Database develop. life cycle - Data warehouses and data lakes
1. Data Warehouse
Definition:
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A data warehouse is a centralized repository for structured and processed data from multiple sources.
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Data is organized for reporting, analysis, and business intelligence (BI).
Characteristics:
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Stores structured data (tables, rows, columns)
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Schema-on-write → data is cleaned and formatted before storage
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Optimized for query performance and analytics
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Supports historical data
Advantages:
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Fast and optimized queries
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Ensures data quality and consistency
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Useful for decision-making and reporting
Disadvantages:
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Expensive to scale
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Not suitable for raw or unstructured data
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Data preparation (ETL) can be time-consuming
Example:
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Amazon Redshift, Google BigQuery, Snowflake
Use Case:
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A retail company analyzing sales trends, customer behavior, and inventory
2. Data Lake
Definition:
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A data lake is a storage system that holds raw, unprocessed data in its native format (structured, semi-structured, or unstructured).
Characteristics:
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Stores all types of data: JSON, XML, images, videos, logs
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Schema-on-read → data is structured when accessed
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Highly scalable, often on cloud storage
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Supports big data and machine learning
Advantages:
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Can store massive volumes of raw data
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Flexible → handles structured and unstructured data
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Ideal for advanced analytics, AI, and ML
Disadvantages:
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Query performance is slower than data warehouses
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Data governance and quality can be challenging
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Without proper management → can become a “data swamp”
Example:
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Amazon S3 + AWS Lake Formation, Azure Data Lake, Google Cloud Storage
Use Case:
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A social media company storing user activity logs, images, and videos for AI analysis
3. Key Differences
Feature | Data Warehouse | Data Lake |
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Data Type | Structured only | Structured, semi-structured, unstructured |
Schema | Schema-on-write | Schema-on-read |
Storage Cost | High (optimized for performance) | Low (raw storage, scalable) |
Processing | ETL (Extract, Transform, Load) | ELT (Extract, Load, Transform) |
Use Case | Business intelligence, reporting | Big data analytics, AI/ML |
Performance | Fast for queries | Slower for complex queries |
Flexibility | Less flexible | Highly flexible |
Summary:
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Data Warehouse: Clean, structured, ready for reporting and business intelligence.
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Data Lake: Raw, flexible, ideal for advanced analytics and machine learning.