1. Data Ingestion: Use tools and frameworks to ingest data from various sources such as lab equipment, databases, and external data providers. 2. Data Storage: Utilize scalable storage solutions, such as cloud-based platforms, to store large volumes of raw data. 3. Data Processing: Employ processing frameworks like Apache Hadoop or Spark to clean, transform, and analyze the data. 4. Data Governance: Implement policies and tools for data quality, security, and compliance to ensure the integrity and confidentiality of the research data. 5. Data Access: Provide APIs and interfaces for easy data retrieval and analysis by researchers and other stakeholders.
