Big Data Technologies: 10 Emerging Trends [2025 & Beyond]

This report dives into the top 10 big data technologies, focusing on enabling technologies such as cloud computing, data integration, stream processing and beyond. Each technology features three use cases and one standout startup, providing a clear picture of their practical applications and future potential. Read more to stay ahead in the rapidly evolving big data & analytics landscape!

Big data and analytics technology manages vast and complex datasets across industries. For example, distributed computing processes large-scale data in real-time to assist industries that require insights for decision-making such as finance and healthcare. Furthermore, in-memory data fabric technology enables real-time data processing and low-latency access to data. It allows organizations to improve analytics and performance, and make timely decisions.

The urgency for stakeholders to implement these advancements comes from the growing need to get actionable insights quickly, stay responsive to market changes, and enhance operational efficiency. Adapting to these changes is crucial for maintaining a competitive edge, fostering agility in analytics, and supporting a data-driven culture across enterprises.

Why should you read this report?

  • Gain insights into the top 10 technologies impacting big data & analytics.
  • Learn about three practical use cases for each technology.
  • Meet 10 innovative startups advancing these technologies.

 

10 Emerging Trends of Big Data Technologies | StartUs Insights

 

Key Takeaways

  1. Cloud Computing
    • Use Cases:
      • Real-Time Data Processing
      • Disaster Recovery and Data Backup
      • Data Lakes for Unstructured Data
    • Startup to Watch: Xonai
  2. In-memory Data Fabric
    • Use Cases:
      • Customer Behavior Insights
      • Dynamic Pricing Models
      • Real-Time Fraud Detection
    • Startup to Watch: Neeve Research
  3. Data Integration
    • Use Cases:
      • Supply Chain Optimization
      • Smart Cities IoT Data Integration
      • Healthcare Systems Integration
    • Startup to Watch: VizRD Labs
  4. Distributed Computing
    • Use Cases:
      • Genomic Data Analysis
      • Social Media Sentiment Analysis
      • Cybersecurity Threat Detection
    • Startup to Watch: Aithericon
  5. Data Visualization
    • Use Cases:
      • Customer Segmentation Analysis
      • Healthcare Data Visualization
      • Operational Efficiency
    • Startup to Watch: Opiam Analytics
  6. Stream Processing
    • Use Cases:
      • Monitoring Network Traffic
      • IoT Data Analytics
      • Online Recommendation Systems
    • Startup to Watch: Geo Engine
  7. Data Preprocessing
    • Use Cases:
      • Autonomous Driving Image Processing
      • E-commerce Feature Engineering
      • Industrial IoT Anomaly Detection
    • Startup to Watch: VUGENE
  8. Data Warehousing
    • Use Cases:
      • Inventory Management
      • Customer Insights and Segmentation
      • Financial Reporting
    • Startup to Watch: Evidentli
  9. Machine Learning (ML)
    • Use Cases:
      • Personalized Healthcare Recommendations
      • Chatbot NLP
      • Manufacturing Predictive Maintenance
    • Startup to Watch: Precise Big Data
  10. NoSQL Databases
    • Use Cases:
      • Real-Time User Data
      • IoT Data Management
      • Healthcare Record Management
    • Startup to Watch: WeaveDB

What You Need to Know About Big Data & Analytics

What are data technologies?

Big data technologies process large datasets to derive insights in real-time while data integration tools streamline data flow between systems to enhance data accessibility. Data visualization tools further create interactive visual representations of data to understand trends and patterns. Overall, these technologies work together to better position organizations in their data-driven decision-making processes.

Data technologies include tools and methodologies that manage, process, and analyze data. These technologies include data warehousing, data lakes, and big data frameworks, among others. Relational databases, for instance, store structured data efficiently, while NoSQL databases handle unstructured data, providing flexibility and scalability. Further, data warehousing solutions consolidate data from various sources for analysis. In contrast, data lakes store vast amounts of raw data in their native format to allow flexible data retrieval and processing.

