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市場調查報告書
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2170045

人工智慧資料工程市場預測至2034年－按組件、部署模式、資料類型、資料處理類型、應用、最終使用者和地區分類的全球分析

AI Data Engineering Market Forecasts to 2034 - Global Analysis By Component (Software and Services), Deployment Mode, Data Type, Data Processing Type, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球人工智慧數據工程市場規模將達到 189 億美元，並在預測期內以 17.0% 的複合年成長率成長，到 2034 年將達到 664 億美元。

人工智慧資料工程是專門設計、建置和管理資料基礎設施和管道的領域，旨在支援人工智慧 (AI) 和機器學習工作負載。這包括用於整合、轉換、管治和交付 AI 模型所需資料的工具、平台和流程，例如自動化管道生成、即時串流處理、特徵工程和資料品管。這些系統使組織能夠向 AI 代理和分析應用程式提供現代化、管治且易於存取的數據，從而確保模型可靠且符合上下文地運作。

人工智慧驅動的管道需求

人工智慧在企業中的快速普及，對能夠支援人工智慧工作負載的資料工程基礎設施的需求空前高漲。這是因為各組織都意識到，資料品質和管道可靠性直接影響人工智慧模型的效能。預計到2027年，超過60%的資料工程用例將涉及向人工智慧代理而非人工分析師提供資料。同時，基礎設施支出較去年同期成長57%，為人工智慧最佳化的資料工程平台和服務帶來了持續的商業性動力。

整合舊有系統的複雜性

許多公司仍在運行並非為人工智慧工作負載而設計的傳統資料基礎設施，這造成了巨大的整合挑戰，並延緩了現代人工智慧資料工程解決方案的採用。據報告，各公司平均每月發生 67 起需要修正的數據事件，而數據品質仍然是企業採用人工智慧的最大障礙，67% 的公司都提到了這一點。這些傳統限制增加了部署成本和複雜性，最終限制了人工智慧資料工程平台在現有企業環境中的部署速度。

基於代理的數據自動化

能夠自主規劃和執行資料工程任務的基於代理的人工智慧系統的出現，為市場帶來了變革性的機會。這些系統可以自動完成以往需要大量人工干預的管道創建、資料轉換和檢驗工作流程。 Gartner 預測，到 2029 年，基於代理商的資料管理將實現 75% 的資料工程工作流程自動化。此外，向原生 AI 資料基礎設施的轉型將催生新的平台類別，從而形成一個遠遠超出傳統資料整合工具範圍的目標市場。

平台整合的壓力

隨著資料基礎設施日益向統一的雲端平台整合，超大規模資料中心業者服務商正以整合服務的形式提供資料處理、管治和人工智慧功能，這對獨立的人工智慧資料工程供應商構成了重大威脅。企業越來越傾向於選擇端到端平台，以降低整合複雜性和供應商管理負擔，而開放原始碼方案則為注重成本的企業提供了功能齊全且免費的選擇。因此，利潤空間受到擠壓，單點解決方案供應商的差異化競爭也變得愈發艱難。

新型冠狀病毒（COVID-19）的影響：

新冠疫情初期，由於企業推遲非核心基礎設施投資並將資源轉向保障業務永續營運，資料工程專案受到衝擊​​。疫情期間，數位轉型加速和雲端運算應用激增，再次推高了對可擴展數據管道的需求。同時，疫情後向人工智慧驅動的自動化、即時數據處理以及完善且管治的上下文環境的結構性轉變，已將人工智慧數據工程從單純的輔助功能永久提升為企業人工智慧成功的核心戰略能力。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率，這主要得益於市場對原生AI數據工程平台的需求不斷成長，這些平台能夠自動生成管道、進行數據轉換以及在雲端和混合環境中品管。企業越來越重視軟體驅動的自動化，以減輕人工工程的負擔並加速AI的引進週期。同時，內建的資料工程功能對於與MLOps和LLMOps工作流程的持續整合至關重要，預計這將推動雲端原生和本地部署軟體的持續收入成長。

