 | • レポートコード：MRC2203A413 • 出版社/出版日：Mordor Intelligence / 2022年1月 • レポート形態：英文、PDF、120ページ • 納品方法：Eメール（受注後2-3営業日） • 産業分類：IT |
| Single User | ￥703,000 (USD4,750) | ▷ お問い合わせ |
| Site Licence | ￥962,000 (USD6,500) | ▷ お問い合わせ |
| Corporate License | ￥1,295,000 (USD8,750) | ▷ お問い合わせ |
• お支払方法：銀行振込（納品後、ご請求書送付）
レポート概要
| Mordor Intelligence社の本市場調査レポートでは、世界のデータレイク市場について調査・分析し、イントロダクション、調査手法、エグゼクティブサマリー、市場動向、提供別（ソリューション、サービス）分析、展開別（クラウド、オンプレミス）分析、産業別（IT・通信、金融、医療、小売、製造）分析、地域別（北米、ヨーロッパ、アジア太平洋、中南米、中東・アフリカ）分析、競争状況、投資分析、市場の将来など、以下の構成でまとめました。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場動向 ・世界のデータレイク市場規模：提供別（ソリューション、サービス） ・世界のデータレイク市場規模：展開別（クラウド、オンプレミス） ・世界のデータレイク市場規模：産業別（IT・通信、金融、医療、小売、製造） ・世界のデータレイク市場規模：地域別（北米、ヨーロッパ、アジア太平洋、中南米、中東・アフリカ） ・競争状況（Microsoft Corporation、Amazon.com Inc.、Capgemini SE、…） ・投資分析 ・市場の将来 |
The Data Lakes Market was valued at USD 3.74 billion in 2020 and is expected to reach USD 17.60 billion by 2026, at a CAGR of 29.9% over the forecast period 2021 – 2026. Data lakes have become an economical option for many companies rather than an option for data warehousing. Data warehousing involves additional computing of data before entering the warehouse, unlike data lakes. The cost of maintaining a data lake is lower than a data lake owing to the number of operations and space involved in building the database for warehouses.
Key Highlights
- One of the primary drivers in the market is the speed of data retrieval is better for data lakes compared to data warehouses. According to O’Reilly Data Scientist Salary Survey, one-third of the data scientists spend time doing basic operations such as necessary extraction/transformation/load (ETL), data cleaning, and basic data exploration rather than real analytics or data modeling, which reduces the efficiency of the process. In addition, the investment for setting up a data lake is less than setting up a data warehouse.
- The growing use of IoT in many offices and informal spaces has further emphasized the need for data lakes for quicker and efficient data manipulation. The adoption of IoT devices is taking place rapidly as the amounts of data generated are huge with the connected devices in the system, where the demand for data lakes is increasing. Government initiatives across the globe like building smart cities are also supporting their deployment. Enterprises are also deploying solutions based on big data and stream processing to develop and maintain data lakes. The proliferation of data due to the adoption of IoT is driving the market growth for the data lakes market.
- Businesses today are inclined to data-driven decisions. The rise in digitalization is generating an enormous amount of data with organizations. With both medium and large-scale enterprises investing in adopting technologies and security, data lakes eliminates the need for data modeling. Therefore, the demand for data lakes is increasing. Data lakes have emerged as a practical solution to exponentially increasing data as companies need efficient and advanced data analytical capabilities. The features of data lakes of processing data on the cloud are fueling its market growth.
- The slow onboarding, the complexity of legacy data, higher upkeep costs, and data integration on data lakes is restricting market growth to an extent.
- With the onset of COVID 19, the market has seen some cloud-based innovation across different industry verticals with the distributed supply chains in the market and changed purchasing behavior. The use of the technology and data lakes for researchers who need patient information from across the world to examine the viability of these medications quickly and successfully has also driven the market toward its development.
Key Market Trends
Banking Sector to Witness a Significant Market Growth
- Banks have been increasing data lakes to integrate data across various domains to create a central database. Australia and New Zealand Banking Group (ANZ) has been implementing a project to aggregate all the data ponds across its domains to create a central data lake for the banking operations, allowing the bank to shift from the typically used data warehouse architecture.
- Banks are investing in data engineers to provide more responsive data lakes to tackle consumer requirements and have also been trying to increase data utility for on-the-go solutions. State Bank of India (SBI) has provided data lakes to bank executives, deputy managing directors, and chief information to deliver on-the-go analytics, apart from the typically used data warehouse.
- The rise in digital payments by consumers boosted the amount of data stored with banks with each transaction. Hence, opportunities for big-data analytics are growing. As in India, the digital payment trend is growing the market is expected to grow significantly.
- Further, Mox Bank Limited (Mox), a bank in Hong Kong, signed up over 35,000 customers in its first month, using the solutions from Amazon Web Services (AWS) to capture, store, process securely, and analyze that data, leveraging data insights to build a customer-centric banking experience using services from Amazon based on data lakes.
