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市場調查報告書

商品編碼

1624506

資料湖市場：按組件、按部署模式、按組織規模、按最終用途行業、按地區，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個工作天內

價格

簡介目錄

資料湖市場評估，2024-2031

各行業產生的數據量不斷增加、對高級分析的需求以及對允許企業從各種數據格式中提取有意義的信息的經濟實惠的數據管理解決方案的需求，促使數據湖已成為主要推動因素。。Verified Market Research 分析師表示，預計到 2024 年，資料湖市場的估值將降至約 172.1 億美元以下，並在預測期內達到 790.9 億美元的估值。

由於需要管理和分析電子健康記錄 (EHR)、醫學影像和基因組定序產生的大量病患數據，醫療保健產業預計將為數據湖市場的成長做出重大貢獻。因此，從 2024 年到 2031 年，市場將以約 21.00% 的複合年增長率成長。

資料湖市場定義/概述

資料湖是一個集中式儲存庫，可以以自然格式儲存來自多個來源的大量原始數據，包括結構化、半結構化和非結構化數據，無需事先組織。這種靈活性使企業能夠捕獲和維護來自各種來源的數據，包括業務應用程式、物聯網設備和社交媒體，並根據需要執行高級分析和機器學習。資料湖可用於各種應用，包括大數據分析、即時資料處理和預測建模，這對於想要從海量資料集中獲取洞察以改善決策流程的企業至關重要。

推動資料湖市場的關鍵因素是什麼？

各行業數據生產的大幅增長正在推動對數據湖的需求。據International Data Corporation（IDC）稱，全球數據圈預計將從 2018 年的 33 澤字節增長到 2025 年的 175 澤字節。資料量驚人地成長了 431%，需要可擴展且靈活的儲存解決方案（例如資料湖）來管理這些爆炸性資料並從中提取價值。

大數據分析和人工智慧/機器學習 (AI/ML) 技術的日益使用正在推動資料湖市場的發展。NewVantage Partners的研究顯示，91.9%的知名企業計劃在2021年增加對大數據和人工智慧的投資。資料湖提供了儲存和處理高級分析和 AI/ML 應用程式所需的大量不同資料所需的基礎設施。

此外，向雲端運算的轉變正在加速基於雲端的資料湖的擴散。Gartner 預測，到 2025 年，超過 95% 的新數位工作負載將在雲端原生平台上實施，高於 2021 年的 30%。這一趨勢正在推動企業利用基於雲端的資料湖，因為它們具有可擴展性、成本效益以及支援分散式資料處理和分析的能力。

資料治理課題如何阻礙資料湖市場？

資料治理的複雜性是資料湖市場成長的主要障礙。隨著組織從各種來源收集大量原始數據，確保資料品質、安全性和合規性變得更加複雜。如果沒有強大的治理框架，公司就會面臨資料完整性和監管合規性課題的風險，導致分析不準確和決策失誤。這種複雜性需要對治理流程和技術進行大量投資，這阻礙了一些公司使用資料湖。

此外，維護資料湖內資料品質的難度也是關鍵阻礙因素。數據通常是未經加工或驗證就被直接吸收的，這可能會導致錯誤和不準確。缺乏品質控制會對下游分析和決策過程產生負面影響，導致錯誤的見解。為了防止此類風險，組織必須採用嚴格的資料品質標準。

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