巨量资料和数据工程服务市场－全球产业规模、份额、趋势、机会及预测（按服务类型、组织规模、业务职能、最终用户、地区和竞争格局划分，2021-2031年）

全球巨量资料和数据工程服务市场预计将从 2025 年的 719.8 亿美元成长到 2031 年的 1,371.3 亿美元，复合年增长率为 11.34%。

这些服务包括架构设计、基础设施开发和管道管理，旨在将海量原始资料集转换为适合分析的结构化格式。推动该市场发展的关键因素是数位生态系统中非结构化资料的快速积累，以及企业迫切需要利用即时智慧来获得竞争优势。根据NASSCOM 2024年报告，全球对人工智慧和数据分析的投资预计将达到约830亿美元，自2019年以来保持24%的复合年增长率，这表明各组织正在大力投资这些基础技术。

然而，由于资料隐私和主权相关的法规环境复杂，该领域的成长面临许多重大障碍。遵守不同司法管辖区的法规存在困难，这导致跨境资料管治出现摩擦，并可能阻碍工程专案的扩充性。这种合规负担，加上与整合旧有系统相关的技术挑战，持续阻碍全球资料策略的顺利实施。

市场驱动因素

随着企业从实验性试点阶段转向全面生产环境，人工智慧 (AI) 和机器学习 (ML) 技术的应用正在从根本上重塑市场格局。这项转变催生了对高级资料工程服务的需求，以建立弹性管道、管理特征储存并确保复杂演算法所需的高品质资料可用性。随着企业将这些技术投入实际应用，对 MLOps 和可扩展基础设施的需求也呈现爆炸性成长，以支援智慧应用的生命週期。 Databricks 于 2025 年 6 月发布的报告《数据 + AI 现状》指出，生产环境中註册的 AI 模型数量同比增长 1018%，凸显了行业正朝着部署实用 AI 资产的大规模转变，以及由此带来的对工程支持的巨大需求。

随着企业为提升敏捷性和扩充性而对其传统基础设施进行现代化改造，基于云端的资料架构的加速普及也推动了市场扩张。企业正积极将工作负载迁移到公共云端和混合云端环境，以充分利用弹性运算能力和整合分析平台。 Flexera 于 2025 年 3 月发布的《2025 年云端状态报告》显示，78% 的企业将迁移到云端的工作负载量列为关键指标，较前一年的 36% 显着成长。然而，这种快速的去中心化往往会导致复杂的碎片化， 销售团队指出，到 2025 年，“90% 的 IT 领导者将把资料孤岛视为一项重大的业务挑战”，这凸显了工程服务在整合不同系统方面的迫切需求。

市场挑战

复杂的资料隐私和主权监管环境对全球巨量资料和资料工程服务市场的扩张构成重大障碍。由于各国实施的资料在地化义务和隐私权法规各不相同，企业在跨国资料传输方面面临许多限制。这种法律碎片化迫使企业放弃整合高效的全球资料架构，转而采用孤立的、区域特定的基础设施来确保资料居住。因此，工程团队必须管理分散的资料管道，这不仅显着增加了营运复杂性，也降低了从大型集中式资料集中获得的分析价值。

这种合规负担要求企业将大量财务和技术资源投入法律管治和风险缓解方面，而非工程创新和服务扩充性。不断变化的司法管辖区法律所带来的固有不确定性，创造了一种谨慎的营运环境，并常常导致企业推迟大规模数据倡议。 2024年，国际隐私专业人员协会（IAPP）报告称，仅有20%的隐私专业人员对其所在机构持续遵守现行监管标准的能力充满信心。这种普遍存在的信心不足直接阻碍了决策，并减缓了全球数据工程服务的普及，因为企业优先考虑的是避免诉讼和处罚，而非积极拓展市场。

市场趋势

资料湖与资料仓储的融合－湖屋模式的出现，正从根本上重塑市场格局。它将资料湖的低成本储存与资料仓储的高效能管理能力结合。这种统一的架构消除了资料孤岛造成的营运效率低下问题，使企业能够利用 Apache Iceberg 等开放式格式，并在单一资料副本上运行各种分析工作负载。因此，企业正在摒弃复杂且脆弱的 ETL 流程，转而采用直接资料访问，从而显着加强资料管治并降低基础设施开销。根据 Dremio 于 2025 年 1 月发布的报告《人工智慧时代的资料湖屋现状》，67% 的企业计划在未来三年内将大部分分析工作部署在资料湖屋上，这凸显了产业向这种整合框架的快速转型。