在預測期內，基於雲端的細分市場預計將呈現最高的複合年成長率。

在預測期內，雲端細分市場預計將呈現最高的成長率，這主要得益於其可擴展性、柔軟性以及託管服務能力，這些能力能夠支援企業級多樣化的人工智慧工作負載和分散式資料處理。隨著企業將其資料基礎架構遷移到雲端平台並採用無伺服器和容器化化架構，對雲端原生資料工程解決方案的需求正在加速成長。這推動了平台創新，因此，雲端應用正成為人工智慧數據工程市場中成長最快的細分領域。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於美國和加拿大境內眾多大型科技公司的集中分佈、成熟的雲端基礎設施以及企業對人工智慧驅動的資料工程平台的早期採用。該地區受益於數據基礎設施新創企業的大量風險投資、銀行、金融和保險（BFSI）以及醫療保健行業的強勁需求，以及湖倉式和即時數據架構的積極應用。此外，總部位於該地區的領先供應商持續推動創新，進一步鞏固了北美在人工智慧數據工程市場的主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度、日本和韓國的快速數位轉型，以及人工智慧應用的不斷擴展和政府主導的數據基礎設施建設舉措。憑藉大規模的開發者生態系統、對雲端運算和人工智慧技術日益成長的投資，以及對即時數據處理能力不斷成長的需求，該地區已成為人工智慧數據工程市場成長最快的地區，本地供應商和全球企業都在積極拓展企業發展。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 主要參與者（最多3家公司）的SWOT分析
  • 區域細分
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧資料工程市場：按組件分類

  • 軟體
  • 服務

第6章 全球人工智慧資料工程市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合

第7章 全球人工智慧資料工程市場：按資料類型分類

  • 結構化資料
  • 半結構化數據
  • 非結構化數據

第8章：全球人工智慧資料工程市場：按資料處理類型分類

  • 批量處理
  • 即時和串流處理

第9章 全球人工智慧資料工程市場：按應用領域分類

  • 數據整合和管道自動化
  • 數據品管
  • 資料轉換與預處理
  • 資料管治與合規
  • AI/ML資料管道管理
  • 其他用途

第10章：全球人工智慧資料工程市場：按最終用戶分類

  • 銀行、金融服務和保險（BFSI）
  • 資訊科技/通訊
  • 醫療保健和生命科學
  • 零售與電子商務
  • 製造業
  • 其他最終用戶

第11章 全球人工智慧資料工程市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • Microsoft Corporation
  • Amazon Web Services, Inc.
  • Google LLC
  • International Business Machines Corporation（IBM）
  • Oracle Corporation
  • Snowflake Inc.
  • Databricks, Inc.
  • Informatica LLC
  • SAP SE
  • Salesforce, Inc.
  • QlikTech International AB
  • Fivetran, Inc.
  • Matillion Limited
  • Talend
  • Alteryx, Inc.
  • Dataiku
  • Precisely
Product Code: SMRC40774

According to Stratistics MRC, the Global AI Data Engineering Market is accounted for $18.9 billion in 2026 and is expected to reach $66.4 billion by 2034 growing at a CAGR of 17.0% during the forecast period. AI data engineering refers to the specialized discipline of designing, building, and managing data infrastructure and pipelines specifically optimized to support artificial intelligence and machine learning workloads. It encompasses the tools, platforms, and processes used to integrate, transform, govern, and serve data for AI models, including automated pipeline generation, real-time stream processing, feature engineering, and data quality management. These systems enable organizations to deliver fresh, governed, and retrieval-ready data to AI agents and analytical applications, thereby ensuring that models operate on reliable and contextual information.

Market Dynamics:

Driver:

AI-Driven Pipeline Demand

The rapid adoption of artificial intelligence across enterprises is driving unprecedented demand for data engineering infrastructure capable of supporting AI workloads, as organizations recognize that data quality and pipeline reliability directly determine AI model performance. Over 60 percent of data engineering use cases are expected to involve providing data to AI agents rather than human analysts by 2027, while infrastructure spending has grown 57 percent year-over-year, thereby creating sustained commercial momentum for AI-optimized data engineering platforms and services.

Restraint:

Legacy Integration Complexity

Many enterprises continue to operate legacy data infrastructure that was not designed for AI workloads, creating significant integration challenges that slow adoption of modern AI data engineering solutions. Organizations report an average of 67 monthly data incidents requiring remediation, while data quality remains the top barrier to enterprise AI adoption, named by 67 percent of enterprises. These legacy constraints increase implementation costs and complexity, thereby limiting the pace at which AI data engineering platforms can be deployed across existing enterprise environments.

Opportunity:

Agentic Data Automation

The emergence of agentic AI systems capable of autonomously planning and executing data engineering tasks presents a transformative opportunity for the market, as these systems can automate pipeline creation, data transformation, and validation workflows that previously required extensive manual intervention. Gartner predicts that agentic data management will automate 75 percent of data engineering workflows by 2029, while the shift toward AI-native data infrastructure creates new platform categories, thereby expanding the addressable market well beyond traditional data integration tools.

