Global Database Management Analytics Market Size, Share, Growth Analysis By Deployment Model (Cloud-Based, On-Premises), By Database Type (Relational (SQL), Non-Relational (NoSQL), NewSQL), By Organization Size (Large Enterprises, Small and Medium-sized Enterprises (SMEs)), By Application (Data Integration and ETL, Data Visualization and Reporting, Advanced Analytics, Predictive Modeling, and Machine Learning, Customer Analytics and Personalization, Operational Analytics and Fraud Detection, Security and Compliance Management), By Industry Vertical (BFSI, Retail & E-commerce, Healthcare & Life Sciences, Telecommunications, Manufacturing, Government & Public Sector, Others), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Statistics, Trends and Forecast 2025-2034

Report Overview

The Global Database Management Analytics Market was valued at USD 120.3 billion in 2024 and is projected to reach USD 394.1 billion by 2034, expanding at a CAGR of 12.6% during the forecast period. The growth is primarily driven by the rising demand for real-time data insights, cloud-based analytics platforms, and AI-driven data management solutions across enterprises. Organizations are increasingly adopting advanced database analytics to improve operational efficiency, predictive decision-making, and customer engagement.

North America dominated the global market in 2024 with a 41.8% share, accounting for USD 50.28 billion in revenue, owing to strong enterprise digitalization and the rapid integration of AI-based data tools. The US alone contributed USD 45.64 billion in 2024 and is expected to rise to USD 126.13 billion by 2034, growing at a CAGR of 10.7% due to expanding data infrastructure investments and the presence of leading technology providers.

Database Management Analytics refers to the process of collecting, organizing, and analyzing large volumes of structured and unstructured data stored within databases to extract meaningful insights that support strategic and operational decisions. It integrates advanced technologies such as artificial intelligence, machine learning, and cloud computing to enhance data accessibility, optimize performance, and enable predictive analysis.

By transforming raw data into actionable intelligence, database management analytics helps businesses improve efficiency, detect trends, forecast outcomes, and ensure data integrity across multiple platforms. The increasing adoption of digital transformation initiatives, coupled with the growing need for real-time analytics and scalable data solutions, is driving the global expansion of this market across various industries, including finance, healthcare, retail, and manufacturing.

Database Management Analytics plays a critical role in modern enterprises by enabling organizations to harness the full potential of their data assets. It combines database management systems (DBMS) with analytical tools to ensure seamless data integration, storage, and interpretation for informed decision-making. Through techniques such as data mining, predictive modeling, and real-time processing, businesses can identify performance gaps, uncover growth opportunities, and enhance customer experiences.

The evolution of big data and cloud technologies has transformed how organizations handle complex datasets, allowing them to scale analytics operations efficiently while maintaining data security and compliance. Companies are increasingly deploying hybrid and multi-cloud database analytics platforms to handle growing data volumes, improve accessibility, and reduce latency in decision-making.

As industries across the globe move toward data-driven ecosystems, database management analytics is becoming indispensable for optimizing supply chains, financial forecasting, customer insights, and operational automation. The market’s growth is further accelerated by the proliferation of IoT devices, the integration of AI-powered query optimization, and the need for high-performance, low-cost data solutions that can adapt to dynamic business environments.

Recent months have seen big moves and investments in database management, analytics, and statistics. Databricks reached a $4 billion annual revenue run-rate and closed a $1 billion Series K funding round in September 2025, now valued at over $100 billion and with more than 650 customers spending above $1 million yearly.

Oracle launched its next-gen AI Database 26ai in October 2025, focusing on AI-powered analytics and easy enterprise integration, supporting open standards and advanced AI models. ClickHouse, the open-source analytics database, expanded its October 2025 Series C funding round—new investors include Citi Ventures and Bessemer, aiming to boost AI, ML, and global reach for its 2,000+ customers. Adeptia rolled out Automate, a platform for fast, smart enterprise data automation to improve integration and scale.

On the merger side, Analysys Mason tracked several data and analytics-related deals in 2025 involving giants like Accenture, IBM, AWS, and Salesforce, often targeting companies with AI and analytics specialization. Carta acquired Accelex in October 2025 to enhance its analytics suite for institutional investors, and Autodesk unified its analytics infrastructure by integrating various acquired firms for streamlined operations. These developments, alongside tech giants collectively investing $245 billion into data centers for AI—focusing on advanced chips and platforms—signal rapid industry growth and innovation, with analytics and database platforms at the center.

