The field of data management and analytics is witnessing significant developments, driven by the need to efficiently handle complex, heterogeneous data from multiple sources. Researchers are focusing on creating innovative solutions to integrate and analyze large datasets, with a particular emphasis on semantic infrastructures, data lakehouse formats, and multimodal data storage and retrieval. Noteworthy papers in this area include: A Knowledge Graph Informing Soil Carbon Modeling, which introduces a semantic infrastructure to transform agricultural research data into a queryable knowledge representation. A Comparative Study of Delta Parquet, Iceberg, and Hudi for Automotive Data Engineering Use Cases, which presents a comparative analysis of modern data lakehouse formats for real-world time-series automotive telemetry data. Multimodal Data Storage and Retrieval for Embodied AI: A Survey, which systematically evaluates storage architectures and retrieval paradigms for embodied AI agents. Query Logs Analytics: A Systematic Literature Review, which provides a comprehensive overview of log usage and sheds light on promising directions for future research. A DBMS-independent approach for capturing provenance polynomials through query rewriting, which presents a query rewriting-based approach for annotating SQL queries with provenance polynomials.