Database Engines

Database Engines Market Outlook

The global database engines market is anticipated to reach approximately USD 85 billion by 2035, expanding at a remarkable compound annual growth rate (CAGR) of around 12% during the forecast period from 2025 to 2035. This growth can be attributed to the rapid increase in data generation across various sectors, the rising need for efficient data management solutions, and the growing adoption of cloud-based technologies. The demand for real-time analytics, coupled with the surge in artificial intelligence and machine learning applications, is also bolstering the growth of the database engines market. Furthermore, businesses are increasingly recognizing the significance of data-driven decision-making, which is leading to the widespread implementation of advanced database technologies.

Growth Factor of the Market

One of the primary growth factors driving the database engines market is the exponential increase in data volumes generated by both businesses and consumers. The integration of Internet of Things (IoT) devices is enabling the collection of vast amounts of data in real-time, necessitating sophisticated database management systems to handle and analyze this information efficiently. Additionally, the growing trend of digital transformation across industries is compelling organizations to invest in modern database solutions that facilitate seamless data access and processing. The rise of big data analytics is also a significant contributor, as organizations strive to harness insights from large data sets to gain competitive advantages. Furthermore, the emergence of cloud technologies has transformed the way databases are deployed, making them more accessible and scalable for organizations of all sizes. The increasing focus on data security and compliance with regulations such as GDPR is prompting businesses to adopt robust database solutions that ensure data integrity and privacy.

Key Highlights of the Market

• Projected market growth to USD 85 billion by 2035, with a CAGR of 12%.
• Surge in data generation driving demand for advanced database solutions.
• Growing adoption of cloud technologies enhancing scalability and accessibility.
• Increased focus on data security compliance pushing businesses toward modern database engines.
• Rise of big data analytics creating demand for efficient data management systems.

By Type

Relational Database Management System (RDBMS):

Relational Database Management Systems (RDBMS) have long been the cornerstone of database management, characterized by their structured query language (SQL) capabilities and a strong focus on data integrity. RDBMS solutions are particularly favored by organizations requiring stringent data consistency and structured data storage. The ability to create complex queries and utilize structured data models makes RDBMS ideal for applications involving financial transactions, customer relationship management, and any scenario where data interrelations are key. As enterprises increasingly rely on transactional data processing and analytics, the RDBMS segment continues to grow, driven by innovations in scalability and performance enhancements that meet the demands of modern applications.

NoSQL Database:

NoSQL databases are gaining traction due to their flexibility in handling unstructured and semi-structured data, making them suitable for a wide array of applications, including social networks, content management, and real-time analytics. Unlike traditional RDBMS, NoSQL systems allow for dynamic data models, which can easily accommodate changes in data structure without requiring intensive migrations. The ability to scale horizontally, combined with high availability and fault tolerance, positions NoSQL databases as a preferred choice for organizations dealing with large volumes of diverse data. As businesses pursue digital transformation and seek to innovate faster, NoSQL solutions are becoming increasingly indispensable.

In-Memory Database:

In-memory databases are designed to store data primarily in the main memory (RAM), which significantly enhances the speed of data retrieval and processing. This type of database is particularly advantageous for applications requiring real-time data access, such as online transaction processing, data analytics, and business intelligence platforms. The ability to perform high-speed transactions and access data with minimal latency makes in-memory databases an attractive option for organizations looking to improve operational efficiency. The growing trend of data-driven decision-making is further propelling the adoption of in-memory databases, as organizations prioritize speed in their data operations to maintain a competitive edge.

NewSQL Database:

As a bridge between traditional RDBMS and NoSQL solutions, NewSQL databases offer the scalability and performance of NoSQL while ensuring ACID (Atomicity, Consistency, Isolation, Durability) compliance, which is crucial for many transaction-oriented applications. NewSQL databases cater to organizations that require the robustness of an RDBMS but also demand the ability to scale out to handle massive data loads effectively. This adaptability has made NewSQL a popular choice among enterprises transitioning from legacy systems to more modern architectures that can support their growing data needs. The increasing reliance on real-time analytics and online transactions is likely to fuel further growth within this segment.

