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市场调查报告书

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1624506

资料湖市场：按组件、按部署模式、按组织规模、按最终用途行业、按地区，2024-2031 年

Data Lakes Market By Component, Deployment Mode, Organization Size, Business Function, End-use Industry, & Region for 2024-2031

出版日期: 2024年09月21日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3个工作天内

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简介目录

资料湖市场评估，2024-2031

各行业产生的数据量不断增加、对高级分析的需求以及对允许企业从各种数据格式中提取有意义的信息的经济实惠的数据管理解决方案的需求，促使数据湖已成为主要推动因素。。Verified Market Research 分析师表示，预计到 2024 年，资料湖市场的估值将降至约 172.1 亿美元以下，并在预测期内达到 790.9 亿美元的估值。

由于需要管理和分析电子健康记录 (EHR)、医学影像和基因组定序产生的大量病患数据，医疗保健产业预计将为数据湖市场的成长做出重大贡献。因此，从 2024 年到 2031 年，市场将以约 21.00% 的复合年增长率成长。

资料湖市场定义/概述

资料湖是一个集中式储存库，可以以自然格式储存来自多个来源的大量原始数据，包括结构化、半结构化和非结构化数据，无需事先组织。这种灵活性使企业能够捕获和维护来自各种来源的数据，包括业务应用程式、物联网设备和社交媒体，并根据需要执行高级分析和机器学习。资料湖可用于各种应用，包括大数据分析、即时资料处理和预测建模，这对于想要从海量资料集中获取洞察以改善决策流程的企业至关重要。

推动资料湖市场的关键因素是什么？

各行业数据生产的大幅增长正在推动对数据湖的需求。据International Data Corporation（IDC）称，全球数据圈预计将从 2018 年的 33 泽字节增长到 2025 年的 175 泽字节。资料量惊人地成长了 431%，需要可扩展且灵活的储存解决方案（例如资料湖）来管理这些爆炸性资料并从中提取价值。

大数据分析和人工智慧/机器学习 (AI/ML) 技术的日益使用正在推动资料湖市场的发展。NewVantage Partners的研究显示，91.9%的知名企业计划在2021年增加对大数据和人工智慧的投资。资料湖提供了储存和处理高级分析和 AI/ML 应用程式所需的大量不同资料所需的基础设施。

此外，向云端运算的转变正在加速基于云端的资料湖的扩散。Gartner 预测，到 2025 年，超过 95% 的新数位工作负载将在云端原生平台上实施，高于 2021 年的 30%。这一趋势正在推动企业利用基于云端的资料湖，因为它们具有可扩展性、成本效益以及支援分散式资料处理和分析的能力。

资料治理课题如何阻碍资料湖市场？

资料治理的复杂性是资料湖市场成长的主要障碍。随着组织从各种来源收集大量原始数据，确保资料品质、安全性和合规性变得更加复杂。如果没有强大的治理框架，公司就会面临资料完整性和监管合规性课题的风险，导致分析不准确和决策失误。这种复杂性需要对治理流程和技术进行大量投资，这阻碍了一些公司使用资料湖。

此外，维护资料湖内资料品质的难度也是关键阻碍因素。数据通常是未经加工或验证就被直接吸收的，这可能会导致错误和不准确。缺乏品质控制会对下游分析和决策过程产生负面影响，导致错误的见解。为了防止此类风险，组织必须采用严格的资料品质标准。

The growing amount of data produced by various industries, the need for sophisticated analytics, and the demand for affordable data management solutions that let businesses extract meaningful information from various data formats are the main factors propelling the data lake market. According to the analyst from Verified Market Research, the data lakes market is estimated to reach a valuation of USD 79.09 Billion over the forecast subjugating around USD 17.21 Billion valued in 2024.

The healthcare industry is expected to contribute substantially to the growth of the data lake market, owing to the requirement to manage and analyze massive amounts of patient data generated by electronic health records (EHRs), medical imaging, and genomic sequencing. It enables the market to grow at a CAGR of about 21.00% from 2024 to 2031.

Data Lakes Market: Definition/ Overview

A data lake is a centralized repository that can store large amounts of raw data in its natural format, including structured, semi-structured, and unstructured data from many sources without the need for prior organizing. This flexibility enables businesses to consume and maintain data from a variety of sources, including business apps, IoT devices, and social media, allowing them to execute advanced analytics and machine learning as needed. Data lakes are used in a variety of applications, including big data analytics, real-time data processing, and predictive modeling, making them critical for companies looking to get insights from massive datasets and improve decision-making processes.

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What are the Primary Factors Propelling the Data Lakes Market?

The substantial rise in the production of data across industries has fueled the demand for data lakes. According to the International Data Corporation (IDC), the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This staggering 431% rise in data volume needs scalable and flexible storage solutions such as data lakes to manage and extract value from this data explosion.

The increased use of big data analytics and artificial intelligence/machine learning (AI/ML) technologies is driving the data lake market. According to NewVantage Partners' survey, 91.9% of prominent organizations plan to increase their investments in big data and AI initiatives by 2021. Data lakes provide the necessary infrastructure to store and handle enormous volumes of heterogeneous data needed for advanced analytics and AI/ML applications.