What are emerging data analytics technologies?

  • Artificial intelligence (AI) and machine learning (ML) automate data analysis, enable predictive analytics, and detect anomalies. Thereby, they identify patterns and trends in large datasets.
  • Natural language processing (NLP) enables machines to understand and interpret human language. NLP extracts insights from unstructured data, such as customer feedback and social media interactions to enhance sentiment analysis and gauge consumer behavior.
  • The integration of augmented analytics automates data preparation and insights generation through AI-driven capabilities to offer strategic decision-making rather than data manipulation.
  • Cloud-based analytics solutions provide scalability and accessibility to analyze vast amounts of data without extensive on-premises infrastructure.
  • Stream processing frameworks analyze data as it arrives. This empowers applications that require immediate insights, such as fraud detection and operational monitoring.

Where is this Data from?

StartUs Insights provides data through its comprehensive Discovery Platform, which covers 4.7+ million startups, scaleups, and tech companies globally, as well as 20K+ emerging technologies & trends. The platform excels in startup and technology scouting, trend intelligence, and patent searches, offering a detailed view of the innovation landscape. For this report, we analyzed technologies within specific industries using the trend intelligence feature. During this research, we identified patterns and trends, pinpointing relevant use cases and the startups developing solutions for each. More capabilities and details are available at StartUs Insights Discovery Platform.

10 Emerging Technologies Impacting the Future of Big Data & Analytics [2025 & Beyond]

1. Cloud Computing

Cloud computing offers scalable infrastructure and flexible data processing capabilities. It leverages technologies such as distributed computing frameworks, NoSQL databases, and cloud-native analytics tools for efficient processing, storage, and analysis of vast datasets. This supports diverse data types, including structured, unstructured, and semi-structured data while managing and processing them centrally. Further, cloud computing offers various deployment models such as private, public, hybrid, and multi-cloud where each provides scalability, security, and cost-effectiveness.

Private clouds allow high control over sensitive data, while public clouds offer cost-effective processing solutions. Hybrid and multi-cloud approaches balance and optimize their data strategies by leveraging the benefits of different cloud environments. Additionally, cloud service providers facilitate advanced analytics with services like managed data warehousing, real-time analytics, and machine learning to derive insights and support data-driven decisions.

3 Practical Use Cases of Cloud Computing:

  • Real-Time Data Processing: Cloud platforms enable rapid scaling of analytics applications while supporting frameworks for large data transformations. This allows for the flexible expansion and reduction of resources to process incoming data in real time with reduced cost and time. This immediate analysis makes timely decisions and improves operational efficiency.
  • Disaster Recovery and Data Backup: The cloud offers secure replication and backup of data across geographically diverse servers for ensuring data management and quick recovery during data breaches or failures. This maintains business continuity, minimizes downtime, and protects against data loss for creating data strategies.
  • Data Lakes for Unstructured Data: Cloud services provide serverless options to establish data lakes that accommodate structured and unstructured data. Automation tools simplify data lake creation, while other cloud-native services query diverse data sources using familiar query languages. As a result, it integrates and accesses data across multiple formats to provide deeper insights and flexible data analysis without complex infrastructure.

Startup to Watch: Xonai

UK-based Xonai provides an accelerator technology that integrates with Spark runtimes to reduce job execution times. Xonai enhances compatibility with Spark SQL, caching mechanisms, and commonly used big data storage sources. Additionally, the startup’s cache serializer enables faster caching and reduces storage requirements, with compression options to balance performance and storage for demanding workloads. The Xonai Accelerator activates through Spark plugin properties without altering the execution environment. Thus, the company improves efficiency and reduces cloud and server costs to optimize data infrastructure and analytics processes.

