Big_Data_Lecture_Presentation for dummies

Introduction to Big Data:
Ecosystem, Tools, and
Applications
Faculty Development Program
Presented by: [Your Name]
Institution: [Your Institution Name]
Date: [Insert Date]

Objectives of the Session
• - Understand the concept and characteristics
of Big Data
• - Explore the Big Data ecosystem and
architecture
• - Learn about key tools and technologies
• - Examine real-world examples and use cases
• - Discuss applications in education, industry,
and research

What is Big Data?
• - Refers to datasets too large/complex for
traditional data tools
• - Sources: Social media, sensors, web data,
etc.
• - Goal: Extract meaningful insights and trends

5 Vs of Big Data
• 1. Volume: Massive amounts of data
• 2. Velocity: Speed of data
generation/processing
• 3. Variety: Different data types
• 4. Veracity: Quality and accuracy
• 5. Value: Insights extracted

Big Data vs Traditional Data
• Traditional Data vs Big Data
• - GBs to TBs vs TBs to ZBs
• - Structured vs Structured + Unstructured
• - Batch processing vs Real-time + Batch
• - RDBMS vs Hadoop, Spark, etc.

Big Data Architecture
• 1. Data Sources: Web, IoT, Apps, Logs
• 2. Ingestion Layer: Kafka, Flume, Sqoop
• 3. Storage Layer: HDFS, NoSQL
• 4. Processing Layer: Spark, MapReduce
• 5. Analytics Layer: Hive, Pig
• 6. Visualization: Tableau, Power BI

Big Data Ecosystem Overview
• - Storage: HDFS, Amazon S3
• - Processing: Hadoop, Spark
• - Streaming: Kafka, Flink
• - NoSQL: MongoDB, Cassandra
• - Analytics: Hive, MLlib
• - Visualization: Tableau, Power BI

Important Big Data Tools
• - Storage: HDFS, S3
• - Batch: MapReduce, Spark
• - Streaming: Kafka, Flink
• - Querying: Hive, Pig
• - ML: Mahout, Spark MLlib
• - Visualization: Tableau, Kibana

Real-World Examples
• - Healthcare: Patient monitoring
• - Retail: Amazon recommendations
• - Banking: Fraud detection
• - Transportation: Uber optimization
• - Education: Performance analytics

Applications in Higher Education
• - Student dropout prediction
• - Curriculum personalization
• - Research trend analysis
• - Smart campus initiatives
• - Data-driven administration

Challenges in Big Data
• - Privacy and security
• - Skill gaps
• - Integration of diverse data
• - Ensuring data quality

Career Opportunities
• - Big Data Engineer
• - Data Scientist
• - ML Engineer
• - Data Analyst
• - Cloud Architect

Summary
• - Big Data enables smarter decisions
• - The ecosystem is evolving
• - Applications are cross-domain
• - Embrace Big Data in teaching, research,
administration

Q&A
• Let’s Discuss Your Thoughts and Questions!

Big_Data_Lecture_Presentation for dummies

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