Last year witnessed the explosive rise of large models, generating global enthusiasm and making AI seem like a solution to all problems. This year, as the hype subsides, large models have entered a deeper phase, aiming to reshape the foundational logic of various industries. In big data processing, the collision between large models and traditional ETL (Extract, Transform, Load) processes has sparked new debates. Large models feature “Transformers,” while ETL relies on “Transform” processes—similar names representing vastly different paradigms. Some voices boldly predict: "ETL will be completely replaced in the future, as large models can handle all data!" Does this signal the end of the decades-old ETL framework underpinning data processing? Or is it merely a misunderstood prediction? Behind this conflict lies a deeper contemplation of technology's future. Will Big Data Processing (ETL) Disappear? With the rapid development of large models, some have begun to speculate whether traditional big data processing methods, including ETL, are still necessary. Large models, capable of autonomously learning rules and discovering patterns from vast datasets, are undeniably impressive. However, my answer is clear: ETL will not disappear. Large models still fail to address several core data challenges: 1. Efficiency Issues Despite their outstanding performance in specific tasks, large models incur enormous computational costs. Training a large-scale Transformer model may take weeks and consume vast amounts of energy and financial resources. By contrast, ETL, which relies on predefined rules and logic, is efficient, resource-light, and excels at processing structured data. For everyday enterprise data tasks, many operations remain rule-driven, such as: Data Cleaning: Removing anomalies using clear rules or regular expressions. Format Conversion: Standardizing formats to facilitate data transmission and integration across systems. Aggregation and Statistics: Categorizing, aggregating, and calculating data daily, weekly, or monthly. These tasks can be swiftly handled by ETL tools without requiring the complex inference capabilities of large models. 2. Ambiguity in Natural Language Large models have excelled in natural language processing (NLP) but have also exposed inherent challenges—ambiguity and vagueness in human language. For example: A single input query may yield varied interpretations depending on the context, with no guaranteed accuracy. Differences in data quality may lead models to generate results misaligned with real-world requirements. By contrast, ETL is deterministic, processing data based on pre-defined rules to produce predictable, standardized outputs. In high-demand sectors like finance and healthcare, ETL's reliability and precision remain critical advantages. 3. Strong Adaptability to Structured Data Large models are adept at extracting insights from unstructured data (e.g., text, images, videos), but they often struggle with structured data tasks. For instance: Traditional ETL efficiently processes relational databases, handling complex operations like JOINs and GROUP BYs. Large models require data to be converted into specific formats before processing, introducing redundancy and delays. In scenarios dominated by structured data (e.g., tables, JSON), ETL remains the optimal choice. 4. Explainability and Compliance Large models are often referred to as “black boxes.” Even when data processing is complete, their internal workings and decision-making mechanisms remain opaque: Unexplainable Results: In regulated industries like finance and healthcare, predictions from large models may be unusable due to their lack of transparency. Compliance Challenges: Many industries require full auditing of data flows and processing logic. Large models, with their complex data pipelines and decision mechanisms, pose significant auditing challenges. ETL, in contrast, provides highly transparent processes, with every data handling step documented and auditable, ensuring compliance with corporate and industry standards. 5. Data Quality and Input Standardization Large models are highly sensitive to data quality. Noise, anomalies, or non-standardized inputs can severely affect their performance: Data Noise: Large models cannot automatically identify erroneous data, potentially using it as "learning material" and producing biased predictions. Lack of Standardization: Feeding raw, uncleaned data into large models can result in inconsistencies and missing values, requiring preprocessing tools like ETL. ETL ensures data is cleaned, deduplicated, and standardized before being fed into large models, maintaining high data quality. Despite the excellence of large models in many areas, their complexity, reliance on high-quality data, hardware demands, and practical limitations ensure they cannot entirely replace ETL. As a deterministic, efficient, and transparent tool, ETL will continue to coexist with large models, providing dual safeguards for data processing. CPU vs. GPU: A Parallel to ETL