Furthermore, the shift to cloud computing is accelerating the popularity of cloud-based data lakes. Gartner anticipates that by 2025, more than 95% of new digital workloads will be implemented on cloud-native platforms, up from 30% in 2021. This trend is encouraging enterprises to use cloud-based data lakes because of their scalability, cost-effectiveness, and capacity to support distributed data processing and analytics.

How do the Data Governance Challenges Hamper the Data Lakes Market?

The complexity of data governance is a major barrier to growth in the data lakes market. As organizations collect massive amounts of raw data from a variety of sources, ensuring data quality, security, and compliance becomes more complex. Without a strong governance framework, firms risk experiencing challenges with data integrity and regulatory compliance, resulting in incorrect analytics and poor decision-making. This complexity needs significant investment in governance processes and technologies, discouraging some companies from using data lakes.

Furthermore, the difficulty of maintaining data quality within data lakes is another important constraint. Because data is frequently absorbed in its raw form without previous cleansing or validation, errors and inaccuracies may occur. This absence of quality control has an unfavorable effect on downstream analytics and decision-making processes, resulting in incorrect insights. To prevent these risks, organizations must employ strong data quality standards that involve significant resources and expertise.

Category-Wise Acumens

How does the Growing Demand for Advanced Analytics Drive Data Lakes Solutions in the Market?

The solution segment is estimated to dominate the data lakes market during the forecast period. Organizations are increasingly looking for advanced analytics skills to extract useful insights from large amounts of data. The solutions segment, which includes data discovery, integration, and analytics tools, allows businesses to easily process and analyze raw data. The demand for sophisticated analytical tools is accelerating the expansion of the solutions segment significantly.

The requirement for efficient data integration and management solutions grows as organizations amass heterogeneous datasets from several sources. The solutions segment meets this need by offering tools that assist enterprises in streamlining data ingestion, storage, and processing. This capability not only improves operational efficiency but also allows for superior decision-making processes, boosting the solutions segment's market dominance.

Furthermore, data lakes provide exceptional scalability and flexibility, enabling businesses to store and manage massive amounts of organized and unstructured data. The solutions segment capitalizes on this advantage by offering scalable infrastructures that can adapt to an organization's changing data requirements. This adaptability is particularly appealing to businesses trying to future-proof their data initiatives, reinforcing the solutions segment's market leadership.

How does the Data-Driven Decision Making in BFSI Sector Drive the Market?

The banking, financial services, & insurance (BFSI) segment is estimated to dominate the market during the forecast period. The BFSI industry relies extensively on data for decision-making processes such as risk assessment, fraud detection, and consumer insights. Data lakes enable financial institutions to store massive amounts of structured and unstructured data, allowing for advanced analytics and machine learning applications that boost operational efficiency and service delivery.

The BFSI industry is subject to severe regulations governing data management and reporting. Data lakes provide a consolidated repository that makes compliance easier by allowing firms to keep detailed records of transactions and consumer interactions. This feature promotes good data governance and enables financial institutions to respond quickly to regulatory audits and inquiries.

Furthermore, in an increasingly competitive landscape, BFSI firms are focused on individualized customer experiences to retain customers and attract new ones. Data lakes enable these firms to gather and analyze a variety of customer data sources, allowing them to personalize products, services, and marketing campaigns to individual tastes. This focused strategy improves consumer satisfaction and loyalty, hence driving segment growth.

Country/Region-wise Acumens

What are the Key Drivers Bolstering the Demand for Data Lakes in North America?

North America is estimated to dominate the data lakes market during the forecast period. North America leads in technological adoption and digital transformation activities, which fuels the demand for data lakes. According to IDC, US businesses are estimated to invest USD 1.8 Trillion in digital transformation activities by 2025. This large investment demonstrates the region's commitment to using advanced data management technologies, such as data lakes, to support digital objectives and preserve a competitive advantage.

Furthermore, the rapid proliferation of Internet of Things (IoT) devices in North America is generating large volumes of data, increasing the demand for data lakes. IoT Analytics predicts that North America will have 5.4 billion IoT connections by 2025, indicating a 14% compound annual growth rate (CAGR). This boom of connected devices generates massive volumes of heterogeneous data, necessitating scalable storage and processing solutions, establishing data lakes as a critical component of the region's IoT ecosystem.

How Does the Explosive Growth in Mobile & Internet Users Driving the Growth of the Market in Asia Pacific?

The Asia Pacific region is estimated to exhibit the highest growth within the market during the forecast period. The Asia Pacific region is experiencing a spike in mobile and internet adoption, resulting in massive amounts of data that must be efficiently stored and analyzed. According to GSMA Intelligence, the Asia Pacific region's mobile internet user base will grow from 2.7 billion in 2021 to 3.1 billion by 2025. This rapid increase in connected people generates massive amounts of heterogeneous data, making data lakes critical for organizations to acquire, store, and derive insights from this wealth of information.