2. In-Memory Data Fabric

In-memory data fabric provides real-time processing solutions that address latency issues found in traditional disk-based storage systems. This is achieved by utilizing in-memory computing to store and analyze data directly in RAM, significantly improving access speeds and enabling rapid decision-making. This technology unifies diverse data types across business units, overcoming challenges of fragmented data silos while offering scalability and flexibility to efficiently handle vast amounts of structured, semi-structured, and unstructured data from sources like IoT, social media, and AI/ML applications.

In-memory data fabrics integrate with existing databases and big data frameworks, supporting both batch and stream processing workloads, and leverage distributed computing to enhance processing capabilities. Despite higher upfront costs for RAM, the overall efficiency gains and performance boost offered by in-memory data fabrics can lead to long-term cost savings and improved application performance for businesses dealing with large volumes of data.

3 Practical Use Cases of In-Memory Data Fabric:

  • Customer Behavior Insights: Businesses analyze customer behavior in real-time by leveraging access to transactional and historical data without the delays inherent in disk-bound databases. As a result, they personalize recommendations, enhance customer experiences, and identify trends that result in more targeted marketing and better customer engagement.
  • Dynamic Pricing Models: In-memory data fabric optimizes pricing strategies by processing massive data volumes at high speed to respond to market demand, competitor pricing, and inventory levels instantly. It supports dynamic pricing and enables companies to maximize revenue by adjusting prices based on immediate market conditions and customer willingness to pay.​
  • Real-Time Fraud Detection: The technology identifies potential fraud within milliseconds by analyzing transactional patterns and anomalies in real-time and tackling the database read and write speeds. Thus, financial institutions reduce fraud losses, meet regulatory compliances, and protect customers’ interests through faster detection and prevention measures.

Startup to Watch: Neeve Research

US-based startup Neeve Research develops the X Platform, a distributed data and compute framework to process volumes of big and fast data in real-time. The platform integrates big-data storage, fast-data streaming, and real-time processing in a single, holistic solution to enhance data management, computing, and analytics capabilities. Built with Java and based on microservices, each X-based system operates in memory to achieve horizontal scalability, fault tolerance, and high performance, while ensuring collaboration between microservices through guaranteed messaging. This architecture allows developers to focus on business logic, as X manages non-functional aspects like state durability and message reliability.

3. Data Integration

Data integration brings together diverse data sources to create a unified view for analysis. Technologies such as artificial intelligence and machine learning enhance data integration processes by increasing efficiency and enabling real-time processing. Also, they improve data quality through automated algorithms that manage large data volumes, detect inconsistencies, and support predictive analytics.

As data volumes grow, cloud-based solutions facilitate real-time integration using advanced change data capture techniques to reduce manual processing efforts. In addition, technologies like master data management (MDM), data warehouses, and cloud integration platforms support the efficient handling of unstructured data. Thus, it enhances data accuracy, provides scalable management solutions, and improves business intelligence for informed decision-making and operational efficiency.

3 Practical Use Cases of Data Integration:

  • Supply Chain Optimization: By consolidating data from multiple sources, such as inventory systems, suppliers, and logistics platforms, data integration creates a unified view of the supply chain. It improves demand forecasting, inventory management, and shipment tracking. Further, this real-time visibility leads to more efficient operations, reduced costs, and enhanced ability to respond to market changes quickly.
  • Smart Cities: Connecting diverse IoT devices and sensors within a city’s infrastructure through data integration enables real-time monitoring and analysis of traffic, utilities, public safety, and environmental conditions. It supports more efficient urban planning and resource management with proactive solutions to citywide challenges like congestion, waste management, and emergency response.
  • Healthcare Systems Integration: Integrating data from healthcare sources, such as electronic healthcare records, wearable devices, and lab systems provides a holistic view of a patient’s health status. This enables personalized treatment plans, accurate diagnosis, and proactive health monitoring to improve patient outcomes, care coordination, and overall healthcare efficiency.