vs. Large Models While ETL cannot be replaced, the rise of large models in data processing is an inevitable trend. For decades, computing systems were CPU-centric, with other components considered peripherals. GPUs were primarily used for gaming, but today, data processing relies on the synergy of CPUs and GPUs (or NPUs). This paradigm shift reflects broader changes, mirrored in the stock trends of Intel and NVIDIA. From Single-Center to Multi-Center Computing Historically, data processing architectures evolved from "CPU-centric" to "CPU+GPU (and even NPU) collaboration." This transition, driven by changes in computing performance requirements, has deeply influenced the choice of data processing tools. During the CPU-centric era, early ETL processes heavily relied on CPU logic for operations like data cleaning, formatting, and aggregation. These tasks were well-suited to CPUs’ sequential processing capabilities. However, the rise of complex data formats (audio, video, text) and exponential storage growth revealed the limitations of CPU power. GPUs, with their unparalleled parallel processing capabilities, have since taken center stage in data-intensive tasks like training large Transformer models. From Traditional ETL to Large Models Traditional ETL processes, optimized for "CPU-centric" computing, excel at handling rule-based, structured data tasks. Examples include: Data validation and cleaning. Format standardization. Aggregation and reporting. Large models, in contrast, require GPU power for high-dimensional matrix computations and large-scale parameter optimization: Preprocessing: Real-time normalization and data segmentation. Model training: Compute-heavy tasks involving floating-point operations. Inference services: Optimized batch processing for low latency and high throughput. This reflects a shift from logical computation to neural inference, broadening data processing to include reasoning and knowledge extraction. Toward a New Generation of ETL Architecture for Large Models The rise of large models highlights inefficiencies in traditional data processing, necessitating a more advanced, unified architecture. Pain Points in Current Data Processing: Complex, Fragmented Processes: Data cleaning, annotation, and preprocessing remain highly manual and siloed. Low Reusability: Teams often recreate data pipelines, leading to inefficiencies. Inconsistent Quality: The lack of standardized tools results in varying data quality. High Costs: Separate development and maintenance for each team inflate costs. Solutions: AI-Enhanced ETL Tools Future ETL tools will embed AI capabilities, merging traditional strengths with modern intelligence: Embedding Generation: Built-in support for text, image, and audio vectorization. LLM Knowledge Extraction: Automated structuring of unstructured data. Dynamic Cleaning Rules: Context-aware optimization of data cleaning strategies. Unstructured Data Handling: Support for keyframe extraction, OCR, and speech-to-text. Automated Augmentation: Intelligent data generation and enhancement. The Ultimate Trend: Transformers + Transform With the continuous advancement of technology, large models and traditional ETL processes are gradually converging. The next generation of ETL architectures is expected to blend the intelligence of large models with the efficiency of ETL, creating a comprehensive framework capable of processing diverse data types. Hardware: Integration of Data Processing Units The foundation of data processing is shifting from CPU-centric systems to a collaborative approach involving CPUs and GPUs: CPU for foundational tasks: CPUs excel at basic operations like preliminary data cleaning, integration, and rule-based processing, such as extracting, transforming, and loading structured data. GPU for advanced analytics: With powerful parallel computing capabilities, GPUs handle large model training and inference tasks on pre-processed data. This trend is reflected not only in technical innovation but also in industry dynamics: Intel is advancing AI accelerators for CPU-AI collaboration, while NVIDIA is expanding GPU applications into traditional ETL scenarios. The synergy between CPUs and GPUs promises higher efficiency and intelligent support for next-generation data processing. Software: Integration of Data Processing Architectures As ETL and large model functionalities become increasingly intertwined, data processing is evolving into a multifunctional, collaborative platform where ETL serves as a data preparation tool for large models. Large models require high-quality input data during training, and ETL provides the preliminary processing to create ideal conditions: Noise removal and cleaning: Eliminates noisy data to improve dataset quality. Formatting and standardization: Converts diverse data formats into a unified structure suitable for large models. Data augmentation: Expands data scale and diversity through preprocessing and rule-based enhancements. Emergence of AI-Enhanced ETL Architectures The future of ETL tools lies in