Big Data Architecture Intro and its implementation in the insutry.pptx

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