Key Takeaways

• The Global Database Management Analytics Market was valued at USD 120.3 billion in 2024, reflecting the increasing demand for efficient data processing and analytics solutions across industries.
• The market is projected to expand at a CAGR of 12.6% from 2025 to 2034, driven by the rising adoption of AI, machine learning, and cloud-based database technologies.
• By 2034, the market is expected to reach a value of USD 394.1 billion, highlighting strong long-term growth potential in enterprise data analytics and management platforms.
• North America dominated the market with a 41.8% share in 2024, owing to the rapid digital transformation and large-scale adoption of advanced analytics systems by enterprises.
• The North American market size stood at USD 50.28 billion in 2024, supported by robust IT infrastructure and high enterprise investments in data-driven solutions.
• The US accounted for USD 45.64 billion in 2024, maintaining its position as a key contributor due to its strong presence of cloud service providers and analytics innovators.
• The US market is forecast to reach USD 126.13 billion by 2034, registering a CAGR of 10.7%, propelled by the growing integration of AI-powered analytics, automation, and enterprise data modernization initiatives.

Role of AI

Artificial Intelligence plays a transformative role in the Database Management Analytics Market by automating data processing, enhancing query performance, and enabling predictive insights. AI-driven tools are revolutionizing how organizations manage, analyze, and interpret vast volumes of structured and unstructured data.

Through intelligent automation, AI reduces manual workloads in database maintenance, improves accuracy in data cleansing, and optimizes storage utilization. Machine learning algorithms assist in identifying hidden patterns, anomalies, and correlations within datasets, enabling businesses to make faster and more informed decisions.

AI-powered analytics platforms also strengthen data security by detecting unusual activities and preventing potential breaches through real-time monitoring. Moreover, natural language processing (NLP) allows non-technical users to interact with databases effortlessly using conversational queries, improving accessibility and productivity. In enterprise environments, AI integration supports advanced data visualization, real-time reporting, and self-learning systems that continuously refine performance metrics.

As businesses increasingly rely on data for strategic operations, AI is becoming indispensable in streamlining workflows, minimizing latency, and ensuring accuracy. The growing convergence of AI, cloud computing, and big data technologies is expected to redefine the future of database management analytics, driving efficiency, scalability, and innovation across industries.

Analysts’ Viewpoint

Analysts view the Database Management Analytics market as poised for sustained expansion, with several converging forces driving its evolution. Many note that the shift from traditional database systems to more dynamic, analytics-rich platforms is expected to intensify as organizations pursue data-driven decision-making, real-time insights, and infrastructure modernization. The integration of AI, in particular, is anticipated to become a differentiator, enabling predictive analytics, automated data cleansing, and enhanced query performance across large and complex datasets.

Stakeholders emphasise that cloud deployments and hybrid architectures are expected to gain prominence, as they provide scalability, flexibility, and faster time-to-insight. At the same time, analysts suggest that enterprises in mature regions may experience a deceleration in growth relative to emerging markets, necessitating innovation from vendors to maintain momentum. From a regional perspective, North America is projected to retain a leading position due to strong enterprise adoption and established tech ecosystems, but growth in APAC and EMEA is flagged as highly strategic for providers.

Analysts also highlight risks such as data governance, regulatory pressure, res and integration complexity, which could slow deployment or raise costs. Nevertheless, the overarching view is that the market’s long-term trajectory remains robust and that vendors with offerings that combine database management, analytics, AI, and cloud capabilities are well-positioned to capture value.

AI Industry Adoption

AI industry adoption in the Database Management Analytics Market has accelerated rapidly as businesses recognize the transformative potential of intelligent automation and data-driven insights. Artificial Intelligence is increasingly integrated into database systems to automate data collection, optimize storage, and enable predictive analytics.

Industries such as finance, healthcare, retail, and manufacturing are adopting AI-powered analytics platforms to improve decision-making, detect anomalies, and enhance customer experiences. These systems leverage machine learning algorithms to forecast trends, detect risks, and uncover relationships in complex datasets, providing organizations with a competitive edge.

The rise of AI in this domain is also driven by the demand for real-time insights, scalability, and cost efficiency in managing large data environments. Cloud-based AI analytics solutions are helping companies automate query optimization, streamline data governance, and personalize business intelligence dashboards.

