This panel discussion will highlight use cases where LabKey is used to centralize, align and analyze data from Benchling and other disparate sources. Panelists will discuss their area of research, their needs for data centralization that brought them to LabKey, best practices and future plans for their Benchling/LabKey integration.
The presentation and panel discussion focused on the integration of LabKey and Benchling for enhanced data management and analysis within scientific research. LabKey, a robust database and analytics platform, is designed to handle a wide variety of data types, including administrative and assay data. It supports connections to numerous relational databases such as Postgres, MySQL, Oracle SQL Server, etc., through external data source mechanisms. This capability allows institutions to amalgamate data from different sources into a unified system for comprehensive analysis and reporting.
A key feature discussed was LabKey’s ability to connect to external databases using JDBC (Java Database Connectivity), allowing for a seamless view and access to diverse databases for authorized users. LabKey’s built-in facilities enable administrators to expose specific schemas within the platform, securing data access while offering rich functionality for data visualization, analysis, and the creation of custom queries and reports. The flexibility and ease of configuration underscore LabKey’s utility in rapidly connecting to any institutional database, emphasizing its role in data integration efforts.
However, the integration process is not without its challenges, particularly in querying across multiple databases. The presentation addressed these limitations by introducing Extract, Transfer, Load (ETL) processes as a solution for effectively copying data from external schemas into LabKey datasets. This approach enables the merging of disparate data sources into a cohesive dataset within LabKey, facilitating complex data analysis and visualization tasks.
The panel discussion featured insights from researchers and scientists at various institutions, who shared their experiences with integrating Benchling and LabKey into their workflows. They highlighted their research focuses, the integration challenges they faced, and how the combined power of LabKey and Benchling addressed their specific data management needs. Technical aspects of the integration, such as setting up external schemas and utilizing ETLs for data flow, were discussed alongside best practices for successful implementation.
In summary, the conversation underscored the strategic importance of harmonizing LabKey and Benchling for data management in scientific research. By leveraging these platforms, institutions can enhance their data analysis capabilities, overcome integration challenges, and pave the way for future advancements in data-driven research methodologies. The panelists’ experiences and insights provided valuable lessons on navigating the complexities of data integration, offering a roadmap for others looking to optimize their research data management strategies.