The data lakes market is expected to witness growth at a CAGR of 27.4% over the forecast period 2019-2024.
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.
- 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 for 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. The growing use of IoT in many offices and informal spaces has further emphasized in need for data lakes for quicker and efficient manipulation of data.
- The adoption of IoT device is taking place at a rapid pace. Government initiatives across the globe like building smart cities are also supporting their deployment. The proliferation of Data due to the Adoption of IoT is driving the market growth for data lakes market.
- The businesses today are inclined to data-driven decisions. The rise in digitalization is generating an enormous amount of data with the organizations 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.
- Whereas, the slow onboarding and data integration on data lakes is restricting market growth to an extent.
Key Market Trends
Banking Sector is Expected to Grow Significantly
- Banks have been increasing the use of 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 which will allow 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 also been trying to increase the utility of data for on the go solutions. State Bank of India (SBI) has been providing data lakes, apart from the typically used data warehouse, to bank executives, deputy managing director, and chief information to deliver on the go analytics.
- The rise in digital payments by the consumers globally is boosting the amount of data stored with banks with each transaction. Hence, opportunities for big-data analytics is growing.
- The deployment of data lakes in 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.
- According to Capgemini, more than 60% of the financial institutions in
the United Statesbelieve 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 Lakesare needed for the use of Smart Meter applications. In Canada, BC Hydro uses an EMCdata 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 have also been growing in usage. Owing to an increase in the usage of smart meters, 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 90 million smart meters is expected to be installed in the country by the year 2020.
The market landscape is defined by established technologies and software providers who have a strong brand image, geographic footprint, and customer base. 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.
April 2019– Temenos, the banking software company launched Temenos Data Lakeand is first to market with a robust, productized data lake that integrates big data analytics into its banking software. Temenos Data Lakeclaims to deliver out-of-the-box data integration, preparation, and optimization to power AI-driven banking applications. January 2019– Tata Consultancy Services, a global IT service, consulting, and business solutions organization, entered the market with its data lakes solutions for Business on AWS Marketplace. The newly launched software captures and manages all types of data in a central Hadoop repository.
Key Topics Covered
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Introduction to Market Drivers and Restraints
4.3 Market Drivers
4.3.1 Proliferation of Data due to the Adoption of IoT
4.3.2 Need for Advanced Analytic Capabilities
4.4 Market Restraints
4.4.1 Slow Onboarding and Data Integration on
4.5 Industry Value Chain Analysis
4.6 Industry Attractiveness – Porter’s Five Force Analysis
5 MARKET SEGMENTATION
5.1 By Offering
5.2 By Deployment
5.3 By End-user Vertical
5.3.1 IT and Telecom
5.3.6 Other End-user Verticals
6 COMPETITIVE LANDSCAPE
6.1 Company 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
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS
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