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1889284

資料工程市場預測至 2032 年：按組件、部署方式、資料類型、最終用戶和地區分類的全球分析

Data Engineering Market Forecasts to 2032 - Global Analysis By Component (Tools & Platforms and Services), Deployment, Data Type, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 預測，全球數據工程市場規模預計將在 2025 年達到 915.4 億美元，並在 2032 年達到 2,491.8 億美元，預測期內複合年成長率 (CAGR) 為 15.38%。數據工程專注於開發和管理用於收集、處理和組織數據的系統，供組織內部使用。這包括建立可擴展的資料管道、連接多個資料來源、維護資料準確性以及確保安全高效的儲存。資料工程師利用雲端技術、巨量資料框架和 ETL 技術來確保資料的可存取性和可靠性。他們的努力使分析師、資料科學家和機器學習工具能夠利用乾淨、結構化的資訊。透過提高資料可靠性、改善工作流程和確保強力的管治，資料工程使企業能夠將複雜的資料集轉化為策略洞察，從而支援更快的決策和長期的業務成長。

根據美國勞工統計局（BLS）的數據，包括數據工程在內的電腦和資訊科技職業，預計在2021年至2031年間就業成長率將達到15%，遠高於所有職業的平均水準。這項預測也支撐了巨量資料母市場的擴張。

巨量資料和即時分析日益普及

巨量資料產生的快速成長和對即時洞察日益成長的需求正在推動數據工程市場的發展。現代企業透過連網設備、線上互動和內部系統產生大量、複雜的資料流。為了支援即時儀錶板、預測智慧和營運分析，企業需要強大的資料管道、可擴展的資料框架和可靠的處理平台。數據工程確保了銀行、電子商務和醫療保健等各個行業能夠快速、準確且易於存取的資訊。隨著企業追求更高的應對力和數據驅動的決策，對高效能資料生態系統、增強的分析能力和即時資料可用性的需求不斷成長，推動市場的持續擴張。

數據整合和管理的高度複雜性

資料工程市場成長面臨的關鍵挑戰之一是整合和管理多樣化資料集的困難。現代企業需要處理大量非結構化、半結構化和結構化數據，這些數據由舊有系統、雲端平台、連網設備和企業應用程式產生。協調這些資料來源、確保資料一致性並防止資料碎片化需要先進的工程技術和複雜的工具。許多組織面臨持續的資料孤島、互通性差和資料品質問題，這些都阻礙了高效率的分析。即時處理增加了額外的複雜性，需要持續的同步和可靠性。這些挑戰會增加營運成本、延長實施週期並降低資料工程計劃的整體效率。

對雲端原生、無伺服器資料基礎設施的需求日益成長

企業對雲端原生、無伺服器架構的日益青睞，為資料工程市場創造了巨大的機會。為了獲得更高的柔軟性、更快的可擴充性和更簡化的操作，企業正在遷移到 Azure Synapse、Google Cloud Dataflow 和 AWS Lambda 等平台。這些系統提供自動擴展、即時處理能力，並可輕鬆整合分析和 BI 工具。隨著數位轉型的加速，企業對雲端驅動的資料管道和現代資料框架的需求也日益成長。能夠以低維護成本處理大規模資料集是無伺服器環境的一大優勢。這種向可擴展、經濟高效的雲端解決方案的轉變，大大拓展了高階資料工程服務和工具的發展機會。

科技快速變化和工具過時

數據生態系統的快速創新帶來了重大威脅，因為現有工具和技術可能很快就會過時。新的平台、處理引擎和雲端原生架構層出不窮，迫使企業不斷更新技能、重構流程並實現系統現代化。頻繁的遷移會增加營運成本、降低工作流程穩定性並帶來整合方面的挑戰。許多公司缺乏足夠的資源來跟上科技發展的腳步。這種持續的升級壓力會阻礙對數據工程的長期投資。因此，對未來解決方案的不確定性會降低採用率，並阻礙資料工程專案的整體發展。