Graph Database:

Graph databases specialize in representing and querying relationships among data points, making them particularly effective for applications involving complex relationships and interconnected data, such as social networks, recommendation systems, and fraud detection. By storing data as nodes and edges, graph databases facilitate rapid connection querying and complex relationship mapping, which is invaluable for organizations that need to analyze data sets with interconnected information. As businesses seek to leverage relationships to derive insights and enhance customer experiences, the adoption of graph databases is expected to rise, with industries like finance and e-commerce leading the way in utilizing these technologies.

By Relational Database Management System

MySQL:

MySQL is one of the most widely used open-source relational database management systems, favored for its reliability, ease of use, and strong community support. It is commonly employed in web applications and online transaction processing due to its speed and efficiency. MySQL's flexible architecture allows for excellent performance in both small-scale applications and large enterprise solutions, making it a versatile option for businesses of all sizes. The continuous updates and enhancements to MySQL ensure that it remains relevant in an ever-evolving technological landscape, and its compatibility with various platforms further cements its position as a go-to database solution.

Microsoft SQL Server:

Microsoft SQL Server is known for its advanced features, including robust security measures, comprehensive analytics capabilities, and seamless integration with other Microsoft products. This RDBMS is particularly popular among enterprises that rely on the Microsoft ecosystem, offering tools for business intelligence, reporting, and data warehousing. The scalability of SQL Server means it can accommodate growing data needs as organizations expand, making it a preferred choice for large enterprises requiring reliable data management solutions. The strong support and regular updates from Microsoft further enhance the appeal of SQL Server in the competitive database market.

Oracle Database:

Oracle Database has established itself as a leader in the RDBMS market, particularly for enterprise applications requiring high reliability and performance. Known for its comprehensive suite of features, Oracle Database supports complex transactions, extensive data warehousing capabilities, and advanced security features. The option to run Oracle Database on-premises or in the cloud provides flexibility for organizations looking to optimize their database operations. With a strong focus on innovation, including support for machine learning and big data applications, Oracle remains a significant player in the evolving database engines market.

PostgreSQL:

PostgreSQL is an advanced open-source RDBMS known for its robustness and extensibility, making it a favorite among developers and enterprises alike. It supports advanced data types and allows for complex queries, making it suitable for various applications, from simple websites to large-scale data warehousing solutions. PostgreSQL's commitment to SQL compliance and its growing ecosystem of extensions provide users with the tools necessary for a wide array of data management needs. The active community contributes to continuous improvements and updates, ensuring that PostgreSQL remains a competitive option in the database landscape.

SQLite:

SQLite is a lightweight, serverless RDBMS that is widely used in application development due to its simplicity and ease of integration. It is particularly favored for mobile and embedded applications, as it requires minimal configuration and offers excellent performance with low resource consumption. SQLite's file-based architecture allows developers to easily implement database functionalities without the need for complex setups, making it an ideal choice for smaller projects and applications where a full-fledged database server is not necessary. Its popularity in mobile applications and web browsers further underscores its significance in the RDBMS market.

By Deployment

On-Premises:

On-premises database solutions involve hosting and managing databases locally within an organization’s infrastructure. This deployment model is often favored by businesses that prioritize data security, control, and compliance, particularly in industries with stringent regulatory requirements. On-premises databases allow for customized configurations and full ownership of sensitive data, enabling organizations to implement tailored security measures. However, this model may require significant upfront investment in hardware and ongoing maintenance costs. As organizations weigh the benefits and challenges, the demand for on-premises databases continues to persist, particularly in sectors such as finance and healthcare where data sensitivity is paramount.

Cloud:

Cloud-based database solutions have gained immense popularity due to their flexibility, scalability, and cost-effectiveness. These solutions allow organizations to access and manage databases over the internet, eliminating the need for extensive hardware investments and maintenance. Cloud databases can scale on demand, accommodating fluctuating workloads with ease, which is particularly beneficial for businesses experiencing rapid growth or seasonal spikes in data usage. Additionally, cloud providers often offer robust security features, data backups, and disaster recovery options, enhancing the overall reliability of data management. As more organizations embrace digital transformation and seek to leverage the benefits of cloud computing, the cloud deployment model is expected to see significant growth in the coming years.