In sectors such as e-commerce and telecommunications, AI is playing a vital role in managing dynamic data flows and ensuring operational agility. As enterprises transition to hybrid and multi-cloud architectures, the adoption of AI-enhanced database analytics is expected to continue expanding, allowing organizations to reduce manual intervention, improve accuracy, and accelerate digital transformation across all business functions.

Emerging trends

• One trend is the rapid shift towards hybrid multi-cloud and data fabric architectures, where enterprises combine on-premises systems with private and public clouds to achieve agility and scalability.
• Another trend is the rise of autonomous databases and AI-embedded analytics platforms that reduce manual database administration and enable real-time insights from complex, unstructured data.
• A third trend involves decentralised data architectures such as the data-mesh mode, where domain teams own and serve data products, supported by federated governance and metadata frameworks.
• A further trend is the elevated importance of metadata management, data governance, and observability as organisations adopt AI and advanced analytics. Effective metadata provides lineage, context, and trust, which underpins scalable analytics.
• Lastly, the democratisation of analytics through self-service platforms, no-/low-code tools, and business-user accessible insights is gaining traction, enabling faster decision-making across functions.
• These trends collectively indicate that database management analytics is evolving beyond traditional storage and querying to become a strategic, enterprise-wide capability driven by architectural innovation, AI integration, and organisational transformation.

US Market Size

The US Database Management Analytics Market was valued at USD 45.64 billion in 2024 and is projected to reach USD 126.13 billion by 2034, expanding at a CAGR of 10.7% during the forecast period. The growth is primarily driven by the rising adoption of cloud-based database solutions, increasing demand for real-time analytics, and the integration of AI and machine learning in data management platforms. The strong presence of leading technology providers and early adoption of digital transformation initiatives are further accelerating market expansion.

Enterprises in sectors such as finance, healthcare, retail, and manufacturing are increasingly investing in data-driven strategies to improve operational efficiency and customer engagement. Additionally, the rise of hybrid and multi-cloud environments in the US is supporting flexible data analytics infrastructure, enabling faster insights and reducing costs.

Growing regulatory focus on data governance and security is also pushing companies to adopt advanced analytics tools for compliance and transparency. With continuous innovation and increasing enterprise data complexity, the US market is expected to remain a key contributor to global revenue in the coming decade.

Investment and Business Benefit

Investments in the Database Management Analytics Market are growing rapidly as enterprises recognize the strategic value of transforming raw data into actionable intelligence. Businesses are channeling capital into AI-driven analytics, cloud infrastructure, and data governance platforms to enhance efficiency, scalability, and decision-making accuracy.

The return on these investments is visible through faster time-to-insight, optimized operational workflows, and improved customer engagement. Venture capital funding and corporate R&D spending are also increasing in areas such as predictive analytics, autonomous databases, and hybrid data architectures, reflecting the market’s strong growth potential.

From a business standpoint, the benefits of adopting advanced database management analytics are substantial. Companies gain the ability to integrate diverse data sources, reduce redundancies, and automate reporting processes. Enhanced data visualization and real-time analytics empower executives to make informed strategic decisions that drive profitability.

Furthermore, predictive modeling capabilities enable organizations to anticipate market trends, manage risks, and identify new revenue opportunities. Small and medium-sized enterprises are particularly benefiting from cloud-based analytics platforms that offer enterprise-grade performance at lower costs. Overall, investments in database management analytics are not only boosting productivity but also helping businesses achieve long-term competitiveness through data-driven innovation and operational excellence.

By Deployment Model

Cloud-based deployment accounted for 67.5% of the Database Management Analytics Market in 2024, emerging as the dominant mode due to its scalability, cost efficiency, and accessibility. The increasing shift toward digital transformation and the growing need for flexible, real-time data processing have accelerated the adoption of cloud platforms.

Organizations across industries are leveraging cloud-based analytics to manage vast volumes of structured and unstructured data while minimizing infrastructure costs. Cloud providers such as AWS, Microsoft Azure, and Google Cloud are continuously expanding their AI and machine learning capabilities, enabling businesses to derive deeper insights and improve decision-making speed.