embedding AI capabilities to achieve smarter data processing: Embedding Capabilities Integrating modules for generating embeddings to support vector-based data processing. Producing high-dimensional representations for text, images, and audio; using pre-trained models for semantic embeddings in downstream tasks. Performing embedding calculations directly within ETL workflows, reducing dependency on external inference services. LLM Knowledge Extraction Leveraging large language models (LLMs) to efficiently process unstructured data, extracting structured information like entities and events. Completing and inferring complex fields, such as filling in missing values or predicting future trends. Enabling multi-language data translation and semantic alignment during data integration. Unstructured Data Recognition and Keyframe Extraction Supporting video, image, and audio data natively, enabling automatic keyframe extraction for annotation or training datasets. Extracting features from images (e.g., object detection, OCR) and performing audio-to-text conversion, sentiment analysis, and more. Dynamic Cleaning Rules Dynamically adjusting cleaning and augmentation strategies based on data context to enhance efficiency and relevance. Detecting anomalies in real-time and generating adaptive cleaning rules. Optimizing cleaning strategies for specific domains (e.g., finance, healthcare). Automated Data Augmentation and Generation Dynamically augmenting datasets through AI models (e.g., synonym replacement, data back-translation, adversarial sample generation). Expanding datasets for low-sample scenarios and enabling cross-language or cross-domain data generation. AI-enhanced ETL represents a transformative leap from traditional ETL, offering embedding generation, LLM-based knowledge extraction, unstructured data processing, and dynamic rule generation to significantly improve efficiency, flexibility, and intelligence in data processing. Case Study: Apache SeaTunnel – A New Generation AI-Enhanced ETL Architecture As an example, the open-source Apache SeaTunnel project is breaking traditional ETL limitations by supporting innovative data formats and advanced processing capabilities, showcasing the future of data processing: Native support for unstructured data: The SeaTunnel engine supports text, video, and audio processing for diverse model training needs. Vectorized data support: Enables seamless compatibility with deep learning and large-model inference tasks. Embedding large model features: SeaTunnel v2.3.8 supports embedding generation and LLM transformations, bridging traditional ETL with AI inference workflows. “Any-to-Any” transformation: Transforms data from any source (e.g., databases, binlogs, PDFs, SaaS, videos) to any target format, delivering unmatched versatility. Tools like SeaTunnel illustrate how modern data processing has evolved into an AI+Big Data full-stack collaboration system, becoming central to enterprise AI and data strategies. Conclusion Large model transformers and big data transforms are not competitors but allies. The future of data processing lies in the deep integration of ETL and large models, as illustrated below: Collaborative data processing units: Leveraging CPU-GPU synergy for both structured and unstructured data processing. Dynamic data processing architecture: Embedding AI capabilities into ETL for embedding generation, LLM knowledge extraction, and intelligent decision-making. Next-gen tools: Open-source solutions like Apache SeaTunnel highlight this trend, enabling "Any-to-Any" data transformation and redefining ETL boundaries. The convergence of large models and ETL will propel data processing into a new era of intelligence, standardization, and openness. By addressing enterprise demands, this evolution will drive business innovation and intelligent decision-making, becoming a core engine for the future of data-driven enterprises. About the Author William Guo is a recognized leader in the big data and open-source communities. He is a Member of the Apache Software Foundation, serving as the PMC for Apache DolphinScheduler and a mentor for Apache SeaTunnel. He has also been a Track Chair for Workflow/Data Governance at ApacheCon Asia (2021/2022/2023) and a speaker at ApacheCon North America. With over 20 years of experience in big data technology and data management, William has held senior leadership roles, including Chief Technology Officer at Analysys, Senior Big Data Director at Lenovo, and Big Data Director/Manager at Teradata, IBM, and CICC. His extensive experience is focused on data warehousing, ETL processes, and data governance. He has developed and managed large-scale data systems, guiding enterprises through complex data integration and governance challenges, and demonstrating a strong track record in leading open-source initiatives and shaping enterprise-level big data strategies. Last year witnessed the explosive rise of large models, generating global enthusiasm and making AI seem like a solution to all problems. This year, as