it is possible to make light data processing using table calculations which are several functions that ranges from string and numerical up to date and aggregate functions. These functions, normally, are simple to use and each one of them contain a small description and example to help the user on how to choose them properly. Level of Detail (LOD) calculations are also offered, which are calculations that do not depend on the level of granularity of the view and are executed before dimension filters, measure filters and table calculations (this is one of the most complex topics in Tableau so don’t worry if it doesn’t make a lot of sense to you). If a heavier data processing wanted to be achieved using Tableau, there is a specific product named Tableau Prep Builder is a tool design to make preparing data easy and intuitive to combine, shape and clean data for analysis in Tableau. In the case if Dash Plotly, as it is written in Python code there exists a huge number of data processing libraries that can help you do any kind of light or heavy data transformations, like Pandas, Polars and Numpy to name a few. In this aspect Dash Plotly is much more flexible and allows a wide range of possibilities that offers the suits the user for a given situation. When a dashboard is finally created and you would like to share your findings with the world, with Tableau you can accomplish this by using Tableau Cloud, Tableau Server, Tableau Online or Tableau Reader depending on your specific needs. With Tableau Server you can assign different roles to your colleagues which define what actions they can implement on the dashboards, ranging from Viewer, Explorer and Creator. With Tableau Online, you don’t need to worry about scaling, as Tableau is in charge of handling these kinds of features so that you don’t have to. With Dash Plotly, as the whole dashboard is written in Python code, it is sufficient to find any kind of script host server that can run the code. There are many alternatives, in the past Heroku was very famous as it offered a free tier for developers. Unfortunately, that is no longer the case. Now, Render is a great alternative to host dashboards design using Dash Plotly as it is free to use. Nevertheless, if heavier computation power is needed or scaling up is also a need, the free tier will not be sufficient and better capabilities should be investigated for payment tiers. You can also deploy your Dash Plotly applications using AWS and if you don’t know how to in Pingala’s YouTube channel there is a video detailing how to do it step by step. Now regarding the cost of using each BI tool; Tableau is known for being an elevated cost program with subscriptions ranging from $12-$70 USD per user. Tableau Public is a free version of the software in which you can learn almost 90% of the capabilities of the paid version (great for anyone who is planning to learn how to build dashboards with Tableau). But for connecting to databases and sharing the dashboards to the community, a paid version of Tableau is required. One of the main advantages of the Dash Plotly framework is that is completely free. You can download Python, any Python IDE like Visual Studio and PyCharm, download the required libraries and start creating your own dashboards. As mentioned before, the deployment is also free if you do not require big computational power. Another advantage of using Dash Plotly is that, as mentioned before, there is a limitless number of libraries that can be used from machine learning process up to web scrapping to the internet, giving a huge potential to the dashboards by being fed by any type of information or algorithm a trait that is not available in Tableau. Tableau does have extensions or plugins that allow to increase the capabilities of the program but is not near to what Python libraries have to offer. In conclusion, if you are looking for a fast solution to build a dashboard that is not hard to use and are willing to pay a good amount of money to use the product, Tableau could be a good option for good. If you are looking for a much robust solution that may need special capabilities only offer on specific libraries, you already now how to program and prefer a free to use solution, then Dash Plotly is the better option for you.

