Big data

1. Overview of Big Data
Characteristics and
Technologies

2. Overview of Big Data Characteristics and Technologies
• Introduction to Big Data
• What is Big Data?
• Characteristics of Big Data - Overview
• Volume, Velocity, Variety
• Veracity and Value
• Sources of Big Data
• Introduction to Big Data Technologies
• Common Big Data Technologies
• Introduction to Data Analytics
• Types of Data Analytics

3. Overview of Big Data Characteristics and Technologies
• Data Processing Concepts
• ETL (Extract, Transform, Load)

4. Introduction to Big Data
• Data Explosion: The rapid growth of data due to
digital interactions exceeds traditional storage
and processing capacities.
• Digital Transformation: Organizations are
increasingly integrating big data analytics to
innovate and enhance operational efficiencies
across sectors.
• Modern Industry Relevance: Big data's
significance is paramount as industries seek data-
driven insights for competitive advantage and
decision-making.
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5. What is Big Data?
• Definition of Big Data: Big Data refers to vast datasets characterized by volume, velocity, and variety,
beyond traditional management.
• Traditional vs Big Data: Unlike traditional data, Big Data requires advanced technologies for real-time
processing and complex analytics.
• Application Examples: Big Data drives innovations in social media, e-commerce, and healthcare,
influencing user engagement and policy decisions.

6. Characteristics of Big Data - Overview
• Volume: Volume refers to the vast amounts of data generated daily, necessitating scalable storage
solutions.
• Velocity: Velocity emphasizes the speed at which data is generated and processed, impacting real-time
analytics.
• Variety: Variety highlights the diverse data types and formats, challenging organizations to integrate
information seamlessly.

7. Volume, Velocity, Variety
• Volume Examples: Scalable infrastructure is
essential for handling petabytes of data
generated by social media platforms daily.
• Velocity Impact: Streaming data from IoT devices
requires immediate processing to enable real-
time decision-making and insights.
• Variety Formats: Data originating from text,
images, and videos needs unified processing
frameworks for comprehensive analysis.
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8. Veracity and Value
• Veracity Explained: Veracity represents the accuracy of data, ensuring reliable insights and fostering trust
in analytics processes.
• Value of Insights: Value encompasses actionable insights derived from data, enhancing decision-making
and providing competitive market advantages.
• Trust and Analytics: Trustworthiness in data improves analytics effectiveness, with inaccuracies leading to
misguided business strategies and outcomes.

9. Sources of Big Data
• Internal Data Sources: Internal sources include enterprise systems like CRM and ERP that generate
structured data for analysis.
• External Data Sources: External data originates from social media, providing insights into customer
sentiment and market trends.
• IoT and Sensor Data: IoT devices and sensors continuously generate data streams critical for real-time
monitoring and decision-making.

10. Introduction to Big Data Technologies
• Traditional Platforms Limitations: Legacy data
platforms struggle to handle large data volumes
and do not support real-time analytics effectively.
• Distributed Computing Necessity: Modern data
technologies leverage distributed computing to
manage massive datasets across multiple servers,
ensuring scalability.
• Scalable Storage Solutions: Adopting cloud-
based storage models enables organizations to
dynamically scale resources, accommodating
data growth seamlessly.
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11. Common Big Data Technologies
• Hadoop Ecosystem: Hadoop includes HDFS for storage and MapReduce for distributed processing,
optimizing large dataset handling.
• Apache Spark: Spark offers in-memory processing capabilities, enhancing speed for iterative algorithms in
big data analytics.
• NoSQL Databases: Databases like MongoDB and Cassandra support flexible schemas, facilitating efficient
storage and retrieval of unstructured data.

12. Introduction to Data Analytics
• Data Analytics Function: Data analytics transforms massive datasets into insightful trends, enabling
informed decision-making for organizations.
• Identifying Trends: Advanced algorithms reveal patterns and correlations, driving strategic initiatives and
operational enhancements.
• Predictive Analysis: By leveraging historical data, analytics predicts future outcomes, aiding proactive
measures and competitive advantages.

13. Types of Data Analytics
• Descriptive Analytics: Descriptive analytics
summarizes historical data, providing insights
through visualizations; examples include sales
reports and customer behavior dashboards.
• Diagnostic Analytics: Diagnostic analytics
examines past performance to understand cause-
effect relationships; examples include root cause
analysis and anomaly detection.
• Predictive Analytics: Predictive analytics uses
statistical models to forecast future events;
examples include credit scoring and risk
assessment in financial services.
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14. Data Processing Concepts
• Data Collection: Data collection involves gathering raw data from varied sources, ensuring extensive
coverage for subsequent processing.
• Data Quality Importance: High data quality is crucial, as it directly impacts the reliability and accuracy of
insights derived.
• Decision-Making Insights: Effective analysis transforms cleaned and structured data into actionable
insights for informed strategic decisions.

15. ETL (Extract, Transform, Load)
• ETL Process Overview: ETL encompasses extraction, transformation, and loading of data into warehouses
or lakes for analytics.
• Key ETL Steps: Extraction gathers data from various sources, transformation modifies it into a suitable
format, and loading finalizes datasets.
• Common ETL Tools: Popular tools like Talend, Informatica, and Apache NiFi facilitate the ETL process with
advanced functionalities.