The cloud model also supports remote collaboration, faster deployment cycles, and seamless integration with enterprise applications, making it highly suitable for data-driven enterprises. In contrast, on-premises deployment continues to serve organizations that prioritize data sovereignty, security, and regulatory compliance, particularly in sectors such as banking, defense, and healthcare.

However, the flexibility and innovation potential of the cloud are encouraging many enterprises to adopt hybrid models that combine the strengths of both environments. As the demand for AI-enhanced, real-time analytics continues to rise, the dominance of cloud-based deployment is expected to strengthen further, redefining how organizations manage, store, and analyze their data globally.

By Database Type

Relational databases accounted for 60.4% of the Database Management Analytics Market in 2024, making them the dominant database type due to their structured architecture, data integrity, and compatibility with traditional enterprise systems. Relational databases, built on SQL frameworks, continue to be preferred across industries that rely heavily on transactional data, such as finance, retail, and manufacturing.

Their ability to maintain consistency, enforce relationships between tables, and support complex queries makes them ideal for mission-critical applications. Integration with AI and analytics tools has further enhanced their efficiency in handling real-time data workloads and predictive analytics.

Non-relational (NoSQL) databases are steadily gaining momentum, particularly in applications requiring scalability, flexibility, and unstructured data handling, such as e-commerce, IoT, and social media analytics. They allow organizations to process large, diverse datasets without predefined schemas, supporting faster innovation cycles.

Meanwhile, NewSQL databases are emerging as a bridge between traditional SQL and NoSQL systems, combining the scalability of NoSQL with the ACID compliance of relational systems. Their adoption is growing in enterprises seeking both consistency and performance. Overall, the rise of hybrid data architectures is enabling businesses to use the right database model for each application, balancing performance, scalability, and cost efficiency across diverse data ecosystems.

By Organization Size

Large enterprises accounted for 75.2% of the Database Management Analytics Market in 2024, maintaining a strong lead due to their extensive data ecosystems, higher IT budgets, and need for advanced analytics capabilities. These organizations are leveraging database analytics to manage complex operations, automate reporting, and gain deeper insights from massive datasets generated across multiple departments and global locations.

Investments in cloud-based infrastructure, AI-driven analytics, and enterprise data governance frameworks have enabled large companies to improve decision-making accuracy, operational efficiency, and customer experience. Industries such as banking, telecommunications, and healthcare are at the forefront of this adoption, focusing on real-time data integration and predictive intelligence.

Small and medium-sized enterprises (SMEs), although holding a smaller share, are increasingly embracing database management analytics as cloud solutions become more accessible and affordable. The availability of scalable, subscription-based analytics platforms is allowing SMEs to compete effectively by harnessing real-time insights without heavy upfront investments.

These tools help smaller organizations optimize marketing, streamline operations, and improve customer retention. Over the coming years, SMEs are expected to accelerate adoption, particularly in emerging markets, supported by government digitalization initiatives and growing awareness of the benefits of data-driven business models.

By Application

Data visualization and reporting accounted for 25.8% of the Database Management Analytics Market in 2024, emerging as the leading application segment. The dominance of this segment is attributed to the growing need for real-time insights, interactive dashboards, and intuitive visual tools that help organizations translate complex datasets into actionable intelligence.

Enterprises are increasingly adopting visualization platforms integrated with AI and machine learning to monitor performance metrics, forecast trends, and support faster decision-making. Solutions such as Microsoft Power BI, Tableau, and Google Data Studio are widely used to enhance data accessibility across departments and simplify business intelligence workflows.

Data integration and ETL processes also play a critical role in unifying disparate data sources, ensuring accuracy and consistency before analysis. Advanced analytics, predictive modeling, and machine learning applications are rapidly expanding, allowing organizations to anticipate customer behavior, optimize operations, and detect anomalies.

Customer analytics and personalization are gaining traction in retail and e-commerce, where businesses use behavioral insights to improve engagement. Operational analytics and fraud detection remain vital in sectors like finance and telecom, ensuring efficiency and security. Meanwhile, security and compliance management continue to strengthen as data privacy regulations tighten, reinforcing the need for governance-driven analytical frameworks across industries.

By Industry Vertical

The BFSI segment accounted for 24.9% of the Database Management Analytics Market in 2024, emerging as the leading industry vertical. The dominance of this segment is attributed to the growing adoption of data-driven technologies by banks, insurance companies, and financial institutions to enhance risk management, fraud detection, and customer analytics.