the hype subsides, large models have entered a deeper phase, aiming to reshape the foundational logic of various industries. In big data processing, the collision between large models and traditional ETL (Extract, Transform, Load) processes has sparked new debates. Large models feature “Transformers,” while ETL relies on “Transform” processes—similar names representing vastly different paradigms. Some voices boldly predict: "ETL will be completely replaced in the future, as large models can handle all data!" Does this signal the end of the decades-old ETL framework underpinning data processing? Or is it merely a misunderstood prediction? Behind this conflict lies a deeper contemplation of technology's future. "ETL will be completely replaced in the future, as large models can handle all data!" Will Big Data Processing (ETL) Disappear? With the rapid development of large models, some have begun to speculate whether traditional big data processing methods, including ETL, are still necessary. Large models, capable of autonomously learning rules and discovering patterns from vast datasets, are undeniably impressive. However, my answer is clear: ETL will not disappear. Large models still fail to address several core data challenges: ETL will not disappear. 1. Efficiency Issues Efficiency Issues Despite their outstanding performance in specific tasks, large models incur enormous computational costs. Training a large-scale Transformer model may take weeks and consume vast amounts of energy and financial resources. By contrast, ETL, which relies on predefined rules and logic, is efficient, resource-light, and excels at processing structured data. For everyday enterprise data tasks, many operations remain rule-driven, such as: Data Cleaning: Removing anomalies using clear rules or regular expressions. Format Conversion: Standardizing formats to facilitate data transmission and integration across systems. Aggregation and Statistics: Categorizing, aggregating, and calculating data daily, weekly, or monthly. Data Cleaning : Removing anomalies using clear rules or regular expressions. Data Cleaning Format Conversion : Standardizing formats to facilitate data transmission and integration across systems. Format Conversion Aggregation and Statistics : Categorizing, aggregating, and calculating data daily, weekly, or monthly. Aggregation and Statistics These tasks can be swiftly handled by ETL tools without requiring the complex inference capabilities of large models. 2. Ambiguity in Natural Language Ambiguity in Natural Language Large models have excelled in natural language processing (NLP) but have also exposed inherent challenges—ambiguity and vagueness in human language. For example: A single input query may yield varied interpretations depending on the context, with no guaranteed accuracy. Differences in data quality may lead models to generate results misaligned with real-world requirements. A single input query may yield varied interpretations depending on the context, with no guaranteed accuracy. Differences in data quality may lead models to generate results misaligned with real-world requirements. By contrast, ETL is deterministic, processing data based on pre-defined rules to produce predictable, standardized outputs. In high-demand sectors like finance and healthcare, ETL's reliability and precision remain critical advantages. 3. Strong Adaptability to Structured Data Strong Adaptability to Structured Data Large models are adept at extracting insights from unstructured data (e.g., text, images, videos), but they often struggle with structured data tasks. For instance: Traditional ETL efficiently processes relational databases, handling complex operations like JOINs and GROUP BYs. Large models require data to be converted into specific formats before processing, introducing redundancy and delays. Traditional ETL efficiently processes relational databases, handling complex operations like JOINs and GROUP BYs. Large models require data to be converted into specific formats before processing, introducing redundancy and delays. In scenarios dominated by structured data (e.g., tables, JSON), ETL remains the optimal choice. 4. Explainability and Compliance Explainability and Compliance Large models are often referred to as “black boxes.” Even when data processing is complete, their internal workings and decision-making mechanisms remain opaque: Unexplainable Results: In regulated industries like finance and healthcare, predictions from large models may be unusable due to their lack of transparency. Compliance Challenges: Many industries require full auditing of data flows and processing logic. Large models, with their complex data pipelines and decision mechanisms, pose significant auditing challenges. Unexplainable Results : In regulated industries like finance and healthcare, predictions from large models may be unusable due to their lack of transparency. Unexplainable Results Compliance Challenges : Many industries require full auditing of data flows and processing logic. Large models, with their complex data pipelines and decision mechanisms, pose significant auditing challenges. Compliance Challenges ETL, in contrast, provides highly transparent processes, with every data handling step documented and auditable, ensuring compliance with corporate and industry standards. 