将生成式人工智慧整合到增强型资料工程中，正成为解决日益扩大的技能差距和不断增加的资料管道复杂性的关键趋势。透过将大规模语言模型直接整合到开发工作流程中，工程团队可以自动化程式码产生、模式映射和旧有系统文件等劳力密集任务。这种从手动编码到架构监督的重心转移，显着加快了可靠资料产品的交付速度，同时最大限度地减少了人为错误带来的技术债。 Ascend.io 于 2025 年 9 月进行的年度脉搏调查显示，83% 的资料工程师表示，人工智慧和新工具提高了他们的工作效率，凸显了智慧自动化对服务生命週期的变革性影响。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章 全球巨量资料与资料工程服务市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 依服务类型（资料建模、资料整合、分析、资料品质）
  • 依公司规模（中小企业、大型企业）
  • 依业务职能（财务、行销/销售、人力资源、其他）
  • 按最终用户划分（媒体与电信、银行、金融服务和保险、製造业、政府、其他）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美巨量资料与数据工程服务市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 我们
  • 加拿大
  • 墨西哥

7. 欧洲巨量资料与数据工程服务市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

8. 亚太地区巨量资料与数据工程服务市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

9. 中东和非洲巨量资料及数据工程服务市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美洲巨量资料与数据工程服务市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章：全球巨量资料与资料工程服务市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• Accenture PLC
• Genpact Inc.
• Cognizant Technology Solutions Corporation
• Infosys Limited
• Capgemini SE
• NTT Data Inc.
• Mphasis Limited
• L&T Technology Services
• Hexaware Technologies Inc.
• KPMG LLP

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Big Data and Data Engineering Services Market is projected to expand from USD 71.98 Billion in 2025 to USD 137.13 Billion by 2031, registering a CAGR of 11.34%. These services involve the architectural design, infrastructure development, and pipeline management necessary to convert massive raw datasets into structured formats suitable for analysis. The market is primarily driven by the rapid accumulation of unstructured data across digital ecosystems and the urgent need for enterprises to leverage real-time intelligence for competitive gains. NASSCOM reported in 2024 that global investments in AI and data analytics reached approximately USD 83 billion, following a 24% compound annual growth rate since 2019, illustrating the significant financial commitment organizations are allocating to these foundational technologies.

However, the growth of this sector faces substantial obstacles due to the complicated regulatory environment regarding data privacy and sovereignty. The difficulty of adhering to diverse jurisdictional laws creates friction in cross-border data governance, which can hinder the scalability of engineering initiatives. This compliance burden, combined with the technical challenges of integrating legacy systems, continues to complicate the seamless implementation of global data strategies.

Market Driver

The incorporation of Artificial Intelligence and Machine Learning technologies is fundamentally reshaping the market as enterprises move from experimental pilots to full-scale production environments. This transition creates a need for advanced data engineering services to build resilient pipelines, manage feature stores, and ensure high-quality data availability for complex algorithms. As organizations operationalize these technologies, the demand for MLOps and scalable infrastructure to support the lifecycle of intelligent applications has surged. A June 2025 report by Databricks, titled 'State of Data + AI', noted a 1,018% year-over-year increase in AI models registered for production, emphasizing the massive industrial pivot toward deploying functional AI assets and the resulting necessity for engineering support.

Market expansion is further fueled by the accelerated adoption of cloud-based data architectures, as businesses modernize legacy infrastructure to achieve greater agility and scalability. Companies are aggressively migrating workloads to public and hybrid cloud environments to utilize elastic computing power and unified analytics platforms. In the '2025 State of the Cloud Report' by Flexera released in March 2025, 78% of organizations identified the volume of workloads migrated to the cloud as a key metric, a significant rise from 36% the previous year. However, this rapid decentralization often leads to complex fragmentation; Salesforce noted in 2025 that 90% of IT leaders find data silos to be a significant business challenge, underscoring the critical need for engineering services to unify disparate systems.

Market Challenge

The complex regulatory landscape regarding data privacy and sovereignty acts as a major barrier to the expansion of the Global Big Data and Data Engineering Services Market. As nations enforce divergent data localization mandates and privacy statutes, organizations encounter severe restrictions on cross-border data flows. This legal fragmentation forces enterprises to abandon unified, efficient global data architectures in favor of segregated, region-specific infrastructures to ensure data residency. Consequently, engineering teams must manage disjointed pipelines, which drastically increases operational complexity and diminishes the analytical value derived from centralized, massive datasets.

