Challenges of Conventional Systems

Conventional RDBMS are

• Designed to handle well structured data
• Traditional storage vendor solutions are very expensive
• Shared block-level storage is too slow
• Schema-on-write requires data be validated before it can be written to disk.
• Software licenses are too expensive
• Get data from disk and load into memory requires application

Conventional RDBMS have various limitations and other challenges as mentioned below,

• It cannot work on unstructured data efficiently
• It is built on top of the relational data model
• It is batch oriented and we need to wait for nightly
• ETL (extract, transform and load) and transformation jobs to complete before the required insight is obtained
• Parallelism in a traditional analytics system is achieved through costly hardware like MPP(Massively Parallel Processing) systems
• Inadequate support of aggregated summaries of data

Data Challenges with Conventional RDBMS

• Volume, Velocity, Variety & Veracity
• Data discovery and comprehensiveness
• Scalability
• Storage issues

Process Challenges with Conventional RDBMS

• Capturing data
• Aligning data from different sources
• Transforming data into suitable form for data analysis
• Modeling data(mathematically,simulation)
• Understanding output, visualizing results and display issues on mobile devices

Management Challenges with Conventional RDBMS

• Security
• Privacy
• Governance
• Ethical issues