Threat:

Platform Consolidation Pressure

The growing consolidation of data infrastructure into integrated cloud platforms poses a significant threat to standalone AI data engineering vendors, as hyperscalers bundle data processing, governance, and AI capabilities into unified offerings. Organizations increasingly prefer end-to-end platforms that reduce integration complexity and vendor management overhead, while open-source alternatives provide capable free options for cost-conscious enterprises, thereby compressing margins and challenging differentiation for point-solution providers.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted data engineering projects as enterprises deferred non-critical infrastructure investments and redirected resources toward immediate operational continuity. Mid-pandemic, accelerated digital transformation and the surge in cloud adoption drove renewed demand for scalable data pipelines, while post-pandemic structural shifts toward AI-driven automation, real-time data processing, and governed context delivery have permanently elevated AI data engineering from a supporting function to a core strategic capability for enterprise AI success.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, due to the growing demand for AI-native data engineering platforms that automate pipeline generation, data transformation, and quality management across cloud and hybrid environments. Enterprises increasingly prioritize software-driven automation to reduce manual engineering effort and accelerate AI deployment cycles, while continuous integration with MLOps and LLMOps workflows requires embedded data engineering capabilities, thereby driving sustained revenue growth across cloud-native and on-premises software deployments.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, driven by the scalability, flexibility, and managed service capabilities required to support variable AI workloads and distributed data processing at enterprise scale. As organizations migrate data infrastructure to cloud platforms and adopt serverless and containerized architectures, demand for cloud-native data engineering solutions accelerates, which in turn drives platform innovation, thereby establishing cloud-based deployment as the fastest-growing segment within the AI data engineering market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the concentration of leading technology companies, mature cloud infrastructure, and early enterprise adoption of AI-driven data engineering platforms across the United States and Canada. The region benefits from substantial venture investment in data infrastructure startups, strong demand from BFSI and healthcare sectors, and proactive adoption of lakehouse and real-time data architectures, while major vendors headquartered in the region continue to drive innovation, thereby reinforcing North America's dominant position in the AI data engineering market.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapid digital transformation across China, India, Japan, and South Korea, coupled with expanding AI adoption and government-backed data infrastructure initiatives. The region benefits from a large developer ecosystem, increasing investment in cloud and AI technologies, and growing demand for real-time data processing capabilities, while local vendors and global players expand their presence, thereby positioning Asia Pacific as the fastest-growing region in the AI data engineering market.

Key players in the market

Some of the key players in AI Data Engineering Market include Microsoft Corporation, Amazon Web Services, Inc., Google LLC, International Business Machines Corporation (IBM), Oracle Corporation, Snowflake Inc., Databricks, Inc., Informatica LLC, SAP SE, Salesforce, Inc., QlikTech International AB, Fivetran, Inc., Matillion Limited, Talend, Alteryx, Inc., Dataiku, and Precisely.

Key Developments:

In October 2026, Dell Technologies expanded its AI Data Platform with a Unified Semantic Layer, Enterprise Knowledge Graph, and Knowledge Agents to give AI systems trusted context from enterprise data, with processing accelerated nearly four times faster than CPU configurations.

In October 2026, Aiven launched Runtime and DataHub for production AI agents, enabling organizations to run agents against live company data while maintaining governance, security, and interoperability across existing customer environments.

In September 2026, Teradata transformed Tera into an agentic coworker with the introduction of Tera Context Engine for governed enterprise knowledge and Tera Harness for intelligent multi-step execution across hybrid cloud environments.

In September 2026, Google LLC released the Data Engineering Agent for BigQuery, enabling users to build, modify, and troubleshoot data pipelines using natural language instructions within the platform interface.

In August 2026, Microsoft Corporation introduced the Fabric data engineering agent (Project Osmos) in preview, delivering autonomous planning, execution, and validation of persistent data engineering tasks against Fabric lakehouse resources.