5. Data Quality and Input Standardization Data Quality and Input Standardization Large models are highly sensitive to data quality. Noise, anomalies, or non-standardized inputs can severely affect their performance: Data Noise: Large models cannot automatically identify erroneous data, potentially using it as "learning material" and producing biased predictions. Lack of Standardization: Feeding raw, uncleaned data into large models can result in inconsistencies and missing values, requiring preprocessing tools like ETL. Data Noise : Large models cannot automatically identify erroneous data, potentially using it as "learning material" and producing biased predictions. Data Noise Lack of Standardization : Feeding raw, uncleaned data into large models can result in inconsistencies and missing values, requiring preprocessing tools like ETL. Lack of Standardization ETL ensures data is cleaned, deduplicated, and standardized before being fed into large models, maintaining high data quality. Despite the excellence of large models in many areas, their complexity, reliance on high-quality data, hardware demands, and practical limitations ensure they cannot entirely replace ETL. As a deterministic, efficient, and transparent tool, ETL will continue to coexist with large models, providing dual safeguards for data processing. CPU vs. GPU: A Parallel to ETL vs. Large Models While ETL cannot be replaced, the rise of large models in data processing is an inevitable trend. For decades, computing systems were CPU-centric, with other components considered peripherals. GPUs were primarily used for gaming, but today, data processing relies on the synergy of CPUs and GPUs (or NPUs). This paradigm shift reflects broader changes, mirrored in the stock trends of Intel and NVIDIA. data processing relies on the synergy of CPUs and GPUs (or NPUs). From Single-Center to Multi-Center Computing From Single-Center to Multi-Center Computing Historically, data processing architectures evolved from "CPU-centric" to "CPU+GPU (and even NPU) collaboration." This transition, driven by changes in computing performance requirements, has deeply influenced the choice of data processing tools. "CPU-centric" to "CPU+GPU (and even NPU) collaboration." During the CPU-centric era, early ETL processes heavily relied on CPU logic for operations like data cleaning, formatting, and aggregation. These tasks were well-suited to CPUs’ sequential processing capabilities. However, the rise of complex data formats (audio, video, text) and exponential storage growth revealed the limitations of CPU power. GPUs, with their unparalleled parallel processing capabilities, have since taken center stage in data-intensive tasks like training large Transformer models. From Traditional ETL to Large Models From Traditional ETL to Large Models Traditional ETL processes, optimized for "CPU-centric" computing, excel at handling rule-based, structured data tasks. Examples include: Data validation and cleaning. Format standardization. Aggregation and reporting. Data validation and cleaning. Format standardization. Aggregation and reporting. Large models, in contrast, require GPU power for high-dimensional matrix computations and large-scale parameter optimization: Preprocessing: Real-time normalization and data segmentation. Model training: Compute-heavy tasks involving floating-point operations. Inference services: Optimized batch processing for low latency and high throughput. Preprocessing: Real-time normalization and data segmentation. Model training: Compute-heavy tasks involving floating-point operations. Inference services: Optimized batch processing for low latency and high throughput. This reflects a shift from logical computation to neural inference, broadening data processing to include reasoning and knowledge extraction. logical computation to neural inference, reasoning and knowledge extraction. Toward a New Generation of ETL Architecture for Large Models The rise of large models highlights inefficiencies in traditional data processing, necessitating a more advanced, unified architecture. Pain Points in Current Data Processing: Complex, Fragmented Processes: Data cleaning, annotation, and preprocessing remain highly manual and siloed. Low Reusability: Teams often recreate data pipelines, leading to inefficiencies. Inconsistent Quality: The lack of standardized tools results in varying data quality. High Costs: Separate development and maintenance for each team inflate costs. Complex, Fragmented Processes : Data cleaning, annotation, and preprocessing remain highly manual and siloed. Complex, Fragmented Processes Low Reusability : Teams often recreate data pipelines, leading to inefficiencies. Low Reusability Inconsistent Quality : The