Good morning. Good afternoon, everyone. My name is Ra. I'm excited to be with you today. I'm currently working for Amazon as a data engineer. I am. My main focus is on building next generation of data platform. Today I want to talk about a topic that quickly becoming crucial in our industry, how generative AI is fundamentally cha changing the way we approach data integration. This isn't about just new tool or incremental change, it's our complete paradigm shift. The title of my talk is Scalable, code Free, ETL, how Generative AI is Redefining Data Integration. And it's a topic that's very relevant to anyone involved in data platforms, engineering, or analytics. I'm looking forward to walking you through how this technology works, showing you some real world results, and discussing what it means for the future of platform engineering. So before we dive into the solution, let's acknowledge the problems we are all too familiar with. The image of tire looking developer says It solved. Organizations today face significant hurdles with traditional E detailed processes. The first major problem is the manual coding burden, creating a new data pipeline from scratch request deep. Technical expertise and specialized engineering skills for each one, which creates a body link in the workflow. The number of new pipelines a business can create is limited by the number of engineers available. This is a major friction point. This leads directly to the second problem. A high technical learning curve. The people who need the data most, the data analyst and business users often can't get it themselves because they lack the technical skills. They must rely on a small, specialized team of engineers, which delays insights and slowly it slows down the entire business. This dependency creates a communication gap and significant time lag. And finally, there is the operational overhead. A data pipeline isn't a one on one on done project. It requires continuous maintenance, troubleshooting, and optimization, which drains valuable resources and time from your engineering team. This constant upkeep prevents teams from focusing on innovation, so we are stuck in a cycle of building and maintaining which limits our ability to respond to new business needs quickly. Generative AI is the key to solving these challenges. It's a fundamental shift moving us from a code centric world to a conversational one. Imagine telling the system in plain English exactly what you need. That's the first step. Translating natural language to your pipeline, you can express your data needs without any SQL or transformation code required. This empowers a much broader group of users to interact with data. The magic happens with intent recognition. The AI doesn't just look for keywords. It understands the business intent behind your request. For example, if you say, show me sales strength, the AI understands you need a time series analysis with specific aggregations, not just a simple query. It translates the business goal into correct technical specification. The system handles the entire workflow automatically, which is what we call automatic execution. It doesn't just generate the code, it handles the deployment and continuous monitoring of the pipeline without any manual intervention from an engineer. This is a huge leap in efficiency and it gets smarter with every use. This is the concept of continuous learning over time. The system builds a knowledge base unique to your organization, learning your specific data assets, and common integration pattern. This makes it more efficient and accurate over time, and it's a self-improving system. This entire paradigm shift is about democratizing data. It puts data integration capability directly in the hands of who need insights. So let's take a closer look at what's happening behind the scenes. This is the architectural breakdown of AI driven ETL system. It all starts with the natural language interface. This is the user phrasing. Front end users can simply type or speak their data request just as they would. Do a search engine or a chat bot. This is where the initial intent is captured, and the system can even prove contextual assistance to help the user formulate their request more clearly, the request is then passed to the semantic password. This is the core intelligence layer. It breaks down the request interpreting and mapping it to this. Specific data entities, their relationships, and the transformation we have asked for. For example, if you ask for sales by product, the passer knows to map sales to specific sales table and product to a product table. It understands the relationship between these entities and what you want to do with them. The output from the passer goes to the execution planner. This component is a strategist. It takes the past information and figures out the most efficient way to execute the pipeline. It considers factors like the size of the data, available, resources, and your company's governance policies to create an optimized plan. This step is critical for ensuring performance and scalability. Finally, the runtime engine puts the plan into action. It takes the optimized plan and translates it into actual executable code for a variety of processing frameworks. Whether that says part job, a series of SQL queries, or something else, the runtime engine ensures the pipeline runs correctly and efficiently translating the plan into the correct syntax for the underlying technology. Let's make this concept even more concrete by walking through a practical example. A business user, perhaps from the marketing team, needs a specific report. Instead of submitting a ticket to the data team and waiting for weeks, they