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Web-based data analysis environments are powerful platforms for exploring large data sets. To ensure that these environments meet the needs of analysts, a human-centered design perspective is needed. Interfaces to these platforms should provide exible search, support user-generated content, and enable collaboration. We report on our eorts to design and develop a web interface for a custom analytics platform| EPIC Analyze| which provides interactive search over large Twitter data sets collected during crisis events. We performed seven think-aloud sessions with researchers who regularly analyze crisis data sets and compiled their feedback. They identied a need for a\big picture" view of an event, exible querying capabilities, and user-dened coding schemes. Adding these features allowed EPIC Analyze to meet the needs of these analysts and enable exploratory research on crisis data. In performing this work, we identied an opportunity to migrate the software architecture of EPIC Analyze to one based on microservices. We report on the lessons learned in performing this migration and the impact it had on EPIC Analyze's capabilities. We also re ect on the benets a microservices approach can have on the design of data-intensive software systems like EPIC Analyze.