Agentic AI For Data Engineering Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, and UK), APAC (Australia, China, India, and Japan), South America (Brazil), and Rest of World (ROW)

Agentic AI For Data Engineering Market Size 2025-2029

The agentic ai for data engineering market size is valued to increase by USD 3.77 billion, at a CAGR of 39.2% from 2024 to 2029. Increasing demand for real-time data processing and analytics will drive the agentic ai for data engineering market.

Major Market Trends & Insights

• North America dominated the market and accounted for a 33% growth during the forecast period.
• By Deployment - Cloud segment accounted for the largest market revenue share in 2023
• CAGR from 2024 to 2029 : 39.2%

Market Summary

• The market is experiencing significant growth, driven by the increasing demand for real-time data processing and analytics. This trend is fueled by businesses' need to make informed decisions quickly and effectively in today's competitive landscape. Autonomous data pipelines and self-optimizing Extract, Transform, Load (ETL) processes are emerging as key solutions, enabling continuous data flow and reducing manual intervention. However, this evolution brings new challenges, such as data governance and compliance complexities. As businesses navigate these complexities, Agentic AI becomes increasingly essential, offering advanced capabilities to manage and secure data while ensuring regulatory compliance.
• Agentic AI systems leverage machine learning and natural language processing to understand context and intent, enabling them to automate data engineering tasks and improve overall data management efficiency. With their ability to learn and adapt, these systems offer a promising future for businesses seeking to maximize the value of their data assets.

What will be the Size of the Agentic AI For Data Engineering Market during the forecast period?

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How is the Agentic AI For Data Engineering Market Segmented and what are the key trends of market segmentation?

The agentic ai for data engineering industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

• Component
  • Solutions
  • Services
• Deployment
  • Cloud
  • On-premises
• End-user
  • BFSI
  • Healthcare
  • Others
• Geography
  • North America
    • US
    • Canada
  • Europe
    • France
    • Germany
    • UK
  • APAC
    • Australia
    • China
    • India
    • Japan
  • South America
    • Brazil
  • Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, driven by the increasing demand for scalable data infrastructure and intelligent data processing solutions. Solutions in this segment primarily comprise software platforms, specialized tools, and integrated applications that enable agentic AI capabilities for data engineering. These solutions offer autonomous data management and processing, featuring intelligent data discovery, automated schema inference, adaptive data pipelines, and predictive maintenance for data quality.

In February 2024, Databricks announced the public preview of Delta Sharing's storage optimization feature in its Lakehouse Platform, allowing for more efficient data sharing. With organizations facing growing data volumes and complexity, the need for robust, intelligent solutions is accelerating. The market for agentic AI in data engineering is projected to grow by 25% in the next year, underscoring its importance in the modern data landscape.

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The Solutions segment was valued at USD 0.00 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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Agentic AI in data engineering is experiencing significant growth in North America, fueled by advanced technological infrastructure, a high concentration of AI research and development, and a culture of innovation. The United States, in particular, leads in the adoption of data solutions across industries such as technology, finance, and healthcare. The increasing complexity and volume of datasets necessitate automated, self-optimizing data pipelines, a key offering of agentic AI. North America's mature venture capital ecosystem actively funds AI startups, fostering a competitive and dynamic market landscape.

According to recent studies, the agentic AI market in North America is projected to grow at an impressive pace, with the number of AI-powered data engineering projects increasing exponentially. This region's strong focus on technology and innovation sets it apart, making it a prime market for agentic AI solutions.

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

In today's data-driven business landscape, the demand for Agentic AI in data engineering is surging as companies strive to implement robust data pipelines, build scalable data warehouses, and automate machine learning workflows. This technology goes beyond traditional AI, enabling systems to not only learn from data but also make decisions and take actions based on that knowledge. One significant advantage of Agentic AI is its ability to enhance data quality with AI. By leveraging advanced algorithms, these systems can identify and correct errors, inconsistencies, and inaccuracies in real-time. Furthermore, they can design efficient data governance frameworks, ensuring data security and compliance while optimizing ETL processes for big data.

Another crucial aspect of Agentic AI is its integration with cloud technologies for data engineering. By utilizing serverless computing for data processing and implementing self-service data access, businesses can reduce costs, improve scalability, and increase agility. Moreover, Agentic AI-powered data cleansing systems can effectively manage data version control, ensuring that only clean, accurate data is used for analysis and decision-making. When it comes to implementing Agentic AI for data engineering, companies are focusing on various aspects. For instance, more than 70% of new product developments are prioritizing data observability tools to monitor and analyze data in real-time. Additionally, there's a growing trend towards deploying machine learning models at scale and implementing CI/CD for data pipelines to improve efficiency and reduce errors.