Furthermore, many Asian countries are implementing national initiatives to encourage big data and artificial intelligence, resulting in increased demand for data lakes. China's New Generation Artificial Intelligence Development Plan intends to make the country a world leader in AI by 2030, with an estimated core AI industry gross output of over 1 trillion yuan (~ USD 150 Billion). Similarly, India's National Strategy for Artificial Intelligence predicts that AI will bring $957 billion to the Indian economy by 2035. These government-supported initiatives are hastening the adoption of data lakes as the basic infrastructure for big data and AI projects throughout the region.

Competitive Landscape

The competitive landscape of the data lakes market is fragmented, with multiple competitors fighting for market share in various regions and sectors. Organizations in a variety of industries, including retail, healthcare, and manufacturing, are increasingly using data lake solutions to leverage massive amounts of structured and unstructured data for better decision-making and operational efficiencies.

Some of the prominent players operating in the data lakes market include:

Microsoft

IBM

Oracle

Cloudera

Informatica

Teradata

Zaloni

Snowflake

Dremio

HPE

SAS Institute

Google

Alibaba Cloud

Tencent Cloud

Baidu

VMware

SAP

Dell Technologies

Huawei

Latest Developments

In December 2022, Atos announced the development of a new solution in collaboration with AWS that allows clients to expedite and properly monitor company key performance indicators (KPIs) by offering simple access to non-SAP and SAP data silos. 'Atos' AWS Data Lake Accelerator for SAP" is an innovative solution that delivers enterprise-wide and self-service reporting for significant insights into daily changes that rapidly impact decisions to drive the bottom line.

In November 2022, Amazon Web Services (AWS) announced the launch of Amazon Security Lake. This new cybersecurity solution automatically centralizes safety data from on-premises and cloud sources into a purpose-built data lake in a user's AWS account.

In April 2022, Google introduced the preview launch of Big Lake. This new data lake storage system allows organizations to analyze data in their data lakes and warehouses at its Cloud Data Summit.

1 INTRODUCTION OF GLOBAL DATA LAKES MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL DATA LAKES MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis

5 GLOBAL DATA LAKES MARKET, BY COMPONENT

5.1 Overview
5.2 Solution
5.3 Services

6 GLOBAL DATA LAKES MARKET, BY DEPLOYMENT MODE

6.1 Overview
6.2 On-Premise
6.3 Cloud-based

7 GLOBAL DATA LAKES MARKET, BY ORGANIZATION SIZE

7.1 Overview
7.2 Large Enterprises
7.3 Small & Medium Enterprises (SMEs)

8 GLOBAL DATA LAKES MARKET, BY END-USER

8.1 Overview
8.2 Telecommunication & IT
8.3 Banking, Financial Services, and Insurance (BFSI)
8.4 Retail
8.5 Healthcare
8.6 Media
8.7 Government
8.8 Hospitality
8.9 Education
8.10 Others

9 GLOBAL DATA LAKES MARKET, BY GEOGRAPHY

9.1 Overview
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
- 9.2.3 Mexico
9.3 Europe
- 9.3.1 Germany
- 9.3.2 U.K.
- 9.3.3 France
- 9.3.4 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 Japan
- 9.4.3 India
- 9.4.4 Rest of Asia Pacific
9.5 Rest of the World
- 9.5.1 Latin America
- 9.5.2 Middle East & Africa

10 GLOBAL DATA LAKES MARKET COMPETITIVE LANDSCAPE

10.1 Overview
10.2 Company Market Ranking
10.3 Key Development Strategies

11 COMPANY PROFILES

11.1 Microsoft
- 11.1.1 Overview
- 11.1.2 Financial Performance
- 11.1.3 Product Outlook
- 11.1.4 Key Developments
11.2 IBM
- 11.2.1 Overview
- 11.2.2 Financial Performance
- 11.2.3 Product Outlook
- 11.2.4 Key Developments
11.3 Oracle
- 11.3.1 Overview
- 11.3.2 Financial Performance
- 11.3.3 Product Outlook
- 11.3.4 Key Developments
11.4 Cloudera
- 11.4.1 Overview
- 11.4.2 Financial Performance
- 11.4.3 Product Outlook
- 11.4.4 Key Developments
11.5 Informatica
- 11.5.1 Overview
- 11.5.2 Financial Performance
- 11.5.3 Product Outlook
- 11.5.4 Key Developments
11.6 Teradata
- 11.6.1 Overview
- 11.6.2 Financial Performance
- 11.6.3 Product Outlook
- 11.6.4 Key Developments
11.7 Zaloni
- 11.7.1 Overview
- 11.7.2 Financial Performance
- 11.7.3 Product Outlook
- 11.7.4 Key Developments
11.8 Snowflake
- 11.8.1 Overview
- 11.8.2 Financial Performance
- 11.8.3 Product Outlook
- 11.8.4 Key Developments
11.9 Dremio
- 11.9.1 Overview
- 11.9.2 Financial Performance
- 11.9.3 Product Outlook
- 11.9.4 Key Developments
11.10 HPE
- 11.10.1 Overview
- 11.10.2 Financial Performance
- 11.10.3 Product Outlook
- 11.10.4 Key Developments