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// if (window.globalVar == true) { (async function () { // Specifies style options to customize the Web Chat canvas. // Please visit https://microsoft.github.io/BotFramework-WebChat for customization samples. const styleOptions = { // Hide upload button. hideUploadButton: true }; // Specifies the token endpoint URL. // To get this value, visit Copilot Studio > Settings > Channels > Mobile app page. // // if (globalVar == true) { const tokenEndpointURL = new URL('https://034b67b3612be1a1a3fd8fb7c85c5b.d2.environment.api.powerplatform.com/powervirtualagents/botsbyschema/cr12b_aptio/directline/token?api-version=2022-03-01-preview'); // const tokenEndpointURL = new URL('https://034b67b3612be1a1a3fd8fb7c85c5b.d2.environment.api.powerplatform.com/powervirtualagents/botsbyschema/cr12b_dotcomChatbot/directline/token?api-version=2022-03-01-preview'); // } else { // const tokenEndpointURL = ''; // } // Specifies the language the agent and Web Chat should display in: // - (Recommended) To match the page language, set it to document.documentElement.lang // - To use current user language, set it to navigator.language with a fallback language // - To use another language, set it to supported Unicode locale // Setting page language is highly recommended. // When page language is set, browsers will use native font for the respective language. const locale = document.documentElement.lang || 'en'; // Uses language specified in element and fallback to English (United States). // const locale = navigator.language || 'ja-JP'; // Uses user preferred language and fallback to Japanese. // const locale = 'zh-HAnt'; // Always use Chinese (Traditional). const apiVersion = tokenEndpointURL.searchParams.get('api-version'); // const apiVersion = window.globalVar == true ? tokenEndpointURL.searchParams.get('api-version') : ''; const [directLineURL, token] = await Promise.all([ fetch(new URL(`/powervirtualagents/regionalchannelsettings?api-version=${apiVersion}`, tokenEndpointURL)) .then(response => { if (!response.ok) { throw new Error('Failed to retrieve regional channel settings.'); } return response.json(); }) .then(({ channelUrlsById: { directline } }) => directline), fetch(tokenEndpointURL) .then(response => { if (!response.ok) { throw new Error('Failed to retrieve Direct Line token.'); } return response.json(); }) .then(({ token }) => token) ]); // The "token" variable is the credentials for accessing the current conversation. // To maintain conversation across page navigation, save and reuse the token. // The token could have access to sensitive information about the user. // It must be treated like user password. const directLine = WebChat.createDirectLine({ domain: new URL('v3/directline', directLineURL), token }); // Sends "startConversation" event when the connection is established. const subscription = directLine.connectionStatus$.subscribe({ next(value) { if (value === 2) { directLine .postActivity({ localTimezone: Intl.DateTimeFormat().resolvedOptions().timeZone, locale, name: 'startConversation', type: 'event' }) .subscribe(); // Only send the event once, unsubscribe after the event is sent. subscription.unsubscribe(); } } }); WebChat.renderWebChat({ directLine, locale, styleOptions }, document.getElementById('webchat')); })(); // }

SUCCESS STORIES / Cloud

Fast-tracking data migration to Google Cloud with GenAI-led DataStreak.AI 

Unleashing the power of cloud Innovations and automation with DataStreak.AI to drive strategic decision-making, 50% faster migrations, and 30% cost savings.

Background

Industries worldwide are undergoing significant digital transformation, with the financial services sector, which offers online payment services, at the forefront. As reliance on data for operational agility and customer-centric innovation grows, global investment in big data and business analytics is projected to reach $215 billion by 2025, primarily driven by cloud platform adoption. This shift underscores a critical trend toward scalable, cloud-based infrastructures supporting extensive data volumes and complex processing needs.

For fintech enterprises, transitioning from traditional on-premises infrastructure to cloud platforms is not merely technical but a strategic overhaul involving re-engineering core data migration processes to leverage cloud efficiency and scalability. In an industry that moves at breakneck speed, the ability to execute this transition swiftly and seamlessly becomes a strategic imperative—not just for maintaining service continuity but also for securing a competitive edge in a rapidly evolving market.

The pressing question for industry leaders is how to manage this transition without disrupting existing operations, ensuring they can immediately capitalize on the benefits of cloud-based analytics and AI solutions.