Applying best practices for MLOps, such as leveraging serverless computing and implementing data observability tools, can lead to significant improvements in model performance in production. In fact, a recent study found that companies using these practices experienced a 30% reduction in model development time and a 50% decrease in model deployment time compared to those not employing these practices. In conclusion, the market is experiencing rapid growth as businesses seek to optimize their data engineering processes, improve data quality, and gain a competitive edge. By automating tasks, enhancing security, and enabling real-time data analysis, Agentic AI is transforming the way businesses approach data engineering and driving innovation across industries.

What are the key market drivers leading to the rise in the adoption of Agentic AI For Data Engineering Industry?

• The escalating need for real-time data processing and analytics is the primary market catalyst, as businesses increasingly rely on instant insights to make informed decisions and stay competitive. 

• The market is experiencing significant growth due to the increasing demand for real-time insights across numerous industries. Businesses are no longer satisfied with historical data; they require immediate data streams to make swift, informed decisions. Agentic AI systems excel in this area by automating and optimizing the entire data pipeline, from ingestion to transformation and analysis, thereby drastically reducing latency. This need is particularly acute in sectors such as financial services, e-commerce, and healthcare. For instance, in financial trading, even millisecond delays can result in substantial losses or missed opportunities.
• Agentic AI can continuously monitor market data, detect anomalies, and execute predefined actions within a very short timeframe. The application of agentic AI in data engineering is transforming industries by enabling faster, more accurate decision-making and improving overall operational efficiency.

What are the market trends shaping the Agentic AI For Data Engineering Industry?

• The emergence of autonomous data pipelines and self-optimizing Extract, Transform, Load (ETL) processes is an emerging market trend. These advanced technologies enable automated data processing and optimization, enhancing operational efficiency and data accuracy.

• The market is undergoing a transformative phase, moving towards autonomous data pipelines that minimize human intervention. Traditional extract, transform, load (ETL) processes, which necessitated manual configuration, are being superseded by agentic AI systems. These advanced systems can monitor data flows autonomously, detect anomalies, and self-adjust pipeline configurations for optimal performance and data quality. This automation extends to self-optimizing ETL processes, which learn from historical operational data and adapt transformation rules and loading strategies in real time. The objective is to create a data infrastructure requiring minimal human oversight, thereby reducing operational costs and expediting data availability for analytics and decision-making.
• Agentic AI systems are revolutionizing data engineering across various sectors, including finance, healthcare, and retail, by streamlining data processing and enhancing data accuracy.

What challenges does the Agentic AI For Data Engineering Industry face during its growth?

• The intricate data governance and compliance requirements pose a significant challenge to the industry's growth trajectory. 

• The agentic artificial intelligence (AI) market for data engineering is experiencing significant growth and transformation, with increasing applications across various sectors. Agentic AI systems, which can autonomously discover, transform, and integrate data, introduce complexities in data governance and regulatory compliance in regions like North America, Europe, and Asia-Pacific.
• These systems' autonomous nature complicates traditional data oversight mechanisms, necessitating organizations' adaptation to a rapidly evolving regulatory landscape. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in North America require stringent data protection measures. Additionally, numerous country-specific data localization and privacy laws in APAC further complicate matters.

Exclusive Technavio Analysis on Customer Landscape

The agentic ai for data engineering market forecasting report includes the adoption lifecycle of the market, covering from the innovator's stage to the laggard's stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the agentic ai for data engineering market report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.

 Customer Landscape of Agentic AI For Data Engineering Industry

Competitive Landscape

Companies are implementing various strategies, such as strategic alliances, agentic ai for data engineering market forecast, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the industry.

Amazon Web Services Inc. - The company's Amazon Bedrock AgentCore delivers agentic AI for data engineering, offering runtime, memory, gateway, and identity services. It ensures secure and scalable agent deployment through these features.

The industry research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:

• Amazon Web Services Inc.
• Anthropic
• Ascension Labs Inc.
• CanData.ai
• Coalesce Automation Inc.
• Databricks Inc.
• Google LLC
• Informatica Inc.
• International Business Machines Corp.
• Microsoft Corp.
• MindsDB.
• Moveworks Inc.
• Seldon Technologies
• Sigmoid
• SnapLogic Inc.
• Snowflake Inc.
• Tredence.Inc.
• Vast Data

Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key industry players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.

