If the use case involves creating a data warehousing solution with complex functionality, data analytics, and extensive querying requirements, especially if there's a need for a data lake, then opting for Hadoop and Spark is advisable. In this scenario, Hadoop with technologies like Hive and Spark can provide the necessary capabilities. On the other hand, if the application's primary focus is on handling large volumes of data without the requirement for complex features, data warehousing, or advanced querying, and if dashboarding visibility is not a priority, then MongoDB could be a suitable choice. Overall, I would rate it nine out of ten.
Project Management Software is designed to streamline project tasks, enhance team collaboration, and ensure timely delivery of projects. These solutions offer comprehensive tools for planning, scheduling, resource allocation, and progress tracking. By centralizing project data, they help you optimize workflows and minimize inefficiencies.
Project Management Software solutions are developed to help you and your organization efficiently manage projects from inception to completion. These...
If the use case involves creating a data warehousing solution with complex functionality, data analytics, and extensive querying requirements, especially if there's a need for a data lake, then opting for Hadoop and Spark is advisable. In this scenario, Hadoop with technologies like Hive and Spark can provide the necessary capabilities. On the other hand, if the application's primary focus is on handling large volumes of data without the requirement for complex features, data warehousing, or advanced querying, and if dashboarding visibility is not a priority, then MongoDB could be a suitable choice. Overall, I would rate it nine out of ten.