The increasing volume of financial transactions, coupled with the need for regulatory compliance and real-time data visibility, has accelerated the integration of database analytics across core banking systems. Financial institutions are leveraging predictive analytics and AI-powered tools to optimize credit scoring, detect anomalies, and improve investment decision-making processes.

Retail and e-commerce follow closely, utilizing analytics to personalize shopping experiences and forecast demand trends. Healthcare and life sciences sectors are increasingly employing database analytics for patient data management, drug discovery, and clinical insights. Telecommunications and manufacturing industries are using data management solutions for network optimization and process automation.

Government and public sector organizations are also adopting these systems to improve transparency, citizen services, and policy planning. Overall, BFSI remains at the forefront of adoption due to its data-intensive nature, regulatory mandates, and the continuous need for secure, accurate, and intelligent analytics-driven decision-making frameworks.

Key Market Segments

By Deployment Model

• Cloud-Based
• On-Premises

By Database Type

• Relational (SQL)
• Non-Relational (NoSQL)
• NewSQL

By Organization Size

• Large Enterprises
• Small and Medium-sized Enterprises (SMEs)

By Application

• Data Integration and ETL
• Data Visualization and Reporting
• Advanced Analytics, Predictive Modeling, and Machine Learning
• Customer Analytics and Personalization
• Operational Analytics and Fraud Detection
• Security and Compliance Management

By Industry Vertical

• BFSI
• Retail & E-commerce
• Healthcare & Life Sciences
• Telecommunications
• Manufacturing
• Government & Public Sector
• Others

Regional Analysis

North America accounted for 41.8% of the global Database Management Analytics Market in 2024, representing a market value of USD 50.28 billion. The region’s dominance is attributed to the strong presence of major cloud service providers, advanced IT infrastructure, and a high concentration of data-driven enterprises.

The US leads the regional market, supported by early adoption of AI-driven analytics, machine learning integration, and hybrid cloud deployments across key industries such as banking, healthcare, and retail. Companies are increasingly investing in data modernization and governance solutions to enhance operational efficiency, strengthen cybersecurity, and ensure regulatory compliance.

The growing emphasis on digital transformation and enterprise automation has significantly boosted demand for advanced database analytics platforms. Organizations in Canada and Mexico are also accelerating adoption, driven by expanding industrial digitalization and government-backed smart infrastructure initiatives.

The region’s technology ecosystem, supported by innovation from companies such as Microsoft, IBM, Oracle, and Amazon Web Services, continues to influence global market dynamics. Additionally, increasing demand for real-time analytics, predictive modeling, and AI-powered decision support systems is expected to sustain North America’s leadership in the coming decade, making it a critical hub for database management analytics development and deployment.

Regional Analysis and Coverage

• North America
  • US
  • Canada
• Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Russia
  • Netherlands
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • South Korea
  • India
  • Australia
  • Singapore
  • Thailand
  • Vietnam
  • Rest of Latin America
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Rest of MEA

Driving Factors

The growth of the Database Management Analytics Market is primarily driven by the increasing adoption of cloud-based analytics platforms and the rising integration of AI and machine learning into data management systems. Enterprises are focusing on leveraging data-driven insights to enhance operational efficiency, reduce costs, and improve customer experiences. The expansion of big data ecosystems across industries such as banking, healthcare, retail, and telecommunications is fueling demand for advanced analytics capabilities.

Moreover, the rise of IoT-enabled devices and digital transformation initiatives has generated massive volumes of real-time data that require efficient management and analysis. Governments and large organizations are also emphasizing data governance and compliance, promoting the adoption of structured database solutions. Collectively, these factors are enabling faster decision-making, predictive intelligence, and improved business agility, solidifying database analytics as a core component of modern enterprise strategy.

Restraint Factors

Despite its strong growth outlook, the Database Management Analytics Market faces challenges related to data privacy, security, and integration complexities. The rising incidents of data breaches and cyberattacks have heightened concerns among enterprises regarding cloud data storage and third-party access. Additionally, the high implementation and maintenance costs associated with advanced analytics platforms pose financial challenges, particularly for small and medium-sized enterprises.

The lack of skilled professionals capable of managing AI-driven database systems and interpreting complex datasets further limits the full potential of adoption. Compatibility issues between legacy systems and modern cloud platforms can also hinder smooth data migration and integration.