The Pingahla Informatica MDM team has been on an incredible journey helping customers implement and scale their Informatica MDM environments. Over the past few months, we’ve been working on something we’re really excited about - an accelerator designed to make duplicate detection in Informatica Cloud MDM more flexible and easier to integrate into modern applications. Today, we’re introducing Pingahla’s Informatica MDM Duplicate Shield . This new accelerator acts as a microservice for duplicate detection and validation , built specifically for organizations using Informatica Cloud MDM. It allows teams to leverage Informatica’s powerful matching engine while maintaining full control over their own user interface and workflows. What Is Pingahla’s Duplicate Shield? Pingahla Duplicate Shield enables organizations to access Informatica’s deduplication capabilities through APIs rather than being limited to the standard out-of-the-box UI. This means teams can integrate duplicate detection directly into their own applications, portals, or customer workflows. With Duplicate Shield, you can: Connect to Informatica’s powerful deduplication engine through APIs Run real-time or batch duplicate checks Display suspected duplicates inside your own branded frontend Match records across multiple brands or customer touchpoints Seamlessly integrate with Informatica MDM SaaS A Simple Example Imagine a customer signing up for a loyalty program on your website. Instead of using Informatica’s default UI, you can integrate Duplicate Shield directly into your sign-up form . The service will call the Informatica backend, check for existing records using IDMC and MDM, and instantly flag potential duplicates. Even better, the results can be displayed within your own portal interface , maintaining your brand’s look and feel. Why We Built It Many customers love Informatica’s backend capabilities—especially its advanced matching engine and scalability . However, we repeatedly heard the same request: “Can we keep the power of Informatica’s matching engine but use our own UI?” Organizations often want: Custom sign-up flows Embedded duplicate checks inside CRM or applications Interfaces that match their brand identity Unfortunately, the default Informatica UI has limited customization options . So instead of waiting for a solution, we built one. Pingahla Duplicate Shield delivers: The intelligence of Informatica MDM and IDMC The flexibility to build your own front-end experience What’s Next? Pingahla Duplicate Shield is currently in the final stages of QA and packaging, and we’re preparing for general availability . We’ve already been testing it with select customers, and the feedback has been extremely positive - especially from teams that need more control over how MDM functionality appears within their applications. If you are: Working with Informatica Cloud MDM Looking for greater UI flexibility Struggling with white-label limitations We’d love to connect. Reach out to the info.pingahla.com  to learn how Duplicate Shield can help extend your Informatica MDM implementation.

The India AI Impact Summit 2026 made one thing crystal clear. AI's explosive potential in 2026 depends less on flashy algorithms and more on data maturity. Poor data quality, silos, governance gaps, or unreliable pipelines can derail even the most advanced generative AI, predictive models, or agentic systems. As organizations race to adopt AI for economic growth, operational resilience, and innovation, the real differentiator is a rock-solid data foundation. That is exactly where Pingahla shines. As a boutique data management and cloud analytics firm, we do not just support AI; we enable it. We enable it to deliver real and trustworthy impact. Founded by passionate data experts with the core belief of "Excellence in Everything We Do," Pingahla helps organizations transition to a truly data-driven future. What Pingahla Brings to the AI Table Pingahla partners with the world's leading platforms, such as Amazon Web Services, Microsoft Azure, Informatica, Talend, now part of Qlik, Databricks, and Snowflake, to deliver: Seamless data integration and delivery Moving beyond legacy ETL to modern ELT pipelines that feed AI workloads efficiently. Cloud-based analytics solutions Scalable environments for real-time insights, dashboards, and advanced analytics that power AI decision-making. End-to-end data pipelines and quality management Including our proprietary Pingahla Data Quality Accelerator (PDQA), infused with AI capabilities to clean, reconcile, and enrich data. This reduces bias, hallucinations, and errors in downstream AI models. Strategic consulting to turn raw data into actionable insights From data strategy assessments to governance frameworks that ensure compliance, security, and trust. We operate across on-premise, cloud, and hybrid setups, optimizing ROI on technology investments while enabling secure and governed data access. Our recent recognitions, such as being named Qlik's New Partner of the Year, and solutions like the AI-enhanced PDQA on Databricks, demonstrate how we inject quality and intelligence directly into data flows. In 