Components Covered:

  • Software
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Data Types Covered:

  • Structured Data
  • Semi-Structured Data
  • Unstructured Data

Data Processing Types Covered:

  • Batch Processing
  • Real-Time & Stream Processing

Applications Covered:

  • Data Integration & Pipeline Automation
  • Data Quality Management
  • Data Transformation & Preparation
  • Data Governance & Compliance
  • AI/ML Data Pipeline Management
  • Other Applications

End Users Covered:

  • Banking, Financial Services & Insurance (BFSI)
  • IT & Telecommunications
  • Healthcare & Life Sciences
  • Retail & E-commerce
  • Manufacturing
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
      • Saudi Arabia
      • United Arab Emirates
      • Qatar
      • Israel
      • Rest of Middle East
    • Africa
      • South Africa
      • Egypt
      • Morocco
      • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI Data Engineering Market, By Component

  • 5.1 Software
  • 5.2 Services

6 Global AI Data Engineering Market, By Deployment Mode

  • 6.1 Cloud-Based
  • 6.2 On-Premises
  • 6.3 Hybrid

7 Global AI Data Engineering Market, By Data Type

  • 7.1 Structured Data
  • 7.2 Semi-Structured Data
  • 7.3 Unstructured Data

8 Global AI Data Engineering Market, By Data Processing Type

  • 8.1 Batch Processing
  • 8.2 Real-Time & Stream Processing

9 Global AI Data Engineering Market, By Application

  • 9.1 Data Integration & Pipeline Automation
  • 9.2 Data Quality Management
  • 9.3 Data Transformation & Preparation
  • 9.4 Data Governance & Compliance
  • 9.5 AI/ML Data Pipeline Management
  • 9.6 Other Applications

10 Global AI Data Engineering Market, By End User

  • 10.1 Banking, Financial Services & Insurance (BFSI)
  • 10.2 IT & Telecommunications
  • 10.3 Healthcare & Life Sciences
  • 10.4 Retail & E-commerce
  • 10.5 Manufacturing
  • 10.6 Other End Users

11 Global AI Data Engineering Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Microsoft Corporation
  • 14.2 Amazon Web Services, Inc.
  • 14.3 Google LLC
  • 14.4 International Business Machines Corporation (IBM)
  • 14.5 Oracle Corporation
  • 14.6 Snowflake Inc.
  • 14.7 Databricks, Inc.
  • 14.8 Informatica LLC
  • 14.9 SAP SE
  • 14.10 Salesforce, Inc.
  • 14.11 QlikTech International AB
  • 14.12 Fivetran, Inc.
  • 14.13 Matillion Limited
  • 14.14 Talend
  • 14.15 Alteryx, Inc.
  • 14.16 Dataiku
  • 14.17 Precisely

List of Tables

  • Table 1 Global AI Data Engineering Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Data Engineering Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI Data Engineering Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI Data Engineering Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global AI Data Engineering Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 6 Global AI Data Engineering Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 7 Global AI Data Engineering Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 8 Global AI Data Engineering Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 9 Global AI Data Engineering Market Outlook, By Data Type (2023-2034) ($MN)
  • Table 10 Global AI Data Engineering Market Outlook, By Structured Data (2023-2034) ($MN)
  • Table 11 Global AI Data Engineering Market Outlook, By Semi-Structured Data (2023-2034) ($MN)
  • Table 12 Global AI Data Engineering Market Outlook, By Unstructured Data (2023-2034) ($MN)
  • Table 13 Global AI Data Engineering Market Outlook, By Data Processing Type (2023-2034) ($MN)
  • Table 14 Global AI Data Engineering Market Outlook, By Batch Processing (2023-2034) ($MN)
  • Table 15 Global AI Data Engineering Market Outlook, By Real-Time & Stream Processing (2023-2034) ($MN)
  • Table 16 Global AI Data Engineering Market Outlook, By Application (2023-2034) ($MN)
  • Table 17 Global AI Data Engineering Market Outlook, By Data Integration & Pipeline Automation (2023-2034) ($MN)
  • Table 18 Global AI Data Engineering Market Outlook, By Data Quality Management (2023-2034) ($MN)
  • Table 19 Global AI Data Engineering Market Outlook, By Data Transformation & Preparation (2023-2034) ($MN)
  • Table 20 Global AI Data Engineering Market Outlook, By Data Governance & Compliance (2023-2034) ($MN)
  • Table 21 Global AI Data Engineering Market Outlook, By AI/ML Data Pipeline Management (2023-2034) ($MN)
  • Table 22 Global AI Data Engineering Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 23 Global AI Data Engineering Market Outlook, By End User (2023-2034) ($MN)
  • Table 24 Global AI Data Engineering Market Outlook, By Banking, Financial Services & Insurance (BFSI) (2023-2034) ($MN)
  • Table 25 Global AI Data Engineering Market Outlook, By IT & Telecommunications (2023-2034) ($MN)
  • Table 26 Global AI Data Engineering Market Outlook, By Healthcare & Life Sciences (2023-2034) ($MN)
  • Table 27 Global AI Data Engineering Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
  • Table 28 Global AI Data Engineering Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 29 Global AI Data Engineering Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.