06 - Big Data and Cloud

Page 1: Introduction

• Overview of Cloud and Big Data presentation

• Presenter: Ivika Jäger

• Date: November 3, 2024

Page 2: What is THE CLOUD?

• Definition: A data warehouse accessed over the internet.

Page 3: Cloud Types

• Private Cloud:

  • Dedicated to a single organization.

  • Maximum security.

  • High cost.

• Public Cloud:

  • Shared services offered to the general public.

  • Affordable and accessible.

• Hybrid Cloud:

  • Combines private and public cloud elements.

  • Sensitive data stored on private servers; public platforms used for analytics.

Page 4: Network vs. Internet

• Traditional data warehousing (DW) accessed via network connection.

• A network can be closed and not connected to the internet.

• The Cloud is always accessed over the internet.

Page 5: Cloud Computing

• Definition: Computing services delivered over the internet.

• Resources: Storage, servers, networking, databases, and software.

• Model: Businesses and individuals can rent the resources they need (pay-as-you-go model).

• Example: Amazon Web Services (AWS).

Page 6: Differences Between Traditional and Cloud Data Warehousing

Page 7: Future of Cloud and Traditional DW

• Regulations and Compliance:

  • Laws may require sensitive information to be stored on-premises.

• High Sensitivity:

  • Proprietary data is safer under in-house control.

• Risk Factors:

  • Data breaches, insider threats, dependency on third-party providers.

• Performance Requirements:

  • Traditional DW systems may outperform cloud-based systems.

Page 8: Cloud Computing Models

• Infrastructure as a Service (IaaS):

  • Renting land; complete control of everything on the land.

• Platform as a Service (PaaS):

  • Renting a workshop; tools and space are pre-configured.

• Software as a Service (SaaS):

  • Renting a fully furnished hotel room where everything is ready to use.

Page 9: Key Differences in Cloud Models

Page 10: IaaS Example

• Use Case:

  • Useful for startups needing scalable server capacity.

  • Example: Netflix on AWS: rents servers and optimizes capacity.

Page 11: PaaS Example

• Case Study:

  • Linas Matkasse builds on Microsoft Azure App Services.

  • Utilizes Azure products for various services including app services and analytics.

Page 12: Examples of SaaS Companies

• Popular SaaS Applications:

  • iCloud, Microsoft 365, Zoom, Dropbox, Salesforce, Google Docs.

Page 13: SaaS Characteristics

• Cloud-based, subscription model

• Centralized updates; no installation required

• Examples: Gmail, ChatGPT, Microsoft 365, Dropbox.

Page 14: BI on the Cloud

• Benefits:

  • Accessibility: available anytime, anywhere.

  • Scalability: scale resources based on needs.

  • Cost-efficiency: no upfront investment, pay-as-you-go.

  • Inclusivity: affordable AI, big data, and analytics for small businesses.

Page 15: Analytics as a Service (AaaS)

• Provides advanced analytics capabilities without infrastructure investment.

• Examples: Amazon Quicksight, Tableau Cloud, PowerBI (Cloud version).

Page 16: Data Measurement Overview

• Unit Measurements:

  • From Kilobyte (KB) to Yottabyte (YB) in data storage.

Page 17: When Data Becomes Big

• Data types and sizes vary considerably, emphasizing the sheer volume of big data.

Page 18: Data Creation Trends

• Yearly data creation visualization across regions.

Page 19: Characteristics of Big Data

• Volume: Rapid data accumulation.

• Variety: Types of data: structured, semi-structured, unstructured.

• Velocity: Speed of data creation and processing.

Page 20: New Technologies

• Traditional methods struggle with big data due to the need for real-time processing.

  • Tools: Hadoop, Apache Spark for distributed storage and analytics.

Page 21: Location Data Usage

• Insights into consumer behavior, retail trends, agriculture, and crime analysis.

Page 22: Generating Insights

• Analyzing location data for market trends and customer experiences.

Page 23: Satellite Data Utilization

• Usage of remote sensing and data for monitoring and analysis.

Page 24: Social Media Insights

• Analyzing user interactions to inform business strategies.

Page 25: Financial Data Analytics

• Processing large volumes of financial transactions for trend forecasting and risk management.

Page 26: Sensor Data and IoT

• Devices tracking various physical phenomena for monitoring and analysis.

Page 27: Internet of Things (IoT)

• IoT creates vast amounts of data through connected devices and sensors.

Page 28: IoT Infrastructure

• Components include sensors, connectivity, and software for data management.

Page 29: Smart Home Example

• Integrating automation through sensors for everyday activities.

Page 30: IoT Applications in Industries

• Enhancing efficiency and decision-making across various sectors.

Page 31: Challenges and Opportunities

• Complexity of data analysis and security concerns.

Page 32: The Role of 5G in IoT

• Accelerating IoT capabilities through improved connectivity.

Page 33: Integration of AI in IoT

• Combining smart sensors with AI for enhanced analysis.

Page 34: Summary of Key Points

• Cloud computing enables scalable storage and advanced data analytics through AI.

• Outlook: A future of cloud-based, AI-driven data management and decision-making.