This document discusses different database types including relational, OLTP, OLAP, and data lakes. It provides characteristics of each: - Relational databases are structured for data entry and manipulations (OLTP) or data collection and analysis (OLAP). OLTP databases store normalized data in columns and rows for real-time use, while OLAP databases summarize historical data. - Data lakes store raw structured, semi-structured, and unstructured data for large-scale analytics using machine learning and AI, but the data needs processing to be useful. - The document notes that no single database type is best for all situations and companies may use multiple options depending on their data and goals. It outlines topics

1. What are existing data types?
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5. What are existing database types?
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6. Database Types (Relational - Structured)
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7. Database Types (Relational - Structured)
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• OLTP Characteristics
• Designed for data entry and manipulations
• Day to day business operation
• Normalized data
• Concerned with current data (real time data)
• Data stored in columns and rows (highly detailed)
• Flexible schema
• Most common Type

8. Database Types (Relational - Structured)
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• OLAP Characteristics
• AKA Data warehouse
• Designed for data collection & analysis
• Fast
• Easy access to complex queries
• Concerned with historical/ statistical information
• Data is summarized
• Rigid schema (how the data is organized)
• Not usually concerned with individual transactions

9. Why SQL is not enough?
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10. Why SQL is not enough?
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11. Case Study
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12. G-Flix Example
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13. G-Flix Example
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14. Data lakes
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15. • Designed to capture raw data (structured, semi-
structured, and unstructured)
• Made for large amounts of data
• Uses ML and AI for analytics and processing
• It is not super-useable
• To be useful it is cleansed and organized into databases or data
warehouses.
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Data lakes

16. No one option is better for all…
• No one is better than the other
• Depends on what kind of data you have and what you
want to do with it
• A company can have one or all options
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17. Course Plan
• Data-Warehousing concepts and data modeling
• NoSQL Database Models
• Key-Value
• Column
• Document
• Graph
• In-Memory Databases
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18. PART 1
DATA WAREHOUSING
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19. What is Data Warehouse?
Is a database, or collection of databases, that
centralizes a business's information from
multiple sources and applications, and makes it
available for analytics and use across the
organization.
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20. Data Warehouse Definition
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21. Data Warehouse Architecture
Why do we need a DW?
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