By Enterprise Size

Small and Medium Enterprises (SMEs):

Small and Medium Enterprises (SMEs) represent a substantial segment of the database engines market, driven by their need for efficient, cost-effective data management solutions. SMEs often seek database engines that offer scalability and ease of use without requiring substantial IT resources. Cloud-based databases are particularly attractive to this segment, allowing SMEs to leverage advanced technologies without the burden of heavy infrastructure investments. Additionally, as SMEs increasingly recognize the importance of data-driven decision-making, they are adopting database solutions that provide insights and enhance operational efficiencies. With the rise of digital tools and platforms, the demand for accessible and affordable database solutions among SMEs is projected to grow significantly.

Large Enterprises:

Large Enterprises constitute a significant portion of the database engines market, characterized by their complex data management needs and high-volume transactions. These organizations typically require robust, scalable database solutions that can handle large data sets and support multiple applications simultaneously. Large enterprises often invest in on-premises database solutions for enhanced data control and security, particularly in industries such as finance and healthcare where compliance is critical. Additionally, many large organizations are transitioning to hybrid models that combine on-premises and cloud solutions to optimize their data management strategies. The demand for advanced analytics, real-time processing, and data integration capabilities is driving the growth of database engines tailored for large enterprises.

By User

IT & Telecom:

The IT and Telecom sector is one of the largest users of database engines, driven by the need for efficient data management, real-time processing, and analytics capabilities. The rapid growth of digital services, applications, and telecommunications infrastructure has created a massive demand for robust database solutions that can handle high volumes of data and support critical applications. Organizations within this sector often rely on a mix of relational and NoSQL databases to manage different types of data, from structured customer data to unstructured network logs. The ongoing push toward digital transformation and innovation in telecom services will continue to drive the adoption of advanced database technologies within this sector.

BFSI:

The Banking, Financial Services, and Insurance (BFSI) sector is characterized by stringent regulatory requirements and the need for high levels of data security and integrity. Database engines play a crucial role in managing vast amounts of sensitive financial data, ensuring compliance with regulations, and enhancing operational efficiency. The BFSI sector often utilizes RDBMS solutions for core banking applications, whereas NoSQL databases are increasingly being adopted for fraud detection, risk management, and customer analytics. As financial institutions continue to innovate and enhance customer experiences, the demand for advanced database technologies that support real-time processing and analytics is expected to grow significantly.

Healthcare:

The healthcare industry is increasingly leveraging database engines to manage electronic health records (EHRs), patient data, and clinical analytics. The need for secure, reliable, and accessible data management solutions is paramount, as healthcare organizations strive to improve patient care and comply with regulations such as HIPAA. RDBMS solutions are commonly used for structured patient data, while NoSQL databases are gaining traction for managing unstructured and semi-structured data from various sources, including medical devices and wearable technologies. As healthcare organizations continue to embrace digital health initiatives and data-driven decision-making, the demand for advanced database solutions tailored to the industry will likely increase.

Retail:

The retail sector relies heavily on database engines to manage customer data, inventory levels, sales transactions, and supply chain operations. With the growing importance of personalized customer experiences and data analytics, retailers are increasingly adopting sophisticated database solutions to gain insights into consumer behavior and optimize their operations. Both relational and NoSQL databases are utilized, with RDBMS being favored for transactional systems and NoSQL for handling large volumes of unstructured data generated from e-commerce platforms and social media. The ongoing digital transformation within the retail industry, fueled by the rise of online shopping and omni-channel strategies, is expected to drive the demand for advanced database technologies in this sector.

Government:

Government agencies require reliable and secure database engines to manage vast amounts of public data, citizen services, and regulatory compliance. The need for transparency and accountability necessitates robust database solutions that can handle diverse data types while ensuring data integrity and security. RDBMS solutions are commonly employed for managing structured data, while NoSQL databases are increasingly being adopted for big data applications, including data analytics and public safety applications. As government entities embrace digital transformation initiatives and seek to improve service delivery, the demand for modern database solutions tailored to the unique requirements of the public sector is expected to grow.

By Region

The North American region continues to dominate the global database engines market, accounting for over 35% of the total market share in 2023, driven by the presence of major technology companies and a strong focus on innovation. The region also benefits from a well-established IT infrastructure, high investment in research and development, and a growing demand for advanced data analytics solutions. Furthermore, the rapid adoption of cloud computing and big data technologies is expected to sustain robust growth in the North American database engines market, with a projected CAGR of 11% through 2035.