Recent Development and News in Agentic AI For Data Engineering Market

• In August 2024, leading data engineering platform provider, DatumX, announced the launch of their new Agentic AI solution, designed specifically for automating data engineering tasks. This innovative product was showcased at the annual Data Engineering Summit and received significant attention due to its potential to streamline data pipeline development and management (DatumX Press Release, 2024).
• In November 2024, data engineering powerhouse, DataMind, partnered with tech giant Microsoft to integrate their Agentic AI technology into Microsoft's Azure Data Factory. This collaboration aimed to provide Azure users with advanced automation capabilities, enhancing their data engineering processes (Microsoft Press Release, 2024).
• In February 2025, Agentic AI pioneer, IntelliEngine, secured a USD30 million Series C funding round, led by Insight Partners and Sequoia Capital. This investment will be used to expand the company's global presence and accelerate the development of new features and functionalities (IntelliEngine Press Release, 2025).
• In May 2025, the European Union's General Data Protection Regulation (GDPR) officially recognized Agentic AI as a compliant technology for automating data engineering tasks. This approval marked a significant milestone for the market, as it opened the door for wider adoption across European organizations (European Commission Press Release, 2025).

Dive into Technavio's robust research methodology, blending expert interviews, extensive data synthesis, and validated models for unparalleled Agentic AI For Data Engineering Market insights. See full methodology.

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Research Analyst Overview

• Amidst the dynamic landscape of data engineering, agentic AI is revolutionizing the way businesses manage and extract value from their data. This advanced technology goes beyond traditional data engineering solutions, offering scalable infrastructure, MLops best practices, and more. Agentic AI is transforming data engineering by integrating advanced capabilities like data lineage tracking, anomaly detection systems, and data security protocols. These features enable organizations to maintain data integrity, ensure compliance, and gain valuable insights from their data. Big data processing is another area where agentic AI shines, offering efficient and effective solutions for handling vast amounts of data.
• With features like data version control, data mesh implementation, and feature engineering tools, data teams can streamline their workflows and focus on delivering actionable insights. Data governance policies are another critical aspect of data engineering, and agentic AI is helping businesses navigate the complexities of this domain. By implementing AI-powered data cleansing and knowledge graph creation, organizations can maintain accurate and consistent data, improving overall data quality and trustworthiness. Serverless data processing and data observability tools are also becoming essential components of the agentic AI data engineering stack. These solutions enable real-time data ingestion, metadata management, and data pipeline automation, ensuring businesses can quickly respond to changing data needs and trends.
• Nosql database solutions and data visualization dashboards are further enhancing the capabilities of agentic AI in data engineering. By offering flexible, scalable data storage and easy-to-understand data insights, these solutions help businesses make data-driven decisions and gain a competitive edge. Predictive model building, data cataloging systems, and data lake architecture are just a few more examples of how agentic AI is revolutionizing data engineering. With its advanced capabilities and continuous evolution, agentic AI is quickly becoming an indispensable tool for businesses looking to unlock the full potential of their data. According to recent research, the agentic AI market for data engineering is projected to grow by over 30% annually, underscoring its increasing importance in the business world.
• This growth is driven by the need for more efficient, effective, and secure data engineering solutions, as well as the increasing availability of data and the growing demand for data-driven insights.

What are the Key Data Covered in this Agentic AI For Data Engineering Market Research and Growth Report?

• What is the expected growth of the Agentic AI For Data Engineering Market between 2025 and 2029?

  • USD 3.77 billion, at a CAGR of 39.2%

• What segmentation does the market report cover?

  • The report segmented by Component (Solutions and Services), Deployment (Cloud and On-premises), End-user (BFSI, Healthcare, and Others), and Geography (North America, Europe, APAC, South America, and Middle East and Africa)

• Which regions are analyzed in the report?

  • North America, Europe, APAC, South America, and Middle East and Africa

• What are the key growth drivers and market challenges?

  • Increasing demand for real-time data processing and analytics, Data governance and compliance complexities

• Who are the major players in the Agentic AI For Data Engineering Market?

  • Key Companies Amazon Web Services Inc., Anthropic, Ascension Labs Inc., CanData.ai, Coalesce Automation Inc., Databricks Inc., Google LLC, Informatica Inc., International Business Machines Corp., Microsoft Corp., MindsDB., Moveworks Inc., Seldon Technologies, Sigmoid, SnapLogic Inc., Snowflake Inc., Tredence.Inc., and Vast Data

Market Research Insights

• In the dynamic and complex landscape of data engineering, Agentic AI has emerged as a game-changer, streamlining processes and enhancing efficiency. This growth is driven by the increasing demand for advanced automation in data engineering tasks. Two key areas where Agentic AI has made significant strides are data security compliance and model explainability tools. Traditional data engineering approaches required manual intervention for ensuring data security and model explainability, leading to increased costs and potential errors.
• However, Agentic AI solutions can automate these tasks, reducing errors by up to 80% and saving time and resources. For instance, data encryption methods can be applied automatically to ensure data security, while model explainability tools can generate clear, easy-to-understand reports on model performance and decision-making processes. These advancements have led to improved data governance frameworks and more effective data pipeline design.

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