simply type the request into the system. The request is clear plain English sentence. The AI understands the full context of the request. It recognizes that total sales needs a sum function that. Product category is a group by class, and the top 10 customers require a rank or limit class. It also understands the time dimension for the comparison logic. This is far more sophisticated than a simple search function. This deep understanding allows it to generate complete, optimized logic. This isn't just a simple query, it's a full fledged operational pipeline, which scheduling transformation rules and output formatting built in this. This system creates all the necessary code and logic to make this request a reality. The final product is a production ready workflow that is automatically deployed and ready to run. This pipeline is ready to execute, and it's even set up with monitoring and notification, so the user is informed of its status. The best part, the entire process takes minutes instead of days, and the users never have to write a single line of code. To move from a cool demo to a robust enterprise ready platform, a few critical, comprehensive needed. It's a combination of a solid foundation, powerful processing, and strong operational capabilities. In the foundation layer, the system must have a comprehensive understanding of your data landscape. This requires metadata discovery, which automatically scans and catalogs all data sources. So the AI knows what's available and how it's structured. We also need a strong credential management system to securely handle authentication and maintain zero trust principles, the processing layers. Ensure the system is efficient and powerful. Intelligent catching prevents the same work from being done over and over by catching results and minimizing redundant processing. This is especially important for frequently run reports. It also supports real-time processing with stream based pipelines from time sensitive applications like fraud detection or live dashboards on the operational layer. Data lineage tracking is a key component for governance and troubleshooting. The system automatically creates a trail of how data moves, which is vital for audits, debugging, and ensuring data quality and API orchestration is what allows the system to connect and coordinate with all the various systems and services in a modern enterprise, ensuring seamless data movement across disparate systems. These components work together to create a platform that balances flexibility with governance. Let's examine a real world case study from a financial services company. This is a great example. Because of the complexity of the data environment and strict regulatory requirements, they face significant challenges. Their data landscape was immense and fragment. Fragmented with over 300 data sources across legacy systems and modern cloud platform. The industry's regulatory reporting requirements meant that any new data pipeline needed to handle high complex transformations. The result was that the average time to implement a new data pipeline was a staggering three weeks, and the limited data engineering resources were constantly a bottleneck, unable to keep up with the demand. After implementing an AI driven EL system, the results were transformative. The time to create a new pipeline was reduced to a matter of hours, not weeks. This meant the business could get new reports and insights much faster. They found that 85% of their common integration tasks could be completed without a single line of coding. The impact on the team was huge. Data analysts who were previously reliant on engineers were able to create their own pipelines with natural language. This led to a 40% reduction in the data engineering backlog as the team was freed up to focus on more complex strategic projects. And for a regulatory industry like financial services, the automatic lineage documentation was a critical benefit, ensuring improved compliance and making orders much easier. Here is another powerful case study from the e-commerce sector, which highlights this value of speed and agility. The company's goal was to reduce the time it took to analyze customer behavior with. Traditional methods. It took five days to build a pipeline, which was a huge delay in a fast moving market with the AI driven system, that time dropped to a staggering 30 minutes, allowing the marketing team to react to market changes almost in real time. This is a massive competitive advantage. This led to a huge leap in user empowerment. The marketing team, a group of business users. Created over 75 pipelines on their own, completely reminding the dependency on the data engineering team. This is a perfect example of what it means to democratize data. The system proved to be incredibly scalable. It handles over 500 daily tasks and processes, a massive amount of data, 12 terabytes across 30 different system. This shows that technology can handle enterprise level, scale data and complexity. I, many leaders from the company have also significantly saw the improvement. They went from waiting weeks for data to be being self-sufficient. They were able to respond to market changes in hours instead of weeks because they had the power to create and modify pipeline themselves. These aren't just isolated success stories. The data shows a clear pattern of improvement. The first bar on the graph shows a dramatic reduction in pipeline creation time. The average time was reduced by 96% from days, two hours, which is a massive leap in agility. The second bar shows a similar trend for engineering hours. There was an 89, 80 7% reduction in the number of hours required. Per