In Europe, the database engines market is anticipated to witness steady growth, fueled by increasing investments in digital transformation and data-centric initiatives across various industries. The region is placing greater emphasis on data privacy and compliance, leading many organizations to adopt advanced database solutions that ensure data security. The Asia Pacific region is also experiencing significant growth in the database engines market, largely driven by rapid urbanization, the proliferation of IoT devices, and the increasing adoption of cloud technologies by enterprises. With a growing focus on data-driven decision-making, the market in Asia Pacific is projected to expand at a CAGR of 13% during the forecast period, reflecting the region's dynamic digital landscape.

Opportunities

One of the most significant opportunities in the database engines market lies in the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. These technologies require robust data management systems that can handle vast datasets efficiently and offer real-time analytics. As organizations seek to leverage AI and ML for predictive analytics and automation, the demand for advanced database solutions that facilitate these applications will continue to rise. Moreover, the integration of AI-driven analytics into database engines can enhance data processing capabilities, offering organizations deeper insights into their operations and customer behaviors. This convergence of AI and database technologies presents a lucrative opportunity for vendors and developers aiming to innovate and provide solutions tailored to evolving market needs.

Another promising opportunity for growth in the database engines market is the ongoing digital transformation across industries. As businesses increasingly prioritize data-driven decision-making, there is a growing need for flexible, scalable, and efficient database solutions. This trend is particularly pronounced in sectors such as finance, healthcare, and retail, where organizations are leveraging data analytics to enhance customer experiences and optimize operations. The rise of cloud computing also plays a crucial role, as organizations seek cloud-native database solutions that offer scalability without heavy infrastructure investments. Vendors that can provide comprehensive, integrated solutions that address these evolving needs will be well-positioned to capitalize on this opportunity for growth.

Threats

The database engines market faces several threats, one of the most notable being the increasing prevalence of cyberattacks and data breaches. As organizations become more reliant on database systems for managing sensitive information, they also become attractive targets for malicious actors seeking to exploit vulnerabilities. A successful cyberattack can lead to significant financial losses, reputational damage, and regulatory penalties for affected organizations. As a result, database engine providers must prioritize security measures and ensure that their solutions are equipped to protect against evolving threats. The need for enhanced security features and compliance with data protection regulations is paramount for sustaining trust and mitigating potential risks in the market.

Additionally, the rapid pace of technological advancements presents a challenge for database engine providers. The emergence of newer technologies, such as serverless computing and emerging data architectures, can disrupt traditional database models and render them less competitive. Organizations may be drawn to innovative solutions that promise higher performance, lower costs, and greater flexibility, leading to increased competition among vendors. As a result, established database engine providers must continually adapt their offerings and invest in research and development to stay relevant and meet the changing demands of the market. Failure to keep pace with technological advancements could hinder growth opportunities and result in the loss of market share to more agile competitors.

Competitor Outlook

• Oracle Corporation
• Microsoft Corporation
• IBM Corporation
• Amazon Web Services (AWS)
• Google Cloud Platform
• MongoDB Inc.
• PostgreSQL Global Development Group
• Redis Labs
• Cassandra (Apache Software Foundation)
• MariaDB Corporation
• SQLite Development Team
• SAP SE
• Teradata Corporation
• Snowflake Inc.
• Cloudera Inc.

The competitive landscape of the database engines market is characterized by a mix of established players and emerging startups, each vying to capture market share through innovative solutions and services. Major corporations like Oracle, Microsoft, and IBM dominate the space with their comprehensive, enterprise-grade database offerings that cater to various industry needs. These companies have a significant advantage due to their extensive resources for research and development, enabling them to adapt to evolving market demands and incorporate emerging technologies into their product portfolios. Additionally, partnerships and collaborations with other tech firms are common strategies employed by these leaders to enhance their capabilities and expand their market reach.

Emerging players like MongoDB and Redis Labs have disrupted the market by offering NoSQL solutions that appeal to organizations seeking flexibility and scalability for big data applications. These companies have gained traction by addressing specific pain points of traditional RDBMS and providing solutions that cater to the growing need for handling unstructured and semi-structured data. The rapid adoption of cloud-based platforms has further fueled the competition, allowing these younger players to offer scalable, cost-effective solutions that can compete with established giants. As businesses continue to embrace digital transformation, the competitive landscape is expected to evolve rapidly, with innovative startups challenging traditional players and driving market dynamics.