Startup to Watch: VizRD Labs

US-based startup VizRD Labs develops a multi-layered technology platform to address complex analytics challenges in supply chain and logistics. Its platform integrates a customizable application layer, a machine learning layer, an optimization layer, and a data API layer. The application layer enables tailored solutions to add or adjust applications as needed. The machine learning layer utilizes existing data for accurate forecasting, pattern recognition, and decision policy modeling by calibrating analytics with company data.

The optimization layer leverages advanced algorithms to optimize supply chain design and operations for cost reduction, efficiency maximization, or improved service, using accurate real-time, historical, and projected data. The data API layer integrates with existing data infrastructure through flexible API connections, eliminates redundancy, and automates data ingestion and processing.

4. Distributed Computing

Distributed computing offers a framework that enables processing and analyzing large datasets across multiple machines to enhance efficiency, scalability, and performance. It breaks down large computations into smaller, manageable tasks for parallel data processing across various nodes to reduce latency and improve real-time analytics.

Technologies include distributed file systems and frameworks for parallel processing that manage data storage and computation concurrently. Such systems support fault tolerance, data replication, and dynamic load balancing to ensure consistency and reliability under heavy data workloads. This decentralized approach to data processing improves insights and optimizes resource utilization for data-intensive applications like advanced analytics, machine learning, and real-time decision-making across industries.

3 Practical Use Cases of Distributed Computing:

  • Genomic Data Analysis: Distributed computing processes massive genomic datasets by splitting analysis tasks across multiple nodes. This reduces the time needed to sequence genomes and detect genetic markers leading to faster results in medical research, personalized medicine, and genetic therapy development.
  • Social Media Sentiment Analysis: Companies analyze vast streams of social media data in parallel to understand public sentiment, trends, and consumer behavior in real-time. The technology captures social sentiments to benefit marketing strategies, brand perception, and customer engagement.
  • Cybersecurity Threat Detection: Distributed computing identifies cybersecurity threats by concurrently processing logs and real-time data from multiple network sources for enabling rapid threat detection and response. Thus, it enhances security measures, provides quick threat mitigation, and reduces potential breaches to protect organizational data and assets.

Startup to Watch: Aithericon

German startup Aithericon develops a digital R&D platform covering the research process, from data creation or import to complex analysis and final reporting. It structures workflows that serve as sequences of nodes and represent large-scale process graphs with parallel or sequential execution. Features include tools for building these flows, such as a low-code editor, an application programming interface (API), or command line interface (CLI) tools that enable efficient distributed computing and reusable templates for recurring tasks.

The platform leverages container technology for software packaging and portability, while user-defined code executes through a built-in integrated development environment (IDE) to run scripts or data analysis. Additionally, the data layer ensures input-output handling, central storage access, and automatic report generation with visualization features for human analysis.

5. Data Visualization

Data visualization converts complex datasets into visual formats such as graphs, charts, and maps to identify patterns, trends, and actionable insights. It makes data comprehensible and accessible for data-driven decision-making. Modern visualization techniques range from basic line and bar plots to more advanced visuals like heat maps, bubble plots, and network diagrams to identify correlations and anomalies. Further, big data visualization leverages complex forms, such as kernel density estimation, box and whisker plots, and correlation matrices, to manage massive datasets in real-time.

3 Practical Use Cases of Data Visualization:

  • Customer Segmentation Analysis: Visual dashboards identify and interpret patterns across different customer demographics, behaviors, and preferences to enhance targeted marketing strategies and product development. This results in personalized customer experiences and improved marketing return on investment.
  • Healthcare Data Visualization: Visualizing complex health data, such as patient records and treatment outcomes, assists practitioners to spot trends, improve diagnosis accuracy, and monitor public health issues. This leads to improved clinical decisions, earlier interventions, and better patient care.
  • Operational Efficiency: Visual tools convert raw operational data into graphs and charts to monitor key performance metrics and identify bottlenecks. It streamlines processes, reduces operational costs, and enhances productivity.