新冠疫情的影響：

新冠疫情重塑了數據工程市場，加速了數位化技術的應用，並凸顯了對可靠數據基礎設施的需求。隨著遠距辦公的普及，企業加大了對雲端基礎系統、即時分析和自動化數據管道的投資，以維持營運。醫療保健、線上零售和金融服務等行業需要更快地獲得洞察，以應對不斷變化的客戶需求和疫情帶來的衝擊。儘管經濟的不確定性導致一些企業暫時削減了IT支出，但卻提高了它們對數據驅動決策的整體依賴性。此次危機凸顯了擴充性且具彈性的數據系統的重要性，加速了現代化進程，並增強了對先進數據工程解決方案的長期需求。

預計在預測期內，雲端原生細分市場將佔據最大的市場佔有率。

預計在預測期內，雲端原生解決方案將佔據最大的市場佔有率，因為企業越來越依賴按需擴充性、經濟高效的資源利用以及根據需要自動擴展或縮減基礎設施的能力。微軟 Azure、Google雲端和 AWS 等雲端服務供應商提供託管服務、自動擴展和付費使用制，從而最大限度地減少了對大規模資本投資和維運的需求。雲端原生環境能夠快速部署資料擷取、串流管道和分析工作流程。這種敏捷性和彈性使得雲端原生解決方案優於本地部署和混合模式。

預計在預測期內，零售和電子商務領域的複合年成長率將最高。

預計在預測期內，零售和電子商務領域將保持最高的成長率，這主要得益於網路購物的成長、數位互動的拓展以及對更深入的客戶洞察的需求。該領域的企業依靠即時數據處理、可擴展的數據管道和高級分析技術來支援行銷、庫存管理和個人化客戶參與。數位支付、自動化平台和客戶行為追蹤的日益普及，推動了對先進數據工程系統的需求。隨著建議工具、詐欺分析和預測需求模型的廣泛應用，零售商需要強大的數據基礎設施來管理大量數據，這使得該領域成為市場中成長最快的領域。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率，這得益於其先進的IT基礎設施、廣泛的雲端整合以及對分析和人工智慧的重視。該地區受惠於眾多科技巨頭、主流雲端平台以及企業內部先進的數據策略。銀行、醫療保健和零售等關鍵產業正在持續推動對數據工程計劃的需求。此外，法規結構、高素質的勞動力以及創新文化也鞏固了北美的領先地位。所有這些因素共同造就了北美在數據工程領域中最大、最具影響力的市場地位。

複合年成長率最高的地區：

亞太地區預計將在預測期內實現最高的複合年成長率，這主要得益於強勁的數位轉型、日益增強的雲端整合以及人工智慧驅動系統的廣泛應用。中國、印度、日本和韓國等國家正持續增加對數據平台、自動化工具和智慧分析的投資。電子商務、數位支付、電信網路和智慧產業計劃的快速發展，推動了可擴展數據管道和即時處理的需求。該地區充滿活力的創新環境，以及政府主導的數位化舉措，進一步加速了相關技術的應用。因此，亞太地區預計將繼續保持資料工程生態系統成長最快的地位。

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目錄

第1章執行摘要

第2章 前言

  • 摘要
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 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
  • 競爭對手之間的競爭

5. 全球資料工程市場（按組件分類）

  • 工具和平台
    • ETL/ELT 工具
    • 資料編配與工作流程管理
    • 資料湖/湖屋平台
    • 資料倉儲
    • 串流和即時處理框架
    • 數據品質與管治工具
  • 服務
    • 諮詢顧問
    • 整合與實施
    • 託管服務

第6章 全球資料工程市場依部署類型分類

  • 本地部署
  • 雲端原生
  • 混合

7. 全球資料工程市場（按資料類型分類）

  • 結構化
  • 半結構化
  • 非結構化

第8章 全球資料工程市場（依最終用戶分類）

  • BFSI
  • 電訊和資訊技術
  • 醫療/製藥
  • 零售與電子商務
  • 製造業和能源
  • 政府/公共部門

9. 全球資料工程市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • Complere Infosystem
  • Manthan
  • Xebia
  • Quantiphi
  • Datamatics
  • Tredence
  • Bristlecone
  • Kanerika
  • V2Soft
  • Infostretch
  • Impetus Technologies
  • ThoughtWorks
  • Accenture
  • OptiSol Business Solutions
  • Fivetran
Product Code: SMRC32744