Key players such as Amazon Web Services and Google Cloud Platform are also reshaping the database engines market through their cloud offerings. These cloud service providers offer a range of database solutions, from traditional relational databases to modern NoSQL and in-memory databases, allowing organizations to select the best fit for their business needs. Their ability to provide scalable services with minimal management overhead is appealing to businesses of all sizes. As cloud adoption accelerates, competition among these providers will intensify, leading to better services and lower costs for end-users. The landscape is evolving, and companies that can effectively leverage their strengths to innovate and respond to market demands are poised for success in the dynamic database engines market.

• 1 Appendix

  • 1.1 List of Tables
  • 1.2 List of Figures

• 2 Introduction

  • 2.1 Market Definition
  • 2.2 Scope of the Report
  • 2.3 Study Assumptions
  • 2.4 Base Currency & Forecast Periods

• 3 Market Dynamics

  • 3.1 Market Growth Factors
  • 3.2 Economic & Global Events
  • 3.3 Innovation Trends
  • 3.4 Supply Chain Analysis

• 4 Consumer Behavior

  • 4.1 Market Trends
  • 4.2 Pricing Analysis
  • 4.3 Buyer Insights

• 5 Key Player Profiles

  • 5.1 SAP SE
    • 5.1.1 Business Overview
    • 5.1.2 Products & Services
    • 5.1.3 Financials
    • 5.1.4 Recent Developments
    • 5.1.5 SWOT Analysis
  • 5.2 Redis Labs
    • 5.2.1 Business Overview
    • 5.2.2 Products & Services
    • 5.2.3 Financials
    • 5.2.4 Recent Developments
    • 5.2.5 SWOT Analysis
  • 5.3 MongoDB Inc.
    • 5.3.1 Business Overview
    • 5.3.2 Products & Services
    • 5.3.3 Financials
    • 5.3.4 Recent Developments
    • 5.3.5 SWOT Analysis
  • 5.4 Cloudera Inc.
    • 5.4.1 Business Overview
    • 5.4.2 Products & Services
    • 5.4.3 Financials
    • 5.4.4 Recent Developments
    • 5.4.5 SWOT Analysis
  • 5.5 Snowflake Inc.
    • 5.5.1 Business Overview
    • 5.5.2 Products & Services
    • 5.5.3 Financials
    • 5.5.4 Recent Developments
    • 5.5.5 SWOT Analysis
  • 5.6 IBM Corporation
    • 5.6.1 Business Overview
    • 5.6.2 Products & Services
    • 5.6.3 Financials
    • 5.6.4 Recent Developments
    • 5.6.5 SWOT Analysis
  • 5.7 Oracle Corporation
    • 5.7.1 Business Overview
    • 5.7.2 Products & Services
    • 5.7.3 Financials
    • 5.7.4 Recent Developments
    • 5.7.5 SWOT Analysis
  • 5.8 MariaDB Corporation
    • 5.8.1 Business Overview
    • 5.8.2 Products & Services
    • 5.8.3 Financials
    • 5.8.4 Recent Developments
    • 5.8.5 SWOT Analysis
  • 5.9 Teradata Corporation
    • 5.9.1 Business Overview
    • 5.9.2 Products & Services
    • 5.9.3 Financials
    • 5.9.4 Recent Developments
    • 5.9.5 SWOT Analysis
  • 5.10 Google Cloud Platform
    • 5.10.1 Business Overview
    • 5.10.2 Products & Services
    • 5.10.3 Financials
    • 5.10.4 Recent Developments
    • 5.10.5 SWOT Analysis
  • 5.11 Microsoft Corporation
    • 5.11.1 Business Overview
    • 5.11.2 Products & Services
    • 5.11.3 Financials
    • 5.11.4 Recent Developments
    • 5.11.5 SWOT Analysis
  • 5.12 SQLite Development Team
    • 5.12.1 Business Overview
    • 5.12.2 Products & Services
    • 5.12.3 Financials
    • 5.12.4 Recent Developments
    • 5.12.5 SWOT Analysis
  • 5.13 Amazon Web Services (AWS)
    • 5.13.1 Business Overview
    • 5.13.2 Products & Services
    • 5.13.3 Financials
    • 5.13.4 Recent Developments
    • 5.13.5 SWOT Analysis
  • 5.14 PostgreSQL Global Development Group
    • 5.14.1 Business Overview
    • 5.14.2 Products & Services
    • 5.14.3 Financials
    • 5.14.4 Recent Developments
    • 5.14.5 SWOT Analysis
  • 5.15 Cassandra (Apache Software Foundation)
    • 5.15.1 Business Overview
    • 5.15.2 Products & Services
    • 5.15.3 Financials
    • 5.15.4 Recent Developments
    • 5.15.5 SWOT Analysis