Startup to Watch: Opiam Analytics

Indian startup Opiam Analytics develops Performance Pad, a performance management platform for construction projects that plans, schedules, and estimates based on data analysis. The platform schedules projects through a work breakdown structure (WBS) to allocate activities from a customizable library and establish inter-activity relationships. The estimation service then integrates with the WBS to allocate resources and adjust project estimates.

Performance Pad’s data analytics offer insights through dashboards for time, cost, and cash flow for predicting delays, identifying potential cost overruns, and managing cash flow. These real-time insights enable improved productivity and project health monitoring to offer visibility into key performance indicators (KPIs), enhance project performance, and facilitate efficient decision-making.

 

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6. Stream Processing

Stream processing focuses on real-time data ingestion, processing, and analysis. It works continuously and provides nearly instantaneous insights for applications requiring real-time analytics, such as financial monitoring, equipment oversight in manufacturing, and live data analysis from IoT devices. Technologies supporting stream processing include various open-source platforms and cloud-based solutions designed for high throughput, fault tolerance, and real-time analytics.

Stream processing is capable of handling data formats like javascript object notation (JSON), avro, and protobuf with schema variability, data consistency challenges, and messaging semantics to maintain data integrity. These principles enable rapid and scalable data processing to provide low-latency insights crucial for real-time decision-making, monitoring, and predictive analytics.

3 Practical Use Cases of Stream Processing:

  • Monitoring Network Traffic: Real-time stream processing analyzes network packets and traffic patterns instantly to identify anomalies and potential threats. This enhances network security, minimizes downtime, and supports proactive threat mitigation.
  • IoT Data Analytics: Continuously processing data from IoT sensors and devices monitors equipment status, detects faults, and responds to environmental changes. Thus, it optimizes operations, reduces maintenance costs, and improves predictive maintenance capabilities.
  • Online Recommendation Systems: Real-time data processing analyzes user behavior as they interact with websites to provide personalized product recommendations and dynamic content updates. This increases user engagement along with conversion rates, and enhances the overall customer experience.

Startup to Watch: Geo Engine

German startup Geo Engine builds a platform for geodata and time-series processing that integrates data acquisition, preparation, and analysis in a cloud-enabled environment. It processes spatio-temporal data with time as a dimension and supports raster and vector data workflows through stream-based processing. Geo Engine’s architecture includes features like workflow definition through either declarative methods or a web-based interface for automatic data harmonization and standardized access to local and remote data sources.

It integrates with machine learning, automates data pipelines, and provides flexible access through representational state transfer application programming interface (REST APIs), and open geospatial consortium (OGC) endpoints. This creates custom data products, dashboards, and applications tailored to geospatial data needs. Thus, the company creates a toolset for geospatial data processing, visualization, and advanced analytics, while ensuring integration and usability for various industry needs.

7. Data Preprocessing

Data preprocessing prepares raw data for analysis by transforming it into a clean, structured, and usable format. It tackles challenges such as noise, inconsistencies, null values, and discrepancies to ensure a reliable dataset and suitable for analytics or machine learning.

The process includes data cleaning to remove errors and duplicates, data transformation to convert data into formats compatible with modeling (e.g. encoding categorical variables into numerical values), and normalization and scaling to balance the data’s range. Data integration merges different data sources into a coherent whole, while dimensionality reduction retains relevant features to optimize computational efficiency and enhance analytical accuracy.

3 Practical Use Cases of Data Preprocessing:

  • Autonomous Driving Image Processing: Cleaning and normalizing raw sensor and camera data through preprocessing ensures accurate object recognition and real-time decision-making for autonomous vehicles. This step enhances the safety, precision, and efficiency of in-vehicle navigation.
  • Ecommerce Feature Engineering: Transforming and encoding customer behavior data, such as purchase history and browsing patterns, provides improved input for predictive models. This enhances personalized recommendations and targeted marketing, boosting customer satisfaction and sales conversions.
  • Industrial IoT Anomaly Detection: Preprocessing raw sensor data from industrial equipment removes noise, standardizes signals, and extracts features to detect equipment malfunctions or anomalies early. It thereby enables proactive maintenance, reduced downtime, and cost savings.

