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1. big data analytics : tools
and applications
Presented by: punit
Roll no. 241306098

2. WHAT IS BIG DATA ANALYTICS
 Big data analytics refers to the systematic processing and analysis of large
amounts of data and complex data sets, known as big data, to extract valuable
insights.
 Big data analytics allows for the uncovering of trends, patterns and
correlations in large amounts of raw data to help analysts make data-
informed decisions.
 goal of big data analytics is to extract meaningful information from vast amounts
of data generated from various sources, including social media, sensors,
transactions, and more.

3. Importance of big data analytics
 Competitive Advantage: Organizations leveraging big data analytics can gain
a competitive edge. They can better understand their market, customers, and
competitors, allowing them to innovate faster and respond more effectively
 Real-Time Analytics: With the ability to process data in real-time, companies
can make immediate decisions based on current information. This is crucial
for industries where timely responses are essential, such as finance,
healthcare, and retail.
 Enhanced Supply Chain Management: Big data analytics provides visibility
across the supply chain, allowing companies to optimize logistics, manage
inventory more effectively, and improve supplier relationships.
 Risk Management: Analytics can help in identifying potential risks and fraud
by detecting unusual patterns and anomalies in data.

4. Types OF BIG DATA ANALYTICS
STRUCTURED DATA
 Structured data refers to highly
organized and easily searchable
data that resides in fixed fields
within a record or file.
 Simplifies data entry, searching
and analysis.
 Can be easily queried using
straightforward database languages
like SQL.
UNSTRUCTURED DATA
 Unstructured data lacks a pre-
defined data model, making it
more difficult to collect, process
and analyze.
 Data that lacks a predefined format
or structure.
 Requires specialized techniques
such as natural language processing,
machine learning, and data mining
for meaningful insights.

5. Tools of big data analytics
 Hadoop
 Think of Hadoop as a giant storage warehouse where you can keep all your data. It
helps store and process huge.
 amounts of information across many computers working together.Hadoop is an
open-source framework that allows for the distributed processing of large data sets
across clusters of computers.
 HDFS (Hadoop Distributed File System): Stores large amounts of data across
multiple machines.

6.  Apache spark :
 Apache Spark is like a super-fast calculator. It processes data quickly by
keeping it in memory (RAM) rather than fetching it from slower storage.
 Apache Spark is an open-source distributed computing system that provides
an interface for programming entire clusters with implicit data parallelism
and fault tolerance.
 In-Memory Processing: Processes data in RAM instead of disk, making it faster
than Hadoop.
 Supports Multiple Languages: Works with Java, Scala, Python, and R.

7. Applications of big data analytics
 Smart Traffic Systems:
Data Collection:
• Traffic conditions are monitored using Cameras at road entries
and exits.
• GPS devices in vehicles (e.g., Ola, Uber).
Traffic Analysis: Data is analyzed to recommend:
• Jam-free or less congested routes.
• Solutions to reduce fuel consumption.

8.  Finance :
 Fraud Detection: Identifying unusual patterns and anomalies in financial
transactions.
 Risk Management: Assessing risks and making informed investment decisions.
 Customer Insights: Understanding customer behavior to offer personalized
financial products.
 Healthcare :
 Personalized Medicine: Analyzing patient data to tailor treatments to
individual needs.
 Predictive Analytics: Predicting disease outbreaks and patient admissions.
 Operational Efficiency: Streamlining hospital operations and improving
patient care.

9. Thank you
Do you have any questions ?
punitjangir@gmail.com