2026, AI is not just about algorithms. It is about data maturity. The summit repeatedly emphasized that poor data quality, silos, or governance issues derail even the most advanced AI projects. This is where Pingahla fits perfectly. We bridge the gap between ambitious AI visions and practical, reliable execution. Why Data Foundations Are Critical for AI in 2026 The summit's key takeaways align directly with our expertise. Data Quality and Governance as AI Enablers AI models thrive on clean, consistent, and compliant data. Our tools, such as Talend Data Quality Accelerator, Informatica, and the Pingahla PDQA, ensure enterprises trust their inputs while minimizing bias and failures in AI outputs. As highlighted in our recent webinar on tariff, FX, and supply chain risk management with Qlik and Pingahla, AI-powered data reconciliation delivers what-if scenarios and analytics that drive resilient decisions. Cloud-First AI Scaling Partners like AWS, Azure, Databricks, a leader in AI and ML workloads, and Snowflake enable us to build scalable and secure cloud environments. This supports generative AI training, real-time inference, and lakehouse architectures without on-premise limitations. We are helping clients migrate from legacy systems such as Cloudera to Databricks to accelerate AI initiatives. Responsible and Inclusive AI The summit's emphasis on ethics and accessibility mirrors our approach. We prioritize compliance, security across hybrid, on-prem, and cloud environments, data lineage, and democratized access, ensuring AI benefits businesses of all sizes across India's growing ecosystem. Real-World Impact Through Analytics Our cloud analytics power the decision layer that AI augments. This includes predictive insights, demand forecasting, operational optimization, risk detection, and process automation. Clients already use our pipelines to feed machine learning models across industries such as manufacturing, healthcare, CPG, and luxury goods. Pingahla's Fit in India's AI Journey India is emerging as a global AI leader, and the Delhi summit underscored the urgent need for robust data ecosystems. With our Pune office powering Indian operations, alongside our presence in New York, Canada, and Colombia, Pingahla is uniquely positioned to help both local and international clients: Accelerate AI adoption by modernizing data infrastructure Reduce time to value for AI projects through proven integration frameworks such as point-and-click ETL and ELT, and secure data enclaves Build resilient and compliant systems that support long-term AI innovation Whether enabling real-time dashboards for leaders, ensuring data lineage for regulations, or powering cloud migrations for AI workloads, our services make AI practical, impactful, and trustworthy. If you are exploring AI but facing data readiness challenges such as silos, quality issues, governance gaps, or scaling concerns, let us connect. Pingahla is here to turn your data into the high-octane fuel that powers meaningful AI impact. What data challenges are you facing in your AI journey? Share in the comments. I would love to discuss.

If you weren’t able to join our recent Pingahla + Qlik session on Tariff & Supply Chain Risk Optimization webinar , no worries. You can catch the full recording here. Here’s a quick walkthrough of what we covered so you can decide if it’s worth sharing with your finance, supply chain, and procurement teams (spoiler: it is) . Why We Built a Tariff & FX Optimization Solution I kicked things off by framing the problem many manufacturers, CPG brands, and global supply chain organizations are living with every day: Tariffs and FX rates are volatile and politically driven—but your margins and pricing can’t be. Most teams are reactive: they get a static report after  the damage is done. Limited visibility across suppliers, countries, lanes, and FX sources makes it hard to answer simple questions like: “What’s our tariff exposure by lane or product family?” “What happens to contribution margin if FX moves 3%?” “Where can we reroute or re-source to protect margins?” To address that, Pingahla partnered with Qlik  to build an end-to-end, production-ready solution  that: Ingests  data from ERPs, procurement systems, freight forwarders, trade intelligence platforms, and FX feeds using Qlik Talend Cloud Cleans and reconciles  it using our Pingahla Qlik Talend AI Data Quality Accelerator (PDQA) Delivers analytics, what-if scenarios, and optimization recommendations  with Qlik Cloud Analytics , including AI and predictive modelling The Architecture in Plain English Before handing it off to Luis for the live demo, we walked through the high-level architecture: Data Sources ERPs: SAP, Dynamics, Infor, and others Procurement & freight: TMS / freight forwarders, logistics platforms FX & macro: IMF, ECB, and other FX feeds Government HTS / tariff APIs  for near-real-time updates Optional third-party trade intelligence (e.g., Import Genius, S&P Global, etc.) Integration & Transformation – Qlik Talend Cloud Extracts data from all these systems (APIs, files, DBs, message queues) Transforms and harmonizes it into a unified model