According to Stratistics MRC, the Global Data Engineering Market is accounted for $91.54 billion in 2025 and is expected to reach $249.18 billion by 2032 growing at a CAGR of 15.38% during the forecast period. Data engineering focuses on developing and managing the systems that collect, process, and organize data for organizational use. It includes building scalable pipelines, connecting multiple data sources, maintaining accuracy, and ensuring secure and efficient storage. Data engineers utilize cloud technologies, big data frameworks, and ETL methodologies to keep data accessible and trustworthy. Their efforts enable analysts, data scientists, and machine learning tools to work with clean, structured information. By enhancing data reliability, improving workflows, and ensuring strong governance, data engineering empowers companies to turn complex datasets into strategic insights that support faster decisions and long-term business growth.

According to the U.S. Bureau of Labor Statistics (BLS), employment of computer and information technology occupations-which includes data engineering roles-is projected to grow 15% from 2021 to 2031, much faster than the average for all occupations. This projection underscores the parent market's expansion.

Market Dynamics:

Driver:

Growing adoption of big data & real-time analytics

The surge in big data creation and the growing need for real-time insights significantly fuel the data engineering market. Enterprises today generate huge streams of complex data from connected devices, online interactions, and internal systems. To support real-time dashboards, predictive intelligence, and operational analytics, companies require strong pipelines, scalable data frameworks, and dependable processing platforms. Data engineering ensures fast, clean, and accessible information for various sectors including banking, e-commerce, and healthcare. As organizations strive for better responsiveness and data-driven decisions, demand rises for high-performing data ecosystems, enhanced analytics capabilities, and instant data availability, pushing continued market expansion.

Restraint:

High complexity of data integration & management

One of the major restraints slowing the data engineering market is the difficulty of integrating and managing highly diverse datasets. Companies today handle large volumes of unstructured, semi-structured, and structured information coming from legacy systems, cloud platforms, connected devices, and enterprise applications. Coordinating these data sources, ensuring uniformity, and preventing fragmentation requires advanced engineering skills and sophisticated tools. Many organizations face persistent data silos, poor interoperability, and quality issues that hinder efficient analytics. Real-time processing adds further complications, demanding continuous synchronization and reliability. These challenges increase operational costs, extend implementation timelines, and reduce the overall efficiency of data engineering projects.

Opportunity:

Rising demand for cloud-native & serverless data infrastructure

The growing preference for cloud-native and serverless architectures is creating major opportunities in the data engineering market. Companies are moving to platforms such as Azure Synapse, Google Cloud Dataflow, and AWS Lambda to achieve greater flexibility, faster scalability, and simplified operations. These systems provide automated scaling, real-time processing capabilities, and easy integration with analytics and BI tools. As digital transformation accelerates, enterprises increasingly require cloud-driven pipelines and modern data frameworks. The ability to handle large datasets with reduced maintenance effort makes serverless environments highly attractive. This shift toward scalable, cost-efficient cloud solutions significantly boosts opportunities for advanced data engineering services and tools.

Threat:

Rapid technological changes & tool obsolescence

The rapid pace of innovation in the data ecosystem creates a major threat, as existing tools and technologies can become obsolete quickly. New platforms, processing engines, and cloud-native architectures appear regularly, requiring organizations to update skills, rebuild pipelines, and modernize systems. Frequent transitions increase operational costs, reduce workflow stability, and cause integration challenges. Many companies lack the resources to adopt new technologies at the same speed they evolve. This constant pressure to upgrade may discourage long-term data engineering investments. As a result, uncertainty around future-proofing solutions can slow adoption rates and hinder the overall growth of data engineering initiatives.

Covid-19 Impact:

COVID-19 reshaped the data engineering market by driving faster adoption of digital technologies and expanding the need for reliable data infrastructure. With remote work becoming widespread, businesses invested more in cloud-based systems, real-time analytics, and automated data pipelines to maintain operations. Sectors like healthcare, online retail, and financial services required quicker insights to manage evolving customer demands and pandemic-related disruptions. Although economic uncertainty led some firms to reduce IT spending temporarily, the overall dependence on data-driven decision-making increased. The crisis highlighted the importance of scalable, resilient data systems, ultimately accelerating modernization efforts and strengthening long-term demand for advanced data engineering solutions.

