Greatest challenges that prevent business from
capitalizing on big data
➢ Obtaining executive sponserships for investments in big data and its related
activities( such as training etc)
➢ Getting the business units to share information across organizational tower
or pit
➢ Finding the right skills (business analysts and data scientists)
➢ Determining the approach to scale rapidly and elastically
➢ Deciding whether to use structured or unstructured, internal or external data
to make business decisions
➢ Choosing the optimal way to report findings and analysis of big data for the
prsentations to make more sense
➢ Determining what to do with the insights created from big data
, Top challenges facing Big data
➢ Scale: Should you scale vertically or horizontally ?
➢ Security: Most of big data platforms have poor security mechanisms
➢ Schema: Rigid schemas have no place. The need of the hour is dynamic
schema
➢ Continuous Availability: How to provide 24/7 support
➢ Consistency: Should one opt for consistency or eventual consistency?
➢ Partition tolerant: How to build partition tolerant systems that can take
care of both hardware and software failures?
➢ Data quality: how to maintain accuracy, completeness, timeliness etc
Assignment: Why is big data analytics important ?
, Termnologies used in Bid Data Environments
1. In-Memory Analytics:
➢ In-memory analytics is a business intelligence (BI) methodology used to
solve complex and time-sensitive business scenarios.
➢ It works by increasing the speed, performance and reliability when querying
data.
➢ Business Intelligence deployments are typically disk-based, meaning the
application queries data stored on physical disks. In contrast, with in-memory
analytics, the queries and data reside in the server's random access memory
(RAM)
➢ In-memory analytics helps improve the overall speed of a BI system and
provides business-intelligence users with faster answers compared to
traditional disk-based business intelligence, especially for queries that take a
long time to process in a large database.
capitalizing on big data
➢ Obtaining executive sponserships for investments in big data and its related
activities( such as training etc)
➢ Getting the business units to share information across organizational tower
or pit
➢ Finding the right skills (business analysts and data scientists)
➢ Determining the approach to scale rapidly and elastically
➢ Deciding whether to use structured or unstructured, internal or external data
to make business decisions
➢ Choosing the optimal way to report findings and analysis of big data for the
prsentations to make more sense
➢ Determining what to do with the insights created from big data
, Top challenges facing Big data
➢ Scale: Should you scale vertically or horizontally ?
➢ Security: Most of big data platforms have poor security mechanisms
➢ Schema: Rigid schemas have no place. The need of the hour is dynamic
schema
➢ Continuous Availability: How to provide 24/7 support
➢ Consistency: Should one opt for consistency or eventual consistency?
➢ Partition tolerant: How to build partition tolerant systems that can take
care of both hardware and software failures?
➢ Data quality: how to maintain accuracy, completeness, timeliness etc
Assignment: Why is big data analytics important ?
, Termnologies used in Bid Data Environments
1. In-Memory Analytics:
➢ In-memory analytics is a business intelligence (BI) methodology used to
solve complex and time-sensitive business scenarios.
➢ It works by increasing the speed, performance and reliability when querying
data.
➢ Business Intelligence deployments are typically disk-based, meaning the
application queries data stored on physical disks. In contrast, with in-memory
analytics, the queries and data reside in the server's random access memory
(RAM)
➢ In-memory analytics helps improve the overall speed of a BI system and
provides business-intelligence users with faster answers compared to
traditional disk-based business intelligence, especially for queries that take a
long time to process in a large database.