lack of standardized tools results in varying data quality. Inconsistent Quality High Costs : Separate development and maintenance for each team inflate costs. High Costs Solutions: AI-Enhanced ETL Tools Future ETL tools will embed AI capabilities, merging traditional strengths with modern intelligence: Embedding Generation: Built-in support for text, image, and audio vectorization. LLM Knowledge Extraction: Automated structuring of unstructured data. Dynamic Cleaning Rules: Context-aware optimization of data cleaning strategies. Unstructured Data Handling: Support for keyframe extraction, OCR, and speech-to-text. Automated Augmentation: Intelligent data generation and enhancement. Embedding Generation : Built-in support for text, image, and audio vectorization. Embedding Generation LLM Knowledge Extraction : Automated structuring of unstructured data. LLM Knowledge Extraction Dynamic Cleaning Rules : Context-aware optimization of data cleaning strategies. Dynamic Cleaning Rules Unstructured Data Handling : Support for keyframe extraction, OCR, and speech-to-text. Unstructured Data Handling Automated Augmentation : Intelligent data generation and enhancement. Automated Augmentation The Ultimate Trend: Transformers + Transform With the continuous advancement of technology, large models and traditional ETL processes are gradually converging. The next generation of ETL architectures is expected to blend the intelligence of large models with the efficiency of ETL, creating a comprehensive framework capable of processing diverse data types. Hardware: Integration of Data Processing Units Hardware: Integration of Data Processing Units The foundation of data processing is shifting from CPU-centric systems to a collaborative approach involving CPUs and GPUs: CPU for foundational tasks: CPUs excel at basic operations like preliminary data cleaning, integration, and rule-based processing, such as extracting, transforming, and loading structured data. GPU for advanced analytics: With powerful parallel computing capabilities, GPUs handle large model training and inference tasks on pre-processed data. CPU for foundational tasks : CPUs excel at basic operations like preliminary data cleaning, integration, and rule-based processing, such as extracting, transforming, and loading structured data. CPU for foundational tasks GPU for advanced analytics : With powerful parallel computing capabilities, GPUs handle large model training and inference tasks on pre-processed data. GPU for advanced analytics This trend is reflected not only in technical innovation but also in industry dynamics: Intel is advancing AI accelerators for CPU-AI collaboration, while NVIDIA is expanding GPU applications into traditional ETL scenarios. The synergy between CPUs and GPUs promises higher efficiency and intelligent support for next-generation data processing. Software: Integration of Data Processing Architectures Software: Integration of Data Processing Architectures As ETL and large model functionalities become increasingly intertwined, data processing is evolving into a multifunctional, collaborative platform where ETL serves as a data preparation tool for large models. ETL serves as a data preparation tool for large models. Large models require high-quality input data during training, and ETL provides the preliminary processing to create ideal conditions: Noise removal and cleaning: Eliminates noisy data to improve dataset quality. Formatting and standardization: Converts diverse data formats into a unified structure suitable for large models. Data augmentation: Expands data scale and diversity through preprocessing and rule-based enhancements. Noise removal and cleaning : Eliminates noisy data to improve dataset quality. Noise removal and cleaning Formatting and standardization : Converts diverse data formats into a unified structure suitable for large models. Formatting and standardization Data augmentation : Expands data scale and diversity through preprocessing and rule-based enhancements. Data augmentation Emergence of AI-Enhanced ETL Architectures Emergence of AI-Enhanced ETL Architectures The future of ETL tools lies in embedding AI capabilities to achieve smarter data processing: Embedding Capabilities Integrating modules for generating embeddings to support vector-based data processing. Producing high-dimensional representations for text, images, and audio; using pre-trained models for semantic embeddings in downstream tasks. Performing embedding calculations directly within ETL workflows, reducing dependency on external inference services. LLM Knowledge Extraction Leveraging large language models (LLMs) to efficiently process unstructured data, extracting structured information like entities and events. Completing and inferring complex fields, such as filling in missing values or predicting future trends. Enabling multi-language data translation and semantic alignment during data integration. Unstructured Data Recognition and Keyframe Extraction Supporting video, image, and audio data natively, enabling