Applies business rules for tariffs, lanes, materials, and suppliers Data Quality – PDQA Detects duplicates, anomalies, and conflicts across multiple sources (e.g., four FX feeds, one outlier) Surfaces trusted “golden records” for key metrics like FX, tariffs, and costs Ensures dashboards and models are built on validated, explainable data Analytics & Optimization – Qlik Cloud Interactive dashboards and visualizations for business users AI-driven insights and predictive scenarios Optimization recommendations for reroutes, alternate suppliers, and lane selection We designed it to be proactive , not just another reporting layer that tells you what went wrong yesterday. Live Demo: Inside the Tariff & Supply Chain Risk Dashboard Our Qlik Solution Architect, Luis Alejandro Bernal , then walked through the actual dashboard we built for a global paint manufacturing company . He showed four main tabs: 1. Executive Overview & Risk Snapshot At the top: filters like currency, shipment date, product family, and lane , so leaders can slice exposure instantly. Key KPIs included: Total tariff cost Tariff as a % of COGS FX impact at risk On-time delivery rate Contribution margin delta vs. baseline Visuals included: Tariff cost impact on contribution margin over time Top sourcing countries by COGS exposure FX exposure by currency pair The most actionable piece on this page: Lane Optimization – Recommended Reroutes Shows current lane  vs optimized lane Includes rationale (e.g., better cost, lower risk, improved reliability) Can optimize for revenue, cost, time, or a custom business objective Alerts & Exceptions HTS updates Currency alerts Other rules-based notifications that matter to trade, finance, and supply chain teams 2. Raw Material Price & Volatility Analysis Here Luis showed how the solution helps teams manage raw material price risk : Cost of materials over time Price volatility index Tariff impact and FX-adjusted cost High-risk materials  as a % of total Key visuals: Cost share by country  (China, India, US, and others) Raw material price trends & volatility , helping teams choose strategic buy/sell windows 3. FX Monitoring & Exposure This tab focused on FX risk  and how it evolves: Currency FX rate over time Average exchange rate % Volatility index % Rate spread and days in range By selecting a target currency (e.g., CNY), users can: See historical behavior and volatility Identify peaks and troughs Use that insight when layering tariffs and lane decisions on top 4. Supply Chain Risk & At-Risk Suppliers The final tab zoomed out to the full supply chain picture : Total trade value Tariff % Tariff-adjusted value Cost uplift Top at-risk suppliers The standout visualization here was the Sankey diagram : Flows from supplier → origin country → material → destination region → product family Line thickness represents trade value moving through each node Makes it easy to see where risk and value are concentrated in the network This gives a powerful “single pane of glass” view of how tariffs, FX, and sourcing decisions intersect  across the global supply chain. Audience Q&A Highlights We wrapped with a robust Q&A. Here are some of the themes that came up: How often do you update tariffs and FX rates? The model supports scheduled and automated refreshes : As often as every 5–15 minutes  for most sources Real-time  is also possible when the source supports streaming, webhooks, or message queues Can we integrate our own HTS, FX, and trade data sources? Yes. Using Qlik Talend Cloud , we can ingest from: Government tariff APIs Custom HTS files IMF / ECB FX feeds Trade intelligence platforms (e.g., Import Genius, S&P Global) Any third-party API or export your team uses today Everything is configurable to your business rules . Do you support what-if scenarios and hypothetical tariff changes? Yes. The solution includes a scenario engine  where users can adjust: Unit tariff rates FX markups Duty percentages …and see the impact on COGS and contribution margin , comparing baseline vs. adjusted  scenarios before making procurement decisions. Our ERP is heavily customized. How fast can you onboard us? Qlik Talend is built for complex, custom ERPs: Supports 2,000+ connectors (APIs, databases, file formats, etc.) Typical ingestion and mapping can be done in days, not months PDQA further accelerates onboarding by automating data quality checks and surfacing trusted fields Is this self-service for business users, or IT-locked? The dashboards are designed for business teams : Drill from executive KPIs down to shipment-level detail Interact with associative filters Export data Build new visualizations (depending on permissions) Finance, procurement, and supply chain users can get answers without waiting on IT . Is optimization rules-based or machine-learning driven? It can be either or both : Out-of-the-box:  A rules-based framework using historical performance, transportation costs, and lane efficiency Advanced:  ML models predicting lane reliability, delays, or optimal routing, leveraging Snowflake, Databricks, or Qlik AutoML Customers can start simple and evolve toward more advanced ML-driven optimization over time.