This compliance burden necessitates the diversion of critical financial and technical resources toward legal governance and risk mitigation rather than engineering innovation or service scalability. The uncertainty inherent in navigating these shifting jurisdictional laws creates a cautious operational environment, often causing firms to delay large-scale data initiatives. In 2024, the International Association of Privacy Professionals reported that only 20% of privacy professionals expressed complete confidence in their organization's ability to maintain compliance with current regulatory standards. This pervasive lack of certainty directly hampers decision-making and stalls the adoption of global data engineering services, as organizations prioritize avoiding litigation and penalties over aggressive market expansion.

Market Trends

The convergence of Data Lakes and Warehouses into Lakehouse Models is fundamentally restructuring the market by merging the low-cost storage of data lakes with the high-performance management capabilities of data warehouses. This architectural unification resolves the operational inefficiencies caused by fragmented data silos, allowing enterprises to run diverse analytical workloads on a single copy of data using open formats like Apache Iceberg. Consequently, organizations are moving away from complex, brittle ETL processes in favor of direct data access, which significantly enhances governance and reduces infrastructure overhead. A January 2025 report by Dremio, 'State of the Data Lakehouse in the AI Era', indicates that 67% of organizations plan to run the majority of their analytics on data lakehouses within the next three years, highlighting the rapid industrial pivot toward this consolidated framework.

Integration of Generative AI for Augmented Data Engineering is emerging as a critical trend to address the widening skills gap and the increasing complexity of data pipelines. By embedding large language models directly into development workflows, engineering teams are automating labor-intensive tasks such as code generation, schema mapping, and legacy system documentation. This shift moves the focus from manual coding to architectural oversight, significantly accelerating the delivery of reliable data products while minimizing technical debt associated with human error. Ascend.io's 'Annual Pulse Survey' in September 2025 revealed that 83% of data engineers stated that AI and new tools have increased their productivity, highlighting the transformative impact of intelligent automation on the services lifecycle.

Key Market Players

• Accenture PLC
• Genpact Inc.
• Cognizant Technology Solutions Corporation
• Infosys Limited
• Capgemini SE
• NTT Data Inc.
• Mphasis Limited
• L&T Technology Services
• Hexaware Technologies Inc.
• KPMG LLP

Report Scope

In this report, the Global Big Data and Data Engineering Services Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Big Data and Data Engineering Services Market, By Service Type

• Data Modelling
• Data Integration
• Analytics
• Data Quality

Big Data and Data Engineering Services Market, By Organization Size

• Small & Medium-Sized Enterprises
• Large Enterprises

Big Data and Data Engineering Services Market, By Business Function

• Finance
• Marketing & Sales
• HR
• Others

Big Data and Data Engineering Services Market, By End User

• Media & Telecom
• BFSI
• Manufacturing
• Government
• Others

Big Data and Data Engineering Services Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Big Data and Data Engineering Services Market.

Available Customizations:

Global Big Data and Data Engineering Services Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Big Data and Data Engineering Services Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Service Type (Data Modelling, Data Integration, Analytics, Data Quality)
  • 5.2.2. By Organization Size (Small & Medium-Sized Enterprises, Large Enterprises)
  • 5.2.3. By Business Function (Finance, Marketing & Sales, HR, Others)
  • 5.2.4. By End User (Media & Telecom, BFSI, Manufacturing, Government, Others)
  • 5.2.5. By Region
  • 5.2.6. By Company (2025)
• 5.3. Market Map

6. North America Big Data and Data Engineering Services Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Service Type
  • 6.2.2. By Organization Size
  • 6.2.3. By Business Function
  • 6.2.4. By End User
  • 6.2.5. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Big Data and Data Engineering Services Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Service Type
      • 6.3.1.2.2. By Organization Size
      • 6.3.1.2.3. By Business Function
      • 6.3.1.2.4. By End User
  • 6.3.2. Canada Big Data and Data Engineering Services Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Service Type
      • 6.3.2.2.2. By Organization Size
      • 6.3.2.2.3. By Business Function
      • 6.3.2.2.4. By End User
  • 6.3.3. Mexico Big Data and Data Engineering Services Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Service Type
      • 6.3.3.2.2. By Organization Size
      • 6.3.3.2.3. By Business Function
      • 6.3.3.2.4. By End User