• 6 Market Segmentation

  • 6.1 Database Engines Market, By Type
    • 6.1.1 Relational Database Management System (RDBMS)
    • 6.1.2 NoSQL Database
    • 6.1.3 In-Memory Database
    • 6.1.4 NewSQL Database
    • 6.1.5 Graph Database
  • 6.2 Database Engines Market, By User
    • 6.2.1 IT & Telecom
    • 6.2.2 BFSI
    • 6.2.3 Healthcare
    • 6.2.4 Retail
    • 6.2.5 Government
    • 6.2.6 Others
  • 6.3 Database Engines Market, By Deployment
    • 6.3.1 On-Premises
    • 6.3.2 Cloud
  • 6.4 Database Engines Market, By Enterprise Size
    • 6.4.1 Small and Medium Enterprises
    • 6.4.2 Large Enterprises

• 7 Competitive Analysis

  • 7.1 Key Player Comparison
  • 7.2 Market Share Analysis
  • 7.3 Investment Trends
  • 7.4 SWOT Analysis

• 8 Research Methodology

  • 8.1 Analysis Design
  • 8.2 Research Phases
  • 8.3 Study Timeline

• 9 Future Market Outlook

  • 9.1 Growth Forecast
  • 9.2 Market Evolution

• 10 Geographical Overview

  • 10.1 Europe - Market Analysis
    • 10.1.1 By Country
      • 10.1.1.1 UK
      • 10.1.1.2 France
      • 10.1.1.3 Germany
      • 10.1.1.4 Spain
      • 10.1.1.5 Italy
  • 10.2 Asia Pacific - Market Analysis
    • 10.2.1 By Country
      • 10.2.1.1 India
      • 10.2.1.2 China
      • 10.2.1.3 Japan
      • 10.2.1.4 South Korea
  • 10.3 Latin America - Market Analysis
    • 10.3.1 By Country
      • 10.3.1.1 Brazil
      • 10.3.1.2 Argentina
      • 10.3.1.3 Mexico
  • 10.4 North America - Market Analysis
    • 10.4.1 By Country
      • 10.4.1.1 USA
      • 10.4.1.2 Canada
  • 10.5 Database Engines Market by Region
  • 10.6 Middle East & Africa - Market Analysis
    • 10.6.1 By Country
      • 10.6.1.1 Middle East
      • 10.6.1.2 Africa

• 11 Global Economic Factors

  • 11.1 Inflation Impact
  • 11.2 Trade Policies

• 12 Technology & Innovation

  • 12.1 Emerging Technologies
  • 12.2 AI & Digital Trends
  • 12.3 Patent Research

• 13 Investment & Market Growth

  • 13.1 Funding Trends
  • 13.2 Future Market Projections

• 14 Market Overview & Key Insights

  • 14.1 Executive Summary
  • 14.2 Key Trends
  • 14.3 Market Challenges
  • 14.4 Regulatory Landscape

Segments Analyzed in the Report

The global Database Engines market is categorized based on

By Type

• Relational Database Management System (RDBMS)
• NoSQL Database
• In-Memory Database
• NewSQL Database
• Graph Database

By Deployment

• On-Premises
• Cloud

By Enterprise Size

• Small and Medium Enterprises
• Large Enterprises

By User

• IT & Telecom
• BFSI
• Healthcare
• Retail
• Government
• Others

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

Key Players

• Oracle Corporation
• Microsoft Corporation
• IBM Corporation
• Amazon Web Services (AWS)
• Google Cloud Platform
• MongoDB Inc.
• PostgreSQL Global Development Group
• Redis Labs
• Cassandra (Apache Software Foundation)
• MariaDB Corporation
• SQLite Development Team
• SAP SE
• Teradata Corporation
• Snowflake Inc.
• Cloudera Inc.