In today’s business landscape, organizations are expected to do more than deliver services. They must also meet stringent data security and regulatory requirements. At Pingahla, we see quality and compliance as inseparable, and balancing them is central to building trust with our clients. How We Deliver Quality Quality is never a one-time goal for us. It’s a continuous practice, built on: Robust processes  that bring consistency and precision to every project Continuous improvement  through lessons learned and new innovations Client feedback loops  that ensure we always stay aligned with business needs Team accountability  so every deliverable reflects the highest standards This approach ensures projects are not just delivered, but delivered right. Compliance Built Into Everyday Work Compliance at Pingahla is not a checklist; it’s a culture. Our framework covers: Data classification and retention policies  that safeguard sensitive information ISMS practices  for a structured approach to information security Regular audits and assessments  to keep standards in check Employee training and awareness  so that compliance is second nature to every team member Together, these measures reduce risks and assure clients that their data is always secure. Real-World Impact Across Industries Our integrated approach has made a measurable difference for clients across sectors: Financial Services : While migrating sensitive customer data, we balanced speed and accuracy with strict regulatory standards, ensuring the client could move forward without compliance concerns. Healthcare : We implemented strong data governance practices during a cloud migration for a healthcare provider, protecting patient information while enabling faster analytics. Retail and E-commerce : When helping retailers consolidate large volumes of sales data, we built quality checks into the process while ensuring compliance with GDPR and other data privacy regulations. These examples highlight how we adapt to client needs while keeping quality and compliance at the forefront. Why This Matters What sets Pingahla apart is our ability to bring these priorities together. We don’t see compliance as a burden or quality as a finish line. We treat them as ongoing commitments that define how we work and interact with clients. This proactive mindset allows us to: Spot risks early and resolve them before they escalate Deliver solutions that withstand both market expectations and regulatory scrutiny Build long-term partnerships based on trust, not just transactions A Trusted Partner for the Future By embedding quality and compliance into our DNA, Pingahla positions itself not just as a service provider but as a trusted partner. As regulations grow stricter and client expectations rise, we remain committed to strengthening our practices so that trust, security, and excellence continue to define every client relationship.

In today’s rapidly evolving data-driven world, managing data effectively is no longer optional, it's essential. As organizations continue to generate massive volumes of data across systems and channels, the need for reliable, accurate, and accessible information becomes critical. This is where Master Data Management (MDM) steps in as a foundational element of any successful data strategy.   The Power of Data in a Modern Enterprise Data plays a vital role in empowering organizations to make informed decisions, solve complex problems, and uncover valuable insights. From understanding customer behavior to optimizing operations and enhancing user experiences, data fuels innovation and growth across all business functions. A robust data foundation enables cross-functional teams to collaborate efficiently, eliminate silos, and foster a data-driven culture. In today’s customer-centric economy where growth is directly tied to the quality of customer experience having real-time, reliable customer data is a competitive necessity. Missed opportunities, fragmented interactions, and inaccurate profiles can significantly hinder business success. What is Master Data Management (MDM) ? Master Data Management is the process of consistently and accurately identifying and managing core business entities such as customers, suppliers, products, employees, and locations across various systems and touchpoints. It involves consolidating entity data from multiple sources to create a single, unified, and trusted view of the business-critical information that drives decisions. Master data is the essential, mission-critical information that organizations depend on. It provides the foundation for business transactions, customer interactions, and analytics. Examples include: People  :  Customers, suppliers, employees Places  : Locations, branches, warehouses Things  : Products, assets, inventory Without effective MDM, businesses often face issues like duplicate records, data inconsistencies, inaccurate reporting, and inefficient processes. The Challenge with Unmastered Data Take an instance of a life sciences industry, where