pipeline. This frees up your most skilled resources to work on more complex strategic projects, rather than spending their time on manual repetitive tasks. We also see a significant decrease in pipeline errors, a 33% reduction. This is a direct result of the system's ability. To generate optimized and consistent code with built-in validation, which is far less error than manual coding. The most exciting data point for me is that 85% of business users could create simple pipelines with no training. This is a powerful testament to the systems user friendliness and accessibility. The data shows that the promise of democratizing data is not just a theory, it's a measurable reality. These benchmarks are based on aggregated data from 12 enterprise implementations across a variety of sectors. So we know this is a consistent finding. Let's look beyond the numbers at the broader organizational impact. This technology directly impacts your a accelerated time to insight, the time it takes. To create a new data workflow is reduced from weeks to minutes, which enables faster, more agile business decisions. This is crucial for staying competitive in today's market. It also leads to cross-functional empowerment. When business users can create their own data pipelines, they are no longer bottlenecked by a single department. This frees up your data engineers to focus on more complex, high value tasks like building the underlying platform itself. From a financial perspective, you can expect significant cost reduction. The technology offers elastic resource utilization and automatic optimization, which leads to a 30 to 50% lower total cost of ownership compared to traditional ETL solution. And finally, you get a significant reduction in technical rate. The automatically generated pipelines are consistent. Documented and have governance built in, which makes them much easier to manage and maintain in the long run. The system also adapts to a new data source and transformation needs without requiring you to record everything. So how do you get started on this journey? It's important to approach this strategically with a clear roadmap. The first phase is discovery and assessment. You need to take full inventory of your existing data sources and integration points. Don't try to tackle everything at once. Instead, identify a few high value, low complexity use cases that are perfect for an initial pilot. You also need to establish clear success metrics and baseline measurements to show the value of the new approach. Next, a pilot deployment is critical. Implement the AI ETL system for those two to three selected use cases. Train a small initial group of both technical and business users and make sure you validate the results against your traditional methods to prove its effectiveness. This phase is all about building confidence and buying. Once the pilot proves its value, you can scale and optimize. You will expand the system to more data domains and use cases. This is also where you integrate with your existing governance frameworks and establish a center of excellence for knowledge sharing to ensure widespread adoption. The final stage is full enterprise integration, where the ETL AI ETL system becomes your standard approach for data integration. You can then progressively migrate your legacy pipelines to the new system using usage analytics for continuous improvement. It is important to be realistic about the challenges and consideration. This is a new technology, and there are a few hurdles to keep in mind on the technical side. While the system handles most tasks, a few highly specialized or complex transformation must still require a coding extension. You can't expect AI to handle every edge case. Perfectly from day one and for extremely large data volumes, some performance tuning of the AI generated pipeline might be needed to achieve optimal speed. Additionally, legacy system integration can be a challenge. Older systems without modern a PS may require you to build additional con connectors to get them to work with the platform. On the organizational side, your data governance processes will need to evolve. To support this new self-service model, you will need to think about how to manage a high volume of high user generator pipelines. The roles of your data needs will also significantly change. They'll shift from writing code to focusing on architecture, governance, and oversight becoming true platform specialist. Finally, while the system is core free, you'll still need to provide training and adoption guidance for users on how to effectively communicate in their data requirements to the AI systems. To summarize, generative AI is not just an incremental improvement to ETL. It's a fundamental transformation. The ability to use natural language to create data pipelines is a game changer. The business impact is real and measurable. We have seen significant cost savings and huge reductions in development time with some organizations seeing over 90% reductions in development time and significant cost savings. This technology democratizes data access, which is one of the most powerful outcomes. It empowers business users to be self-sufficient and get the insight they need without waiting on a technical team. My final advice is to is to start small and scale strategically. Don't try to boil ocean. Begin with a few well-defined use cases. Prove the value and then expand your implementation as your team's confidence and capabilities grow. This is the future of data integration. It's about making data accessible to everyone in your organization, and I hope this presentation has given you a solid understanding of how that can be achieved. Thank you.