Startup to Watch: VUGENE

Lithuanian startup VUGENE creates a bioinformatics platform for conducting statistical and machine learning analyses on multi-omics research data to understand the origins, causes, treatments, and biomarkers of complex disorders like cancers and neurodegenerative diseases. It manages the data analytics process, from raw data processing and quality control to statistical analysis, model training, and result dissemination.

Features include rigorous quality control through visualizations, biomarker identification, predictive model training, and biological function analysis to translate findings into insights. Further, its computational infrastructure for scalability and privacy, includes automated data pipelines, localized cloud storage, and optimized computing resources to handle intensive tasks like genome sequencing.

8. Data Warehousing

Data warehousing offers a structured environment for storing, processing, and analyzing large datasets. A modern data warehouse relies on a relational database system hosted on-premises or in the cloud, with key components like a central database, extract, transform, and load (ETL) tools for data conversion, metadata for data organization, and access tools for retrieval and visualization for querying and analysis.

Its architectural models such as staging areas for data preparation, hub-and-spoke configurations for departmental access, and sandboxes for exploratory analytics address different organizational needs to support complex queries, multidimensional online analytical processing (OLAP) analysis, and online transaction processing (OLTP). Enhancements like columnar databases, in-memory data stores, and cloud-based warehousing improve access speed, scalability, and flexibility, while data virtualization provides a unified, real-time data view for agile analysis.

3 Practical Use Cases of Data Warehousing:

  • Inventory Management: Consolidating inventory data from multiple sources into a centralized data warehouse enables real-time tracking and trend analysis to optimize stock levels and supply chain efficiency. This centralization improves demand forecasting, reduces overstock or shortages, and lowers operational costs.
  • Customer Insights and Segmentation: Aggregating customer data in a data warehouse provides a unified view for advanced analytics to segment customers and tailor marketing strategies. As a result, data warehousing enhances personalization, increases customer retention, and improves revenue generation.
  • Financial Reporting: Storing financial data in a structured warehouse aggregates, reconciles, and analyzes financial statements. This results in faster, more accurate reporting, improved compliance, and better-informed decision-making for financial planning and risk management.

Startup to Watch: Evidentli

Australian startup Evidentli utilizes its proprietary technology, Piano, to cleanse, aggregate, and harmonize real-world clinical data. Its data automation feature leverages advanced algorithms in machine learning and NLP to process diverse data sources, while ensuring reliability and data security through purpose-built data warehousing. Further, Piano’s insights provide a workbench for clinicians and researchers that supports deep tech capabilities, drag-and-drop functionality, and API-integration with third-party tools for clinical analytics and AI development.

Another solution, the EvidenceHub platform enables transparent sharing of research methods and medical knowledge using open standards, without requiring data sharing. The company’s solutions focus on quality and safety, population health, research, and learning health systems to monitor compliance, automate clinical audits, and design effective health services.

9. Machine Learning

Machine learning integrates with big data and analytics to provide the computational power needed to extract meaningful insights from vast datasets. As big data refers to large volumes of information, machine learning relies on such data for training models to make accurate predictions and decisions. High-performance computing (HPC) and cloud technologies offer distributed environments, automated data processing, and scalable architectures essential to improve machine learning algorithms and analytics in real-time, data-intensive applications.​

3 Practical Use Cases of Machine Learning:

  • Personalized Healthcare Recommendations: Machine learning analyzes patient data from diverse sources to provide personalized treatment recommendations based on medical history, genetic information, and lifestyle factors. As a result, it improves patient outcomes, supports preventive care, and enables precision medicine.
  • Chatbot NLP: By leveraging advanced machine learning models, chatbots understand and respond to user queries in real-time, enhancing customer interactions and providing accurate information. This improves customer support, operational efficiency, and 24/7 availability.
  • Predictive Maintenance in Manufacturing: Machine learning processes equipment sensor data to identify patterns that predict machine failures for maintenance before issues occur. This reduces downtime, lowers maintenance costs, and optimizes production schedules.

