Data Lakes and Lakehouses

A data lake is a centralized repository that stores vast quantities of raw data in its native format, accommodating structured, semi-structured, and unstructured data for flexible, large-scale analytics and machine learning. While powerful, data lakes can lack the data management, reliability, and performance features of a traditional data warehouse, sometimes leading to disorganized "data swamps." The data lakehouse is a modern architectural paradigm that evolves this concept by combining the low-cost, flexible storage of a data lake with the robust data structures and management features of a data warehouse, such as ACID transactions, schema enforcement, and indexing. This hybrid approach aims to create a single, unified platform that can efficiently support both business intelligence (BI) and data science workloads directly on the same data, eliminating data silos and redundancy.

1. Introduction to Modern Data Architectures
   1. Historical Evolution of Data Management
      1. Early File-Based Systems
         1. Flat File Storage
         2. Sequential Access Patterns
         3. Data Redundancy Issues
      2. Emergence of Relational Databases
         1. CODD's Relational Model
         2. SQL Development
         3. ACID Properties
      3. Growth of Enterprise Data Warehousing
         1. Inmon vs. Kimball Methodologies
         2. OLTP vs. OLAP Systems
         3. Data Mart Evolution
      4. Distributed Computing Era
         1. Hadoop Ecosystem Emergence
         2. MapReduce Programming Model
         3. NoSQL Database Growth
   2. Limitations of Traditional Data Systems
      1. Scalability Constraints
         1. Vertical Scaling Limitations
         2. Hardware Cost Escalation
         3. Performance Bottlenecks
      2. Rigid Data Models
         1. Schema-First Requirements
         2. Change Management Complexity
         3. Data Type Restrictions
      3. High Maintenance Overhead
         1. Infrastructure Management
         2. Performance Tuning Requirements
         3. Backup and Recovery Complexity
      4. Limited Support for Unstructured Data
         1. Binary Data Handling
         2. Text Processing Limitations
         3. Multimedia Storage Challenges
   3. The Big Data Revolution
      1. Defining Big Data
         1. Volume Characteristics
         2. Velocity Requirements
         3. Variety Challenges
         4. Veracity Concerns
         5. Value Extraction
      2. Data Type Classifications
         1. Structured Data
            1. Tabular Formats
            2. Relational Records
            3. Fixed Schema Data
         2. Semi-Structured Data
            1. JSON Documents
            2. XML Files
            3. Avro Records
            4. Parquet Files
            5. ORC Files
         3. Unstructured Data
            1. Text Documents
            2. Images and Videos
            3. Audio Files
            4. Sensor Data
            5. Log Files
            6. Social Media Content
      3. Big Data Adoption Drivers
         1. Digital Transformation Initiatives
         2. IoT Device Proliferation
         3. Social Media Explosion
         4. Mobile Computing Growth
         5. Cloud Computing Adoption