Big Data Architecture Intro and its implementation in the insutry.

1. Introduction to Big
Data Architecture
Big data architecture is the framework for processing, managing, and
analyzing large and complex data sets. It involves various tools,
techniques, and infrastructure to handle the volume, velocity, and variety
of data in an efficient and cost-effective manner.

2. Key Components of Big Data
Architecture
Data Nodes
Data nodes refer to individual
servers or machines that
store and process data.
These nodes work together in
a cluster to manage and
analyse large datasets. Each
node typically has its own
local storage and
computational resources.
Data Streams
Data streams for efficient data
transfer and real-time
processing, enabling the
capture of large-scale,
continuously generated data.
Data stream processing deals
with data as it is generated,
allowing for faster insights
and rapid response to
changing conditions.
Processing Frameworks
Frameworks that enable
distributed processing for
handling massive amounts of
data efficiently and effectively.

3. Data Ingestion and Collection
1 Data Sources
Diverse sources of data including
databases, IoT devices,
applications, sensors, and APIs.
2 Data Pipelines
Efficient and reliable data pipelines
to streamline the collection process
and ensure data quality and integrity.
3 Real-time Processing
Systems capable of real-time processing to handle high-velocity data streams and
immediate data availability.

4. Data Storage and Management
Distributed Storage
Utilization of distributed storage
systems for cost-effective and scalable
storage of massive volumes of data.
Data Security
Implementation of robust security
measures to protect data from
unauthorized access and ensure
compliance with data protection
regulations.
Data Governance
Establishment of governance frameworks and policies for data classification, retention,
and access control.

5. Data Processing and Analysis
Data Exploration Uncover patterns, trends, and insights within
large volumes of data.
Data Transformation Prepare and cleanse raw data for analysis and
modeling purposes.
Modeling & Analytics Application of statistical and machine learning
models for predictive and prescriptive
analytics.

6. Examples
Data Exploration:
Example: Analysing large volumes of social media data to understand global trends and sentiments. This
involves exploring massive datasets containing tweets, posts, and comments to identify patterns, popular
topics, and emerging discussions.
Data Transformation:
Example: Processing and transforming raw sensor data from Internet of Things (IoT) devices in a smart
city. Converting unstructured sensor data into a structured format, aggregating information, and handling
data from diverse sources for further analysis.

7. Examples
Data Modelling:
Example: Creating a recommendation system for an e-commerce platform based on extensive user
behaviour and purchase history. Implementing machine learning algorithms on large datasets to
personalise product recommendations for individual users.
Data Analytics:
Example: Analysing healthcare data from multiple sources, including electronic health records, wearable
devices, and genomic data. Using advanced analytics to identify correlations, predict disease patterns,
and enhance personalised medicine.

8. Data Visualization and Reporting
Data Visualization
Transform complex data into visually appealing and easy-to-understand
charts, graphs, and dashboards.
Reporting Automation
Automate the generation of reports to provide insights and support decision-
making processes.

9. Thank you