Top 5 Challenges for Hadoop MapReduce in the Enterprise
IBM sponsored content at Datanami.com lists these challenges for Hadoop MapReduce in enterprise settings:
- Lack of performance and scalability….
- Lack of flexible resource management….
- Lack of application deployment support….
- Lack of quality of service assurance….
- Lack of multiple data source support….
Who would know enterprise requirements better than IBM? They have been in the enterprise business long enough to be an enterprise themselves.
If IBM says these are the top 5 challenges for Hadoop MapReduce in enterprises, it’s a good list.
But I don’t see “semantics” in that list.
Do you?
Semantics make it possible to combine data from different sources, process it and report a useful answer.
Or rather understanding data semantics and mapping between them makes a useful answer possible.
Try pushing data from different sources together without understanding and mapping their semantics.
It won’t take long for you to decide which way you prefer.
If semantics are critical to any data operation, including combining data from diverse sources, why do they get so little attention?
Doubt your IBM representative would know but you could ask them, while trying out the IBM solution to the “top 5 challenges for Hadoop MapReduce:”
How you should discover and then map the semantics of diverse data sources?
Having mapped them once, can you re-use that mapping for future projects with the IBM solution?