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EVOLUTION
OF BIG DATA
Shilpa krishna
Research Scholar
PATH TO BIG DATA
DATA MANAGEMENT
PROFESSIONAL
DATA ENGINEER
BUSINESS ANALYST
MACHINE LEARNING
RESEARCHER
DATA ORIENTED PROFESSIONAL
BIG DATA PIPELINE IN
DEPTH
Components of a big data pipeline are :
• The messaging system
• Message distribution support to various nodes for
further data processing.
• Data analysis system to derive decisions from data.
• Data storage system to store results and related
information.
• Data representation and reporting tools and alerts
system.
Important parameters that a big data pipeline
system must have -
• Compatible with big data
• Low latency
• Scalability
• Flexibility
• Economic
• A diversity that means it can handle various use cases.
In a Big Data pipeline system, the
two core processes are -
• The messaging system
• The data ingestion process
LAMDA ARCHITECTURE
Batch Layer
Serving Layer
Incoming
data Queries
Real time Layer
Data Storage Layer
Batch
Engine
Real Time
Engine
Serving
Backend
Historical data Result data
KAPPA ARCHITECTURE
Serving Layer
Real time Layer Query
Streaming data
Query
Stream
process
Real time
views
Real time
views
BIG DATA
VISUALIZATION
• Review large amounts of data
• Spot trends
• Identify correlations and unexpected relationships
• Present the data to others
Big data visualisation techniques offer a fast
and effective way to :
Various Big Data Visualisation examples
include :
1. Linear
2. 2D/Planar/Geospatial
3. 3D/Volumetric
4. Temporal
5. Multidimensional
6. Tree/hierarchical
BIG DATA PRIVACY
Big data privacy risks
1. Data breaches
2. Data brokerage
3. Data discrimination
Best practices for maintaining the
privacy of big data -
1. Employ real-time Monitoring
2. Implement homomorphic encryption
3. Avoid collecting too much data
4. Prevent internal threats
THANK YOU FOR WATCHING

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Evolution of big data