Industrial organisations involved:

  • Analytical companies specializing in data analysis and interpretation.
  • Cloud service providers offering platforms for cleaning and enriching experimental big data.
  • Agricultural companies leveraging sensor technology for monitoring soil and meteorological factors.

Technical/scientific Challenge:

Integration Complexities and Ensuring Data Reliability and Security in Supercomputer-driven Augmented Big Data Systems Using AI and HPC Technologies

In the expansive landscape of big data, integrating diverse data sources into supercomputer systems poses significant hurdles. As torrents of data pour in from IoT devices, social media platforms, customer interactions, and transactions, establishing a unified framework for data aggregation becomes paramount. The immense volume and speed of data demand advanced analytics for real-time insights, leveraging technologies such as machine learning and predictive analytics. Augmented Data Quality mechanisms play a critical role in ensuring data reliability by identifying and rectifying discrepancies. Addressing privacy and security concerns is essential, with regulations like GDPR and CCPA mandating robust security measures. Augmented big data solutions must merge advanced analytics, data quality assurance, and security protocols, harnessing AI, HPC, and HPDA technologies. This comprehensive approach unlocks the full potential of big data for informed decision-making, driving competitive advantage in today’s data-driven era.


Enhancing Augmented Big Data Quality for Supercomputer Data Processing

The solution for enhancing augmented big data quality for supercomputer data processing lies in deploying a comprehensive ecosystem of technologies and tools. By leveraging Apache Griffin for data quality assurance, organizations ensure data reliability and integrity through automated validation processes and quality standards. Apache Spark, renowned for its real-time data processing capabilities, empowers organizations to analyze vast datasets swiftly and efficiently. TensorFlow, on the other hand, provides advanced analytics through deep learning, allowing for complex data analysis and pattern recognition. Together, these tools enable organizations to derive valuable insights from their data, facilitating informed decision-making and strategic planning. Additionally, MLlib offers scalable machine learning capabilities, enabling organizations to apply diverse machine learning algorithms to their data, further enhancing analysis and prediction accuracy. Apache Kafka plays a crucial role in this ecosystem by facilitating real-time data streaming, enabling organizations to build efficient data pipelines and process streaming data in real-time. This ensures that organizations can harness the full potential of their data, capturing insights as they occur and enabling timely decision-making. Lastly, Apache Ranger provides robust data security and governance features, ensuring compliance with regulations such as GDPR and CCPA. By enforcing data access policies and protecting sensitive information, Apache Ranger enhances data security and instills trust in the data. This integrated approach empowers organizations to address the challenges of data integration, processing, and governance effectively, driving competitive advantage in today’s data-driven landscape.

Buisnes impact:  

  • Informed Decision Making: High-quality data enables informed decisions, driving organizational growth and success.
  • Enhanced Efficiency: Augmented data quality streamlines operations, reducing errors, and improving overall efficiency.
  • Data-Driven Transformation: Augmented data quality empowers organizations to become data-driven, fuelling strategic initiatives and maintaining competitiveness.
  • Increased Automation: Automated data quality tasks boost productivity, minimizing errors and allowing teams to focus on value-added activities.
  • Sustainability: Scalable data quality solutions ensure adaptability to evolving data needs, enabling sustainable growth and resilience.


  • Unified Data Analysis: Augmented data quality facilitates a consolidated view of diverse data sources, simplifying analysis and enabling more comprehensive insights.
  • Improved Decision-Making: Access to accurate and reliable data enhances decision-making processes, leading to better outcomes and strategic planning.
  • Enhanced Operational Efficiency: Streamlined data processing workflows and automated tasks improve operational efficiency, reducing manual effort and time.
  • Scalable Solutions: Augmented data quality solutions offer scalability to handle increasing data volumes and evolving business needs, ensuring long-term viability.
  • Enhanced Data Reliability: Mechanisms for data validation and cleansing ensure data reliability, fostering trust in organizational data assets.
  • Future-Proofing: Integration with advanced technologies like AI and HPC future-proofs data processing capabilities, enabling organizations to stay ahead in the rapidly evolving landscape.

Success story # Highlights:

  • Keywords: Augmented, Big Data, Supercomputer, Data processing
  • Industry sectorAnalytical Companies, Cloud Service Providers, Agricultural Companies
  • Technology: Apache Kafka, Apache Ranger, Apache Griffin, Apache Spark, TensorFlow, MLib

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Türkiye, Republic of North Macedonia, Iceland, Montenegro, Serbia