- The deployment of data lakes in the banking sector breaks down the number of silos. Storing data in a centrally managed infrastructure like Apache Hadoop–based data lake infrastructure helps cut down the number of information silos in an organization making data accessible to users across the enterprise.
North America is Expected to have High Adoption for Data Lakes
- According to Capgemini, more than 60% of the financial institutions in the United States believe that big data analytics offers a substantial competitive advantage over the competitors and more than 90% of the companies believe that the big data initiatives determine the chance for success in the future.
- Data Lakes are needed for the use of Smart Meter applications. In Canada, BC Hydro uses an EMC data lake for analyzing data aggregated by various smart meters. The data then enables detecting discrepancies in the system. This has aided in achieving savings of 75% of the electricity due to theft.
- The number of Smart Meters in the region has also been growing in usage. Owing to an increase in the usage of smart meters, a huge amount of data is being generated, which needs the use of Data Lakes. According to U.S Energy Information Administration, a total of over 94 million smart meters were installed among various sectors, including residential, commercial, industrial, and transportation.
- The region’s market is driven by the factors such as the increasing generation of data, such as clickstream data, server logs, subscriber data, customer relationship management (CRM), and enterprise resource planning (ERP), are expected to boost the market growth with vendors launching various data lake solutions and services. In addition, the higher rate of adoption of AI and ML in the region is also expected to drive market growth.
Competitive Landscape
The market landscape is defined by established technologies and software providers who have a strong brand image, geographic footprint, and customer base. However, the market is concentrated. Companies, such as Amazon and Microsoft, which hold a significant share of the cloud space, have a competitive edge over the existing market players, due to the consumer preference for cloud-delivered solutions and services.
- June 2020 – Microsoft acquired ADRM Software, which provides industry-specific data models for analytics. ADRM helps businesses address problems with integrated data architecture. ADARM Software’s industry-specific data models serve as information blueprints for planning, architecting, designing, governing, reporting, business intelligence, and advanced analytics. This acquisition will enable Microsoft to combine the Azure cloud platform with ADRM’s industry models to create intelligent data lakes.
Additional Benefits:
- The market estimate (ME) sheet in Excel format
- 3 months of analyst support
1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Industry Attractiveness – Porter’s Five Forces Analysis
4.2.1 Threat of New Entrants
4.2.2 Bargaining Power of Buyers
4.2.3 Bargaining Power of Suppliers
4.2.4 Threat of Substitutes
4.2.5 Intensity of Competitive Rivalry
4.3 Industry Value Chain Analysis
4.4 Assessment of Impact of COVID-19 on the Industry
4.5 Market Drivers
4.5.1 Proliferation of Data due to the Adoption of IoT
4.5.2 Need for Advanced Analytic Capabilities
4.6 Market Restraints
4.6.1 Slow Onboarding and Data Integration of Data Lakes
5 MARKET SEGMENTATION
5.1 Offering
5.1.1 Solution
5.1.2 Service
5.2 Deployment
5.2.1 Cloud-based
5.2.2 On-premise
5.3 End-user Vertical
5.3.1 IT and Telecom
5.3.2 BFSI
5.3.3 Healthcare
5.3.4 Retail
5.3.5 Manufacturing
5.3.6 Other End-user Verticals
5.4 Geography
5.4.1 North America
5.4.1.1 United States
5.4.1.2 Canada
5.4.2 Europe
5.4.2.1 United Kingdom
5.4.2.2 Germany
5.4.2.3 France
5.4.2.4 Italy
5.4.2.5 Rest of Europe
5.4.3 Asia-Pacific
5.4.3.1 China
5.4.3.2 Japan
5.4.3.3 India
5.4.3.4 Rest of Asia-Pacific
5.4.4 Latin America
5.4.4.1 Mexico
5.4.4.2 Brazil
5.4.4.3 Argentina
5.4.4.4 Rest of Latin America
5.4.5 Middle-East & Africa
5.4.5.1 United Arab Emirates
5.4.5.2 Saudi Arabia
5.4.5.3 South Africa
5.4.5.4 Rest of Middle-East & Africa
6 COMPETITIVE LANDSCAPE
6.1 Key Vendor Profiles
6.1.1 Microsoft Corporation
6.1.2 Amazon.com Inc.
6.1.3 Capgemini SE
6.1.4 Oracle Corporation
6.1.5 Teradata Corporation
6.1.6 SAP SE
6.1.7 IBM Corporation
6.1.8 Solix Technologies Inc.
6.1.9 Informatica Corporation
6.1.10 Dell EMC
6.1.11 Snowflake Computing Inc.
6.1.12 Hitachi Data Systems
7 INVESTMENT ANALYSIS
8 FUTURE OF THE MARKET