Moreover, stringent government regulations regarding data handling, particularly in sectors such as banking and healthcare, add compliance burdens on enterprises. These constraints collectively slow down deployment speed and increase operational risks, making strategic planning and infrastructure investment critical for sustainable growth.

Growth Opportunities

The Database Management Analytics Market presents significant opportunities through the growing adoption of hybrid and multi-cloud database infrastructures. These environments enable enterprises to optimize performance, ensure flexibility, and maintain data security across diverse operational landscapes. The increasing integration of AI, natural language processing (NLP), and automation tools in analytics platforms offers new avenues for innovation, allowing non-technical users to interact with data more effectively.

The rise of data monetization strategies also opens revenue streams as companies leverage analytics insights to improve product offerings and customer engagement. Emerging economies are investing heavily in digital transformation, creating untapped markets for cloud-based and self-service analytics solutions. Furthermore, sectors such as healthcare, government, and retail are exploring advanced predictive analytics and real-time data management to drive personalized services. These advancements position database analytics as a cornerstone for future digital ecosystems and intelligent enterprise architectures.

Challenging Factors

The Database Management Analytics Market faces several challenges that could impede its growth trajectory. One of the major challenges lies in the complexity of managing exponentially growing data volumes from diverse sources, including IoT devices, cloud platforms, and enterprise applications. Ensuring real-time data consistency, accuracy, and governance across these fragmented systems remains a technical hurdle.

Vendor lock-in is another concern, as organizations relying on specific cloud providers face difficulties in migrating or integrating with alternative platforms. The rising cost of cybersecurity infrastructure, required to protect sensitive enterprise data, adds further operational strain. Additionally, balancing data privacy regulations across different jurisdictions creates compliance challenges for multinational companies.

Limited awareness among SMEs about the long-term benefits of database analytics and inadequate IT infrastructure in developing regions also restricts adoption. Addressing these issues through innovation, interoperability, and workforce upskilling will be crucial to achieving sustainable, large-scale market expansion.

Competitive Analysis

Oracle positions itself as a full-stack enterprise database and analytics provider with strong ties to traditional relational systems and is increasingly adding AI capabilities. Microsoft and AWS lead in cloud deployment and have broad ecosystems of analytics services.

IBM emphasizes hybrid cloud and enterprise governance, appealing to large organisations with complex needs. Snowflake appeals to organisations moving quickly to the cloud with data-lake/warehouse convergence and a focus on performance and scale. The ability to integrate structured, semi-structured, and unstructured data along with AI/ML capabilities is a major differentiator across providers.

The market is characterised by high complexity and evolving architectures: vendors are expanding from purely relational database systems toward analytics platforms that support advanced modelling, real-time streaming, vector search, and hybrid environments.

M&A, partnerships, and technology upgrades are frequent—firms that demonstrate innovation in AI-driven analytics, multi-cloud deployment, and data governance are better positioned. Legacy providers face the challenge of adapting rapidly to cloud and AI paradigms, while cloud-native and open data platform providers pose disruptive threats. Market share is relatively concentrated among large incumbents, but there is a growing opportunity for agile specialists and platform providers serving niche or emerging use-cases.

Top Key Players in the Market

• Microsoft Corporation
• Oracle Corporation
• Amazon Web Services (AWS)
• IBM Corporation
• SAP SE
• Snowflake
• Databricks
• Alibaba
• Qlik
• MongoDB
• Splunk
• Salesforce
• Informatica
• Alteryx
• CLoudera
• SaS
• Mathworks
• Teradata
• Datadog
• Others

Major Developments

• April 29, 2025: Google Cloud was named a Leader in the Forrester Wave™: Data Management for Analytics Platforms Q2 2025, achieving the highest score across 13 criteria for its intelligent data-platform strategy that integrates unified data management, AI-assisted analytics, and real-time processing.
• June 17, 2025: EDB launched its “Postgres AI Hybrid Management” release, offering hybrid-deployment control, unified observability across cloud/on-prem environments, automated provisioning, and AI-driven recommendations—reportedly boosting productivity by up to 30%.
• May 14, 2025: Databricks announced its agreement to acquire database startup Neon for about USD 1 billion to strengthen AI-powered data management capabilities and accelerate enterprises’ deployment of AI agents over their data estates.

Report Scope