Challenges of the conventional

1. Challenges of Conventional Systems

2.  ‘Analytics' has been used in the business
intelligence world to provide tools and
intelligence to gain insight into the data
 Data mining is used in enterprises to keep pace
with the critical monitoring and analysis of
mountains of data
 How to unearth all the hidden information
through the vast amount of data ?
Challenges of Conventional Systems

3. obtained
 Parallelism in a
achieved through
traditional
costly
analytics system is
hardware like MPP
(Massively Parallel Processing) systems
 Inadequate support of aggregated summaries of data
Common Challenges
 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
Challenges of Conventional Systems

4. • Volume, Velocity, Variety & Veracity
• Data discovery and
comprehensiveness
• Scalability
• Storage issues
Data
Challenges
• 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
Process
Challenges
Challenges of Conventional Systems

5. • Security
• Privacy
• Governance
• Ethical issues
Management
Challenges
Challenges of Conventional Systems

6. • Designed to handle well structured
data
• traditional storage vendor solutions
are very expensive
• shared block-level storage is too slow
• read data in 8k or 16k block size
• 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
Traditional
/ RDBMS
Challenges of Conventional Systems

7. Solution
constraints
• Inexpensive storage
• A data platform that could handle large
volumes of data and be linearly scalable
at cost and performance
• A highly parallel processing model that
was highly distributed to access and
compute the data very fast
• A data repository that could break down
the silos and store structured, semi-
structured, and unstructured data to
make it easy to correlate and analyze the
data togethe
Solution is…