Startup to Watch: Precise Big Data

Taiwanese startup Precise Big Data develops AI-based healthcare technologies, including AI-Screening and BD-CARdio to enhance cardiovascular disease diagnosis and management. AI-Screening uses mobile AI to label cardiovascular diseases, assist doctors with analysis reports, and assess relationships between daily habits and cardiovascular health.

BD-CARdio provides real-time electrocardiogram (ECG) analysis for cardiovascular diseases and generates detailed reports to support clinical decision-making. The system uses machine learning to provide insights quickly, automate operational workflows, and support clinical validation for medical professionals. Features include access to ECG values, vascular embolization indices, and cardiovascular risk assessments via an app interface.

10. NoSQL Databases

NoSQL databases handle diverse data models such as key-value stores, document stores, column-family stores, and graph databases which assist in managing unstructured, semi-structured, and structured data at scale. Key-value stores support fast retrieval of data pairs, document stores manage semi-structured data formats, column-family stores handle time-series data and logs, and graph databases map complex relationships for applications like social networks and recommendation engines.

These databases allow data distribution across multiple servers to accommodate growing volumes and traffic. Additionally, they offer high performance with low latency, cost-effective deployment on commodity hardware, and high availability through built-in fault tolerance. Advantages include real-time data processing, IoT data management, machine learning workloads, and large-scale analytics.

3 Practical Use Cases of NoSQL Databases:

  • Real-Time User Data: Storing and accessing user behavior and session data in a schema-less structure allows for low-latency reads and writes while supporting real-time personalization and enhancing the user experience. Further, this scalability and access enable applications for higher engagement and retention.
  • IoT Data Management: NoSQL databases handles a high volume and variety of IoT-generated data streams to store, process, and analyze unstructured data in near real-time. This offers efficient data storage, improved device monitoring, and rapid response to environmental changes.
  • Healthcare Record Management: Managing diverse and complex healthcare records, including unstructured notes and images, through NoSQL databases ensures scalable data storage and quick retrieval for patient care. This leads to more accurate medical decision-making, streamlined access to patient history, and efficient healthcare delivery.

Startup to Watch: WeaveDB

UAE-based startup WeaveDB provides a streamlined platform for Web3 development to offer JSON APIs for database queries, cross-chain authentication, and a JSON utility language for data validation and access control. Its technology auto-signs transactions with disposal keys to eliminate wallet popups and allow instant processing. Smart contracts leverage built-in data indexing for efficient queries and scheduled tasks managed through cron jobs without manual transactions. Web console simplifies deployment to build complex logic without writing smart contract code directly. Thus, the company allows Web3 dapps to handle off-chain computations while ensuring data ownership remains with users.

The Impact of Big Data & Analytics

Patents, Grants & Investment Landscape

Despite recent challenges, the big data & analytics technologies are cited in over 146 000 patents and 19 900+ grants supporting research and development efforts. Investment activity is strong with an average investment value of USD 19.2 million per round, involving over 11 700 investors and more than 61 500 funding rounds closed.

The top five investors are Y Combinator, Techstars, Antler, Intel Ignite and Alumni Ventures. The leading funding types include Seed, Early Stage VC/Series A, Pre-Seed, Angel, and Venture Round, with the main industries being software development, financial services, marketing, e-learning, and healthcare.

Global Footprint

The top country hubs are the USA, India, UK, Germany, and Canada, while London, New York City, San Francisco, Bangalore, and Singapore emerge as leading city hubs.

Leverage Emerging Big Data & Analytics Technologies

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