세계의 데이터 엔지니어링 시장 예측 : 구성요소별, 전개 형태별, 데이터 유형별, 최종 사용자별, 지역별 분석(-2032년)

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