Big data refers to large, complex datasets that come in a variety of formats from multiple sources. It is characterized by high volume, velocity, and variety of data, as well as additional Vs like veracity, value, and variability. Managing big data presents numerous challenges around storage, processing speed and volume, data integration and quality, extracting useful insights, privacy and security, scalability, and costs. Skills and tools are required to govern, manage, and analyze big data effectively.

2. Big Data
data that contains greater variety,
arriving in increasing volumes and
with more velocity.
This is also known as the three Vs.
Put simply, big data is larger, more
complex data sets, especially from
new data sources.

3. Six V's of big data (value, volume, velocity,
variety, veracity, and variability), which also apply
to health data.

4. Sources of Big Data

5. Objective of Big Data

6. Big Data Technologies

7. Data Warehouse vs Big Data

8. Chalenges of Big Data
 **Volume:** Managing and storing massive amounts of data
can be challenging. Big Data often exceeds the capacity of
traditional databases and requires specialized storage and
processing infrastructure.
 **Velocity:** Dealing with the speed at which data is generated
and needs to be processed in real-time or near real-time.
Quick data ingestion, processing, and analysis become critical
for time-sensitive applications.
 **Variety:** Big Data comes in various formats, including
structured, semi-structured, and unstructured data. Handling
this diversity requires flexible data processing and storage
techniques.

9. Chalenges of Big Data
Veracity: Ensuring the quality and accuracy of Big Data can be difficult,
especially when dealing with data from multiple sources. Data validation,
cleansing, and integration are essential to maintain data integrity.
Value: Extracting meaningful insights from Big Data requires advanced
analytics and machine learning techniques. Finding valuable patterns
and relationships within the data can be complex.
Privacy and Security: With vast amounts of sensitive data being
collected, protecting user privacy and ensuring data security becomes a
critical challenge. Safeguarding data against unauthorized access and
potential breaches is essential.
Scalability: As data volumes grow, the systems and infrastructure must
scale accordingly to handle increased demands for storage, processing,
and analysis.

10. Chalenges of Big Data
Cost: The infrastructure, tools, and personnel required for Big Data
projects can be expensive. Organizations need to carefully manage
costs while maximizing the value derived from Big Data investments.
Interoperability: Integrating and making sense of data from diverse
sources can be complex, particularly when dealing with legacy systems
and different data formats.
Skill Gap: There is a shortage of skilled data professionals who possess
the expertise to handle Big Data technologies and perform advanced
analytics.
Ethical Considerations: Using Big Data for decision-making raises ethical
concerns, particularly regarding potential biases and discrimination in
algorithms and data usage.
Regulatory Compliance: Compliance with data protection and privacy
regulations becomes more challenging as data volumes increase, and
data is shared across borders.
Data Governance: Establishing proper data governance practices is

11. Type of Data
• Structured Data: Any data that can be processed, is easily accessible, and can be
stored in a fixed format is called structured data.
• Unstructured Data: Unstructured data in Big Data is where the data format
constitutes multitudes of unstructured files (images, audio, log, and video). Google
Search’ or ‘Yahoo Search.’
• Semi-structured Data: In Big Data, semi-structured data is a combination of both
unstructured and structured types of data. This form of data constitutes the
features of structured data but has unstructured information that does not adhere
to any formal structure of data models or any relational database. Some semi-
structured data examples include XML

12. Big Data Impacting Enterprise Systems

13. Analytics Life Cycle

14. Analytics Life Cycle
1.Netflix
2.Amazon
3.Uber