The cloud-native segment is expected to be the largest during the forecast period

The cloud-native segment is expected to account for the largest market share during the forecast period. This is because companies increasingly rely on its on-demand scalability, cost-effective usage, and the ability to grow or shrink infrastructure automatically as needed. Cloud providers such as Microsoft Azure, Google Cloud, and AWS provide managed services, automated scaling, and pay-per-use billing, which minimize the need for large capital investments and operational upkeep. With cloud-native setups, businesses can quickly deploy data ingestion, streaming pipelines, and analytics workflows. This agility and resilience make cloud-native the preferred choice over on-site or hybrid models.

The retail & e-commerce segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the retail & e-commerce segment is predicted to witness the highest growth rate, fueled by rising online shopping, expanding digital interactions, and the need for deeper customer insights. Companies in this sector depend on real-time data processing, scalable pipelines, and advanced analytics to enhance marketing, inventory management, and personalized engagement. Increasing use of digital payments, automation platforms, and customer activity tracking boosts the demand for sophisticated data engineering systems. With growing adoption of recommendation tools, fraud analytics, and predictive demand models, retailers require robust data infrastructures to manage high data volumes, making this segment the fastest expanding in the market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, owing to its advanced IT infrastructure, widespread cloud integration, and strong commitment to analytics and AI. This region benefits from a rich blend of technology giants, major cloud platforms, and sophisticated data strategies within enterprises. Key industries-like banking, healthcare, and retail-fuel continuous demand for data engineering projects. Additionally, regulatory frameworks, a well-trained workforce, and a culture of innovation reinforce North America's leadership. All these elements combine to make North America the largest and most influential region in the data engineering landscape.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by strong digital transformation, rising cloud integration, and increasing use of AI-driven systems. Nations like China, India, Japan, and South Korea continue to expand investments in data platforms, automation tools, and intelligent analytics. Rapid expansion in e-commerce, digital payments, telecom networks, and smart industry projects boosts the need for scalable data pipelines and real-time processing. The region's thriving innovation landscape, combined with government-backed digital initiatives, accelerates adoption even further. As a result, APAC is set to remain the fastest-growing region in the data engineering ecosystem.

Key players in the market

Some of the key players in Data Engineering Market include Complere Infosystem, Manthan, Xebia, Quantiphi, Datamatics, Tredence, Bristlecone, Kanerika, V2Soft, Infostretch, Impetus Technologies, ThoughtWorks, Accenture, OptiSol Business Solutions and Fivetran.

Key Developments:

In September 2025, Quantiphi and an Amazon Web Services (AWS) Premier Tier Services Partner announced the expansion of its Strategic Collaboration Agreement (SCA) with AWS, strengthening the relationship and further enabling the companies to deliver enterprise-ready generative AI solutions at scale.

In August 2025, Accenture has agreed to acquire CyberCX, a leading privately-owned cybersecurity services provider serving both private and public sector organizations across Australia, New Zealand and internationally. The move represents Accenture's largest cybersecurity acquisition to date and will significantly bolster Accenture's cybersecurity services in Asia Pacific.

In February 2024, Xebia has launched a new office in Riyadh, the firm's second in the Middle East. Marking its entry in the Saudi market, Xebia's latest office is located at AstroLabs, a technology hub and ecosystem in Riyadh's booming Al Malqa district. The new office is the 28th worldwide for the Dutch-origin consulting group.

Components Covered:

  • Tools & Platforms
  • Services

Deployments Covered:

  • On-premises
  • Cloud-native
  • Hybrid

Data Types Covered:

  • Structured
  • Semi-structured
  • Unstructured

End Users Covered:

  • BFSI
  • Telecom & IT
  • Healthcare & Pharma
  • Retail & E-commerce
  • Manufacturing & Energy
  • Government & Public Sector

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Data Engineering Market, By Component

