In today’s data-driven world, the ability to effectively collect, process, analyze, and visualize information is a critical skill across all sectors, especially in cutting-edge research environments like the ENSURE-6G project. ENSURE-6G Event #4: Workshop on Research Methods and Open Science Skills Development – Day #1 delved into this crucial area with a session led by Dr. Engin Zeydan from the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain, focusing on “Data Visualization and Communication of Results.”
The session emphasized that simply generating data isn’t enough; the true value lies in how that data is transformed into actionable insights and effectively communicated to diverse audiences. Dr. Zeydan’s presentation provided a comprehensive overview of the data engineering ecosystem, highlighting its indispensable role in supporting data science and AI-driven applications.
The Six Pillars of Data Engineering
Dr. Zeydan outlined six key frameworks that form the backbone of a robust data engineering pipeline [09:35]:
- Data Connection: This initial stage focuses on connecting to various data sources, whether they are APIs, streaming data, or local files [11:38]. Tools like Apache Flume can be used to track log files and ingest changes into a database [13:07].
- Data Ingestion: Once connected, data needs to be ingested into a temporary holding area, often a message queue or buffer zone, to enable replication and partitioning across nodes and allow multiple subscribers to access it. Apache Kafka is a prominent tool in this space, handling hundreds of megabytes of reads and writes simultaneously [17:25].
- Data Analysis and Processing: This is where the core AI and machine learning tasks occur. Dr. Zeydan discussed different types of analytics—descriptive, diagnostic, predictive, and prescriptive—each offering increasing value but also complexity [23:48]. Tools like Apache Spark Flink and Apache Beam are crucial for real-time stream processing and enabling SQL queries on streaming data [26:53].
- Data Storage: After analysis, data needs to be stored efficiently. This can range from traditional relational databases (like MySQL) for structured data to data warehouses for business intelligence, and increasingly, data lakes for storing large amounts of raw, unstructured, and semi-structured data using NoSQL or NewSQL databases and Hadoop clusters [29:12].
- Data Monitoring and Visualization: This is where the fruits of data engineering become visible. Frameworks like Kibana (from the Elasticsearch stack), Grafana, Apache Superset, and Streamlit allow researchers to uncover relationships, discover trends, and create real-time dashboards [32:31]. Kibana, for instance, provides immediate visualization of data pushed through a pipeline, making it valuable for business intelligence [33:50].
- Data Orchestration and Management: The final, yet equally critical, framework involves connecting and managing all these disparate components and pipelines seamlessly [35:23]. Tools like Apache Airflow enable the creation and management of complex workflows with hundreds or thousands of pipelines, ensuring operational efficiency and providing insights into system health [36:01].
Why Data Engineering Matters for Researchers
Dr. Zeydan highlighted that the surge in data engineering’s importance is driven by several factors, including the availability of large datasets, low-cost storage, powerful processors, distributed computing, and advancements in analytical techniques like machine learning and AI [08:12]. For researchers, mastering these frameworks means:
- Handling Big Data: Efficiently managing ever-growing volumes of diverse data.
- Enabling Real-time Insights: Processing streaming data to make immediate decisions or observations.
- Streamlining Workflows: Automating complex data pipelines from collection to visualization.
- Improving Communication: Presenting complex research findings in clear, understandable, and impactful visualizations.
MLflow: A Case Study in Model Management
During the discussion, an important point was raised about MLflow, a tool for managing machine learning lifecycles, which complements the data engineering ecosystem [42:53]. MLflow serves as a global model repository, allowing researchers to:
- Version Control Models: Keep track of different versions of trained models.
- Manage Experimentation: Monitor and compare various machine learning experiments.
- Deploy Models via API: Make models readily available for other modules or applications, such as an orchestrator in a 6G network, ensuring seamless integration and use in production or testing environments.
This capability is particularly vital in collaborative research projects like ENSURE-6G, where multiple work packages might produce different models that need to interact effectively.
Conclusion
Dr. Engin Zeydan’s session on data visualization and communication of results provided a comprehensive roadmap for navigating the complexities of modern data ecosystems. By understanding and implementing these data engineering frameworks, researchers within the ENSURE-6G project and beyond can not only advance their technical capabilities but also ensure their groundbreaking work is effectively translated into tangible insights and impactful communication.
Watch the full session here:
