drafts/external_tables.md

@@ -1,74 +1,7 @@
-# External Tables: Definition, Usage, and Best Practices in Data Platforms
+# External tables: definition and usage
 
-## Introduction
+## Prerequisites 
-External tables are a fundamental concept in modern data platforms, enabling seamless integration between data lakes and analytical systems. This post explores their definition, architecture, use cases, implementation strategies, and best practices.
 
-## 1. Understanding External Tables
+Before giving the definition of the external tables, several concepts must be explained.
-- **Definition**: External tables are database objects that reference data stored outside the database management system (DBMS) but can be queried as if they were internal tables.
-- **Key Differences**: Unlike traditional tables, external tables don't store data within the DBMS but point to data in external storage systems.
-- **Benefits**:
-  - Access to data without physical movement
-  - Support for diverse file formats
-  - Cost-effective storage solutions
-  - Schema-on-read flexibility
-
-## 2. Architecture and Components
-- **Data Lake Integration**: External tables connect to data lakes (S3, ADLS, etc.) or other storage systems
-- **Metadata Management**: Schema definitions and partitioning information
-- **Query Engines**: Execution frameworks that process queries against external data
-- **Storage Formats**: Support for Parquet, ORC, Avro, JSON, CSV, and more
-
-## 3. Use Cases and Applications
-- **Data Lake Querying**: Direct analysis of lake data without ETL
-- **Schema Evolution**: Handling changing data structures
-- **Cost Optimization**: Pay only for storage, not compute
-- **Cross-Organization Sharing**: Secure data access across teams
-- **Real-Time Analytics**: Querying streaming data in external storage
-
-## 4. Implementation Guide
-### Platform-Specific Setup
-- **Snowflake**: `CREATE EXTERNAL TABLE` with stage references
-- **Databricks**: Delta Lake integration and external table creation
-- **AWS**: Athena with S3 external tables
-- **Azure**: Synapse external tables with ADLS
-
-### Best Practices
-- Use appropriate file formats (Parquet for analytics)
-- Implement proper partitioning strategies
-- Set up appropriate file naming conventions
-- Configure appropriate permissions
-
-## 5. Performance Considerations
-- **Query Optimization**: Pushdown predicates and column pruning
-- **Partitioning**: Effective data organization for faster queries
-- **Caching**: Leveraging intermediate results
-- **Monitoring**: Query performance tracking and tuning
-
-## 6. Security and Governance
-- **Access Control**: Row-level and column-level security
-- **Encryption**: Data at rest and in transit protection
-- **Audit Logging**: Tracking access and modifications
-- **Compliance**: Meeting regulatory requirements
-
-## 7. Challenges and Limitations
-- **Performance**: Network latency with remote storage
-- **Consistency**: Handling concurrent writes to external data
-- **Tooling**: Limited ecosystem compared to internal tables
-- **Vendor Lock-in**: Platform-specific implementations
-
-## 8. Future Trends
-- **Unified Data Platforms**: Tight integration between lakes and warehouses
-- **AI Integration**: External tables as training data sources
-- **Real-Time Processing**: Streaming data integration
-- **Hybrid Architectures**: Combining internal and external approaches
-
-## 9. Conclusion
-External tables represent a powerful paradigm for modern data architectures, enabling flexible, cost-effective data access. While they offer significant benefits, careful implementation and monitoring are essential for optimal performance.
-
-## 10. Additional Resources
-- [Snowflake External Tables Documentation](https://docs.snowflake.com/)
-- [Databricks Delta Lake Guide](https://docs.databricks.com/)
-- [AWS Athena Developer Guide](https://docs.aws.amazon.com/athena/)
-- [Microsoft Synapse External Tables](https://docs.microsoft.com/)
-- [Data Engineering Stack Exchange](https://data.stackexchange.com/)
 
+We will