  • 5.1 Introduction
  • 5.2 Tools & Platforms
    • 5.2.1 ETL / ELT Tools
    • 5.2.2 Data Orchestration & Workflow Management
    • 5.2.3 Data Lakes & Lakehouse Platforms
    • 5.2.4 Data Warehouses
    • 5.2.5 Streaming & Real?time Processing Frameworks
    • 5.2.6 Data Quality & Governance Tools
  • 5.3 Services
    • 5.3.1 Consulting & Advisory
    • 5.3.2 Integration & Implementation
    • 5.3.3 Managed Services

6 Global Data Engineering Market, By Deployment

  • 6.1 Introduction
  • 6.2 On-premises
  • 6.3 Cloud-native
  • 6.4 Hybrid

7 Global Data Engineering Market, By Data Type

  • 7.1 Introduction
  • 7.2 Structured
  • 7.3 Semi-structured
  • 7.4 Unstructured

8 Global Data Engineering Market, By End User

  • 8.1 Introduction
  • 8.2 BFSI
  • 8.3 Telecom & IT
  • 8.4 Healthcare & Pharma
  • 8.5 Retail & E-commerce
  • 8.6 Manufacturing & Energy
  • 8.7 Government & Public Sector

9 Global Data Engineering Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Complere Infosystem
  • 11.2 Manthan
  • 11.3 Xebia
  • 11.4 Quantiphi
  • 11.5 Datamatics
  • 11.6 Tredence
  • 11.7 Bristlecone
  • 11.8 Kanerika
  • 11.9 V2Soft
  • 11.10 Infostretch
  • 11.11 Impetus Technologies
  • 11.12 ThoughtWorks
  • 11.13 Accenture
  • 11.14 OptiSol Business Solutions
  • 11.15 Fivetran

List of Tables

  • Table 1 Global Data Engineering Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Data Engineering Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Data Engineering Market Outlook, By Tools & Platforms (2024-2032) ($MN)
  • Table 4 Global Data Engineering Market Outlook, By ETL / ELT Tools (2024-2032) ($MN)
  • Table 5 Global Data Engineering Market Outlook, By Data Orchestration & Workflow Management (2024-2032) ($MN)
  • Table 6 Global Data Engineering Market Outlook, By Data Lakes & Lakehouse Platforms (2024-2032) ($MN)
  • Table 7 Global Data Engineering Market Outlook, By Data Warehouses (2024-2032) ($MN)
  • Table 8 Global Data Engineering Market Outlook, By Streaming & Real?time Processing Frameworks (2024-2032) ($MN)
  • Table 9 Global Data Engineering Market Outlook, By Data Quality & Governance Tools (2024-2032) ($MN)
  • Table 10 Global Data Engineering Market Outlook, By Services (2024-2032) ($MN)
  • Table 11 Global Data Engineering Market Outlook, By Consulting & Advisory (2024-2032) ($MN)
  • Table 12 Global Data Engineering Market Outlook, By Integration & Implementation (2024-2032) ($MN)
  • Table 13 Global Data Engineering Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 14 Global Data Engineering Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 15 Global Data Engineering Market Outlook, By On-premises (2024-2032) ($MN)
  • Table 16 Global Data Engineering Market Outlook, By Cloud-native (2024-2032) ($MN)
  • Table 17 Global Data Engineering Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 18 Global Data Engineering Market Outlook, By Data Type (2024-2032) ($MN)
  • Table 19 Global Data Engineering Market Outlook, By Structured (2024-2032) ($MN)
  • Table 20 Global Data Engineering Market Outlook, By Semi-structured (2024-2032) ($MN)
  • Table 21 Global Data Engineering Market Outlook, By Unstructured (2024-2032) ($MN)
  • Table 22 Global Data Engineering Market Outlook, By End User (2024-2032) ($MN)
  • Table 23 Global Data Engineering Market Outlook, By BFSI (2024-2032) ($MN)
  • Table 24 Global Data Engineering Market Outlook, By Telecom & IT (2024-2032) ($MN)
  • Table 25 Global Data Engineering Market Outlook, By Healthcare & Pharma (2024-2032) ($MN)
  • Table 26 Global Data Engineering Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 27 Global Data Engineering Market Outlook, By Manufacturing & Energy (2024-2032) ($MN)
  • Table 28 Global Data Engineering Market Outlook, By Government & Public Sector (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.