7. Europe Big Data and Data Engineering Services Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Service Type
  • 7.2.2. By Organization Size
  • 7.2.3. By Business Function
  • 7.2.4. By End User
  • 7.2.5. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Big Data and Data Engineering Services Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Service Type
      • 7.3.1.2.2. By Organization Size
      • 7.3.1.2.3. By Business Function
      • 7.3.1.2.4. By End User
  • 7.3.2. France Big Data and Data Engineering Services Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Service Type
      • 7.3.2.2.2. By Organization Size
      • 7.3.2.2.3. By Business Function
      • 7.3.2.2.4. By End User
  • 7.3.3. United Kingdom Big Data and Data Engineering Services Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Service Type
      • 7.3.3.2.2. By Organization Size
      • 7.3.3.2.3. By Business Function
      • 7.3.3.2.4. By End User
  • 7.3.4. Italy Big Data and Data Engineering Services Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Service Type
      • 7.3.4.2.2. By Organization Size
      • 7.3.4.2.3. By Business Function
      • 7.3.4.2.4. By End User
  • 7.3.5. Spain Big Data and Data Engineering Services Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Service Type
      • 7.3.5.2.2. By Organization Size
      • 7.3.5.2.3. By Business Function
      • 7.3.5.2.4. By End User

8. Asia Pacific Big Data and Data Engineering Services Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Service Type
  • 8.2.2. By Organization Size
  • 8.2.3. By Business Function
  • 8.2.4. By End User
  • 8.2.5. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Big Data and Data Engineering Services Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Service Type
      • 8.3.1.2.2. By Organization Size
      • 8.3.1.2.3. By Business Function
      • 8.3.1.2.4. By End User
  • 8.3.2. India Big Data and Data Engineering Services Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Service Type
      • 8.3.2.2.2. By Organization Size
      • 8.3.2.2.3. By Business Function
      • 8.3.2.2.4. By End User
  • 8.3.3. Japan Big Data and Data Engineering Services Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Service Type
      • 8.3.3.2.2. By Organization Size
      • 8.3.3.2.3. By Business Function
      • 8.3.3.2.4. By End User
  • 8.3.4. South Korea Big Data and Data Engineering Services Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Service Type
      • 8.3.4.2.2. By Organization Size
      • 8.3.4.2.3. By Business Function
      • 8.3.4.2.4. By End User
  • 8.3.5. Australia Big Data and Data Engineering Services Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Service Type
      • 8.3.5.2.2. By Organization Size
      • 8.3.5.2.3. By Business Function
      • 8.3.5.2.4. By End User

9. Middle East & Africa Big Data and Data Engineering Services Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Service Type
  • 9.2.2. By Organization Size
  • 9.2.3. By Business Function
  • 9.2.4. By End User
  • 9.2.5. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Big Data and Data Engineering Services Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Service Type
      • 9.3.1.2.2. By Organization Size
      • 9.3.1.2.3. By Business Function
      • 9.3.1.2.4. By End User
  • 9.3.2. UAE Big Data and Data Engineering Services Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Service Type
      • 9.3.2.2.2. By Organization Size
      • 9.3.2.2.3. By Business Function
      • 9.3.2.2.4. By End User
  • 9.3.3. South Africa Big Data and Data Engineering Services Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Service Type
      • 9.3.3.2.2. By Organization Size
      • 9.3.3.2.3. By Business Function
      • 9.3.3.2.4. By End User

10. South America Big Data and Data Engineering Services Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Service Type
  • 10.2.2. By Organization Size
  • 10.2.3. By Business Function
  • 10.2.4. By End User
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Big Data and Data Engineering Services Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Service Type
      • 10.3.1.2.2. By Organization Size
      • 10.3.1.2.3. By Business Function
      • 10.3.1.2.4. By End User
  • 10.3.2. Colombia Big Data and Data Engineering Services Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Service Type
      • 10.3.2.2.2. By Organization Size
      • 10.3.2.2.3. By Business Function
      • 10.3.2.2.4. By End User
  • 10.3.3. Argentina Big Data and Data Engineering Services Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Service Type
      • 10.3.3.2.2. By Organization Size
      • 10.3.3.2.3. By Business Function
      • 10.3.3.2.4. By End User

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Big Data and Data Engineering Services Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. Accenture PLC
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Genpact Inc.
• 15.3. Cognizant Technology Solutions Corporation
• 15.4. Infosys Limited
• 15.5. Capgemini SE
• 15.6. NTT Data Inc.
• 15.7. Mphasis Limited
• 15.8. L&T Technology Services
• 15.9. Hexaware Technologies Inc.
• 15.10. KPMG LLP

16. Strategic Recommendations

17. About Us & Disclaimer