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50% Reduced Migration Time

30% Lower Costs

65% Increased Performance

Improved Efficiency

Client Situation

Our client is a global fintech company that operates one of the world's largest online payment systems, supporting over 400 million consumer and merchant accounts across more than 200 markets. Despite their strong market position, they faced operational challenges such as efficiently integrating and managing large datasets and maintaining low latency during high transaction volumes.

These issues have become critical as the company prepares for the planned shutdown of its Hadoop-based data centers by November 2025, necessitating the migration of 6,500 data processing jobs to the cloud. Previous internal efforts to upgrade their systems often fell short, posing risks to their seamless service delivery and market leadership. The company has planned to leverage cloud solutions to improve computational power and storage efficiency, especially during peak times, focusing on sustainable growth.

This strategic shift to Google Cloud, facilitated by Prodapt’s expertise in large-scale cloud data migrations and our Gen-AI-driven solution, Datastreak.AI, aims to modernize their data ecosystem, streamline processes, and deliver faster, more reliable insights. The partnership intends to enhance operational efficiency, reduce costs, accelerate service delivery, and maintain a competitive edge in fintech. Prodapt is crucial in navigating this transformation.

Prodapt's expertise in complex data migrations and innovative approach with DataStreak.AI enabled us to seamlessly transition to a modern cloud environment within our ambitious timelines. Their solution optimized our operations, significantly reduced costs, and enhanced our data-driven decision-making capabilities, said the large FinTech Company's chief technology officer

Diagnosis

Prodapt's comprehensive analysis of the client's data landscape uncovered a series of interlinked challenges that critically impeded their modernization efforts. One formidable issue identified was the complexity of manually migrating extensive data pipelines. These pipelines span hundreds of applications, each utilizing a mix of data processing technologies such as SQL, Spark, PySpark, and Scala. The manual nature of this migration process made it labor-intensive and prone to errors and significantly slowed down the transition, undermining the overall efficiency and accuracy of the migration.

Another key challenge was the technical limitations of the client’s legacy systems, initially designed for on-premises environments. They lacked the agility and scalability required for modern, cloud-native frameworks like Google Cloud, and they required significant updates and integration to function seamlessly in the cloud.

The other challenges were migrating the fragmented data architecture and the internal team's limited experience with cloud-native technologies. This fragmentation added complexity to data management and migration, necessitating technical upgrades and a strategic overhaul of the data handling processes. These interconnected challenges highlighted the need for an automated, uniform approach to streamline migration.

Solving It

Prodapt's DataStreak.AI leverages generative AI to optimize network operations, targeting the modernization of legacy systems, enhanced data handling, and streamlined processes to reduce latency and improve service delivery. This comprehensive automation framework integrates advanced tools for data validation, pipeline transformation, and governance optimization, explicitly addressing the challenges associated with the migration processes' manual nature and legacy systems' technical limitations. By automating key workflows, including schema-level, column-level, and row-level data validation, DataStreak.AI reduces the likelihood of errors as a direct response to the manual complexities previously identified.

The deployment of Data Migration Pro (DMPro), a GUI-based engine for streamlined data validation, and Code Maverick, a generative AI-powered co-pilot for business logic transformation, directly tackles the inefficiencies in the client's legacy data management practices. These tools are critical in transforming these practices into efficient, cloud-native operations, aligning with the need to modernize data pipelines to function seamlessly on Google Cloud.

A meticulously planned cutover phase, including parallel run monitoring and a four-week hyper-care period, was implemented to support the migration process further. This phase was crucial for promptly resolving any post-migration issues, enhancing operational efficiency, and ensuring the technical robustness of cloud-native systems throughout the transition.

The automation using Datastreak.AI enabled a 50% reduction in the migration timeline, facilitating a swift transition to Google Cloud, which was essential before the shutdown of Hadoop data centers. This rapid migration led to a 30% cost reduction by eliminating outdated infrastructure maintenance and manual workflows. Moreover, the enhanced data pipeline and analytics processes resulted in a 65% boost in performance, allowing the client to generate quicker, more reliable insights and maintain competitive market leadership.

Additionally, the reduction in manual interventions minimized errors and freed up resources, enabling the client to focus on strategic initiatives and modernize their data infrastructure for sustained growth and competitiveness.

March 3, 2025

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