Hi, my name is Kar Kda. I have overall 18 years of IT experience specializing in, data warehousing and, data engineering background. Today I'm very excited to talk about AI powered ETL, in the Golan 2025 conference without much delay. Let's dig deep into that. AI powered ETL transforming data with smarter pipelines. The traditional extract transform. And Load Paradigm is undergoing a revolutionary transformation through artificial intelligence integration. AI technologies are fundamentally re-imagining each phase of ETL Lifecycle, creating adaptive and intelligent data pipelines capable of autonomous operations. Modern AI enhanced ETL systems transcend conventional rule-based approaches by implementing self-healing mechanisms that anticipate and resolve failures adaptive to transformation engines that learn from historical patterns and intelligent loading strategies that optimize data placement based on usage patterns and business requirements. The data explosion challenge India leads AI adoption. Around 59% of Indian enterprises actively deploy AI in their business operations considerably higher than 42% of the global average exponential data growth. India's data creation will grow at A-C-H-E-R of 29.7, percentage from 22 from estimated three pointes by. Operational inefficiencies. Indian enterprises spend close to 43.8 percentage of total data analysis time solely on data preparation activities with 72.3 percentage of data engineers dedicating more than half of their working hours to troubleshooting traditional ET l systems originally designed when gigabyte scale data warehouses were considered. Substantial fundamentally cannot scale to accommodate this explosive growth without significant AI enhancement and re-imagination, some quantifiable benefits. A 76.4 percentage of processing efficiency, 83.7, percentage of error reduction, 2.34 millions of annual savings for each enterprise through minimized error handling and optimized operations. And 87.3 percentage of prediction accuracy, a comprehensive of 2024 study published in the prestigious journal Decision support Systems meticulously analyzed 2 87 enterprise data integration implementations across diverse industry sectors. This landmark research provides compelling evidence of the transformative economic and operational advantages delivered by AI powered ETL solutions in enterprise environments. The technical architecture of AI enhanced ETL, there are four main phases. Data ion layer is the very first phase. It processes 17 different data formats simultaneously, including complex semi-structured formats like nested JSON and industry specific EDI variance, phase number two, machine learning subsystem. Transformer based models achieve 90, 94 0.7 accuracy in predicting optimal transformational path for previously unseen data structures. step number three, metadata repository organizations maintaining comprehensive metadata experienced 217 percentage higher overall pipeline reliability to those limited metadata. Four. Self-healing mechanisms reduced average downtime of 6.4 hours per ETL in traditional systems to just 23.7 minutes in AI enhanced pipelines. Some of the implementation challenges and solutions, the first one being the technical debt. The average large organization maintains 7,842 unique transformational scripts with 43.2 percentage containing hardcoded business logic that has not been reviewed in over 18 months, creating significant migration complexity. Number two, we have skill gaps. 67.8 percentage of organizations site insufficient a ML expertise as a significant barrier to adoption, despite India's reputation as a global technology talent hub. Number three, data governance. 91.7 percentage of organizations report increased regulatory scrutiny or automated data processes with regulatory complaints, costs increasing by an average of 27.3 percentage in the first year following AI implementation. Successful implementations typically follow methodical approaches informed by. Empirical research focusing on metadata enrichment, targeted use cases and hybrid approaches that maintain critical manual processes while gradually expanding AI capabilities, smarter extraction with ai, intelligent source detection organizations, leveraging machine learning for source discovery catalog, and integrated new data sources 7.3 times faster than those using traditional methods. With 81 percentage of surveyed enterprises reporting capability to onboard new structured data sources in less than three business days, adaptive scheduling, AI driven workload balancing, reduced soul system performance impact by 46.3 percentage, while simultaneously increasing extraction throughput by 32.8 percentage with dynamic workload adaption resulting in a 51.7 percentage reduction in computing costs. Format recognition. Modern deep learning approaches can identify and pass previously unseen data formats with 93.7 accuracy after being trade on just 2225. A representative examples, transformation with machine learning. We have three categories here. The first one being pattern recognition. Machine learning identifies and automates 82.3 percentage of transformations that follow recurring patterns through unsupervised learning techniques. Anomaly detection, AI powered anomaly detection has reduced data quality issues in production environments by 76.8 percentage dramatically improving downstream reliability. Number three, predictive cleansing. Advanced in simple models. Combining multiple ML approaches automatically resolved 86.7% of data quality issues that previously required manual. Traditional E transformation phases rely on rigid rule-based logic requiring constant maintenance as business needs evolve. Machine learning fundamentally reimagines this approach, creating intelligent adaptive transformations that not only respond to changing data characteristics, but also anticipate and address potential issues before they impact business operations. Intelligent loading. we have three phases here. The first one being optimal target selection. It gives 5.3 times better query performance. The second one being dynamic partitioning. it gives 67.8 improvement, in query performance. And the third one being realtime optimization, where, it gives around 81.4 percentage reduction in loading related insurance. The loading phase has evolved from basic data movement into a sophisticated decision making ecosystem that strategically determines how, when and where data is persisted. Modern AI algorithms have revolutionized these traditionally straightforward processes, enabling systems to make complex context aware decisions that dramatically enhance downstream analytical capabilities. Organizations implementing AI and he loading capabilities have experienced remarkable results around 62.4 percentage boost in query performance against loaded data at a 47.8 percentage reduction in storage costs through intelligent data placement and organization strategies. These translate directly to faster insights and significant operational savings. AI powered self-healing pipelines. it has three main, steps. The first one being predictive monitoring, where the neural network models strain on telemetry data correctly predicted 91.3 percentage of data integration failures before they impacted downstream systems. Number two, being the automatic correct two actions. 76.4 percentage of integration failures followed recognizable patterns that could be addressed through predefined remediation strategies. Number three, being continuous learning. Self-healing systems showed a 42.3 percentage higher autonomous resolution rate using reinforcement learning compared to simpler machine learning approaches, perhaps the most revolutionary. Aspect of AI and he ETL is the development of self-healing pipelines. Traditional ETL workflows often fail when encountering unexpected data formats or system issues requiring manual intervention, organizations implementing AI powered pipeline resilience capabilities experienced, 79.3 percentage reduction in insurance tickets requiring human resolution, implementation considerations. Skills development, metadata, foundation, high value use cases, feedback mechanisms, balanced automation organizations looking to implement AI Enhanced ETL should consider several critical factors to maximize success, probability, and business values, starting with high value use cases. Builds momentum through demonstrable business impact building. A robust metadata foundation provides the essential context for AI systems to make intelligent decisions. Implementing feedback mechanisms enables continuous improvement. Without explicit reprogramming balancing automation with human oversight, ensure appropriate governance and. Investing in skills development creates the organizational capability required to leverage those powerful technologies effectively. future discussions in AI powered ETL, reinforcement Learning for Optimization by 2027, approximately 67 percentage of enterprise integration environments will incorporate. Reinforcement learning capabilities for continuous optimization, representing a 8.3 fold increase from current adoption rates, natural language interfaces. By 2028, approximately 62 percentage of enterprises will offer natural language capabilities for basic integration tasks, expanding the population of integration creators by 7.3 times autonomous data ecosystems. By 2030, approximately 47 percentage of enterprises will implement substantial ecosystem autonomy for non-critical data domains, potentially reducing total cost of ownership by over 70 percentage. The integration of AI into EDL processes continues to evolve rapidly with several emerging trends. Transform how organizations manage their data integration workflows. These develop a future where data integration becomes substantially more efficient, more reliable, and more accessible to a broader population of business users. that's all for me and thank you so much for giving me an opportunity to talk about AI powered. ETL, smarter Pipelines in GoLine 25.