automatic keyframe extraction for annotation or training datasets. Extracting features from images (e.g., object detection, OCR) and performing audio-to-text conversion, sentiment analysis, and more. Dynamic Cleaning Rules Dynamically adjusting cleaning and augmentation strategies based on data context to enhance efficiency and relevance. Detecting anomalies in real-time and generating adaptive cleaning rules. Optimizing cleaning strategies for specific domains (e.g., finance, healthcare). Automated Data Augmentation and Generation Dynamically augmenting datasets through AI models (e.g., synonym replacement, data back-translation, adversarial sample generation). Expanding datasets for low-sample scenarios and enabling cross-language or cross-domain data generation. Embedding Capabilities Integrating modules for generating embeddings to support vector-based data processing. Producing high-dimensional representations for text, images, and audio; using pre-trained models for semantic embeddings in downstream tasks. Performing embedding calculations directly within ETL workflows, reducing dependency on external inference services. Embedding Capabilities Integrating modules for generating embeddings to support vector-based data processing. Producing high-dimensional representations for text, images, and audio; using pre-trained models for semantic embeddings in downstream tasks. Performing embedding calculations directly within ETL workflows, reducing dependency on external inference services. Integrating modules for generating embeddings to support vector-based data processing. Producing high-dimensional representations for text, images, and audio; using pre-trained models for semantic embeddings in downstream tasks. Performing embedding calculations directly within ETL workflows, reducing dependency on external inference services. LLM Knowledge Extraction Leveraging large language models (LLMs) to efficiently process unstructured data, extracting structured information like entities and events. Completing and inferring complex fields, such as filling in missing values or predicting future trends. Enabling multi-language data translation and semantic alignment during data integration. LLM Knowledge Extraction Leveraging large language models (LLMs) to efficiently process unstructured data, extracting structured information like entities and events. Completing and inferring complex fields, such as filling in missing values or predicting future trends. Enabling multi-language data translation and semantic alignment during data integration. Leveraging large language models (LLMs) to efficiently process unstructured data, extracting structured information like entities and events. Completing and inferring complex fields, such as filling in missing values or predicting future trends. Enabling multi-language data translation and semantic alignment during data integration. Unstructured Data Recognition and Keyframe Extraction Supporting video, image, and audio data natively, enabling automatic keyframe extraction for annotation or training datasets. Extracting features from images (e.g., object detection, OCR) and performing audio-to-text conversion, sentiment analysis, and more. Unstructured Data Recognition and Keyframe Extraction Supporting video, image, and audio data natively, enabling automatic keyframe extraction for annotation or training datasets. Extracting features from images (e.g., object detection, OCR) and performing audio-to-text conversion, sentiment analysis, and more. Supporting video, image, and audio data natively, enabling automatic keyframe extraction for annotation or training datasets. Extracting features from images (e.g., object detection, OCR) and performing audio-to-text conversion, sentiment analysis, and more. Dynamic Cleaning Rules Dynamically adjusting cleaning and augmentation strategies based on data context to enhance efficiency and relevance. Detecting anomalies in real-time and generating adaptive cleaning rules. Optimizing cleaning strategies for specific domains (e.g., finance, healthcare). Dynamic Cleaning Rules Dynamically adjusting cleaning and augmentation strategies based on data context to enhance efficiency and relevance. Detecting anomalies in real-time and generating adaptive cleaning rules. Optimizing cleaning strategies for specific domains (e.g., finance, healthcare). Dynamically adjusting cleaning and augmentation strategies based on data context to enhance efficiency and relevance. Detecting anomalies in real-time and generating adaptive cleaning rules. Optimizing cleaning strategies for specific domains (e.g., finance, healthcare). Automated Data Augmentation and Generation Dynamically augmenting datasets through AI models (e.g., synonym replacement, data back-translation, adversarial sample generation). Expanding datasets for low-sample scenarios and enabling cross-language or cross-domain data generation. Automated Data Augmentation and Generation Dynamically augmenting datasets through AI models (e.g., synonym