data is collected across a multitude of channels - patient portals, healthcare providers (HCP) interactions, clinical trials, digital health apps, electronic medical records (EMRs) and more. Each of these touchpoints may capture information related to patients, physicians, trial participants or healthcare institutions  in different systems and formats. Now, imagine a single healthcare professional engaging with a life sciences company across various touchpoints like attending a virtual event, prescribing a therapy, participating in a clinical trial etc, without master data management in place each interaction might be captured in a different system often resulting in duplicate or inconsistent records for the same HCP. This data fragmentation can cause several issues like  : Multiple conflicting profiles for the same HCP. Uncoordinated outreach from different departments. Missed opportunities for personalized engagement. Risk of non compliance with regulations like GDPR. With MDM, all of these disparate data points are : Matched and Merged using business rules. De-duplicated to ensure a single, golden HHCP profile. Enriched with the validated data from external data providers. Governed through stewardship and compliance controls. Thus, the result is a 360 degree unified view of HCP that is uniformly accessible across departments. By ensuring a single source of truth for master data, MDM enhances every strategic initiative from marketing personalization and customer engagement to operational efficiency and digital transformation. Elevating data with the help of MDM. In my role as the lead for Master Data Management (MDM) implementation at a leading pharmaceutical and life sciences company, I witnessed firsthand how an effective MDM system can drive significant business value. The organization experienced tangible improvements in operational efficiency and stakeholder engagement through the implementation of a robust MDM solution. The business initially faced substantial challenges due to duplicate healthcare professional (HCP) profiles originating from multiple touchpoints and residing in a centralized data lake. These inconsistencies led to increased costs in marketing campaigns, inaccurate field representative targeting, and incorrect payouts to HCPs participating in seminars, conferences, and guest lectures. Throughout the engagement, I collaborated closely with business stakeholders, product owners, and end users to design and implement a comprehensive MDM solution that addressed these pain points. The result was a transformative system that improved data quality and usability for analytical and operational purposes. The highlights of the implemented MDM system includes A 360-degree view of HCP profiles by integrating data from multiple sources using advanced match and merge logic with source-specific survivorship rules. A fully customized user interface that enabled business users to view and update data efficiently. Multi-level approval workflows for data change requests, ensuring strong data governance aligned with user roles. Role-based security to control data access and maintain compliance. A tailored workflow to accurately determine HCP expert levels, ensuring correct payouts. Improved data quality through integration with third-party providers for address, phone, and email validation. The outcome has been substantially improved as the business saw  Reduced marketing campaign costs by eliminating duplicate HCP records. Accurate and fair HCP payouts for business events based on verified expert levels. Enhanced operational efficiency driven by a unified and reliable 360-degree HCP profile. Increased response rates from mail campaigns due to improved data accuracy. Master Data Management isn’t just about cleaning data, it’s about transforming it into a strategic asset that supports the entire business. MDM empowers organizations to build accurate, real time and holistic views of their key entities.  How does Pingahla empower growth through Master Data Management? At Pingahla, we specialize in building robust and scalable Master Data Management (MDM) solutions that help organizations turn fragmented data into a trusted, unified asset. In today’s complex digital landscape, where data is generated across countless systems and channels, businesses face an uphill battle to maintain data consistency, accuracy, and accessibility. Our MDM solutions are designed to  Eliminate duplicate and inconsistent data across departments and platforms. Unify data related to Customer, Products, Suppliers and more into a single version of truth. Enhance personalized experiences by delivering a 360 degree view of business entities. Enhance decision making with high quality, real time data. Support regulatory compliance and data governance best practices. Whether you're in life sciences, retail, manufacturing, or financial services, Pingahla’s MDM frameworks are tailored to meet industry-specific requirements and integrate seamlessly into your existing ecosystem.