replacement, data back-translation, adversarial sample generation). Expanding datasets for low-sample scenarios and enabling cross-language or cross-domain data generation. Dynamically augmenting datasets through AI models (e.g., synonym replacement, data back-translation, adversarial sample generation). Expanding datasets for low-sample scenarios and enabling cross-language or cross-domain data generation. AI-enhanced ETL represents a transformative leap from traditional ETL, offering embedding generation, LLM-based knowledge extraction, unstructured data processing, and dynamic rule generation to significantly improve efficiency, flexibility, and intelligence in data processing. Case Study: Apache SeaTunnel – A New Generation AI-Enhanced ETL Architecture Case Study: Apache SeaTunnel – A New Generation AI-Enhanced ETL Architecture As an example, the open-source Apache SeaTunnel project is breaking traditional ETL limitations by supporting innovative data formats and advanced processing capabilities, showcasing the future of data processing: Apache SeaTunnel Native support for unstructured data: The SeaTunnel engine supports text, video, and audio processing for diverse model training needs. Vectorized data support: Enables seamless compatibility with deep learning and large-model inference tasks. Embedding large model features: SeaTunnel v2.3.8 supports embedding generation and LLM transformations, bridging traditional ETL with AI inference workflows. “Any-to-Any” transformation: Transforms data from any source (e.g., databases, binlogs, PDFs, SaaS, videos) to any target format, delivering unmatched versatility. Native support for unstructured data : The SeaTunnel engine supports text, video, and audio processing for diverse model training needs. Native support for unstructured data Vectorized data support : Enables seamless compatibility with deep learning and large-model inference tasks. Vectorized data support Embedding large model features : SeaTunnel v2.3.8 supports embedding generation and LLM transformations, bridging traditional ETL with AI inference workflows. Embedding large model features “Any-to-Any” transformation : Transforms data from any source (e.g., databases, binlogs, PDFs, SaaS, videos) to any target format, delivering unmatched versatility. “Any-to-Any” transformation Tools like SeaTunnel illustrate how modern data processing has evolved into an AI+Big Data full-stack collaboration system , becoming central to enterprise AI and data strategies. AI+Big Data full-stack collaboration system Conclusion Conclusion Large model transformers and big data transforms are not competitors but allies. The future of data processing lies in the deep integration of ETL and large models , as illustrated below: transformers transforms deep integration of ETL and large models Collaborative data processing units: Leveraging CPU-GPU synergy for both structured and unstructured data processing. Dynamic data processing architecture: Embedding AI capabilities into ETL for embedding generation, LLM knowledge extraction, and intelligent decision-making. Next-gen tools: Open-source solutions like Apache SeaTunnel highlight this trend, enabling "Any-to-Any" data transformation and redefining ETL boundaries. Collaborative data processing units : Leveraging CPU-GPU synergy for both structured and unstructured data processing. Collaborative data processing units Dynamic data processing architecture : Embedding AI capabilities into ETL for embedding generation, LLM knowledge extraction, and intelligent decision-making. Dynamic data processing architecture Next-gen tools : Open-source solutions like Apache SeaTunnel highlight this trend, enabling "Any-to-Any" data transformation and redefining ETL boundaries. Next-gen tools The convergence of large models and ETL will propel data processing into a new era of intelligence, standardization, and openness. By addressing enterprise demands, this evolution will drive business innovation and intelligent decision-making, becoming a core engine for the future of data-driven enterprises. About the Author William Guo is a recognized leader in the big data and open-source communities. He is a Member of the Apache Software Foundation, serving as the PMC for Apache DolphinScheduler and a mentor for Apache SeaTunnel. He has also been a Track Chair for Workflow/Data Governance at ApacheCon Asia (2021/2022/2023) and a speaker at ApacheCon North America. With over 20 years of experience in big data technology and data management, William has held senior leadership roles, including Chief Technology Officer at Analysys, Senior Big Data Director at Lenovo, and Big Data Director/Manager at Teradata, IBM, and CICC. His extensive experience is focused on data warehousing, ETL processes, and data governance. He has developed and managed large-scale data systems, guiding enterprises through complex data integration and governance challenges, and demonstrating a strong track record in leading open-source initiatives and shaping enterprise-level big data strategies.
