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data_fetcher [2025/05/30 12:46] – [Example 3: Logging Integration] eagleeyenebuladata_fetcher [2025/06/06 01:46] (current) – [Data Fetcher] eagleeyenebula
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 **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**: **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
 The **Data Fetcher** component is a lightweight and modular system designed to retrieve data from various sources such as local files, remote databases, and external APIs. Built with scalability in mind, it abstracts the complexities of data retrieval behind a consistent interface, enabling developers to integrate new data sources without disrupting existing workflows. This streamlined approach reduces redundancy and promotes clean, maintainable code throughout the data pipeline. The **Data Fetcher** component is a lightweight and modular system designed to retrieve data from various sources such as local files, remote databases, and external APIs. Built with scalability in mind, it abstracts the complexities of data retrieval behind a consistent interface, enabling developers to integrate new data sources without disrupting existing workflows. This streamlined approach reduces redundancy and promotes clean, maintainable code throughout the data pipeline.
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 The component is built using a plug-and-play architecture, allowing developers to easily define adapters or connectors for different data formats and protocols whether it's **JSON**, **CSV**, **SQL**, or **RESTful** endpoints. Error handling, logging, and retry mechanisms are embedded into the system, ensuring robust and reliable operation even in unstable network environments. Furthermore, its reusable design makes it an ideal foundation for data-driven applications that require flexibility, such as ETL pipelines, real-time dashboards, or machine learning workflows. The component is built using a plug-and-play architecture, allowing developers to easily define adapters or connectors for different data formats and protocols whether it's **JSON**, **CSV**, **SQL**, or **RESTful** endpoints. Error handling, logging, and retry mechanisms are embedded into the system, ensuring robust and reliable operation even in unstable network environments. Furthermore, its reusable design makes it an ideal foundation for data-driven applications that require flexibility, such as ETL pipelines, real-time dashboards, or machine learning workflows.
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 ==== Example 4: Extending DataFetcher for New Sources ==== ==== Example 4: Extending DataFetcher for New Sources ====
  
-Extend the `DataFetcher` to include functionality for fetching data from a database.+Extend the **DataFetcher** to include functionality for fetching data from a database.
  
-```python+<code> 
 +python
 import sqlite3 import sqlite3
  
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             logging.error(f"Error fetching from database: {e}")             logging.error(f"Error fetching from database: {e}")
             raise             raise
-```+</code>
  
 **Usage**: **Usage**:
-```python+<code> 
 +python
 db_path = "example_database.db" db_path = "example_database.db"
 query = "SELECT * FROM users;" query = "SELECT * FROM users;"
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 results = ExtendedDataFetcher.fetch_from_database(db_path, query) results = ExtendedDataFetcher.fetch_from_database(db_path, query)
 print("Database Results:", results) print("Database Results:", results)
-```+</code>
  
 ===== Advanced Features ===== ===== Advanced Features =====
  
 1. **Fetching from Remote Databases**: 1. **Fetching from Remote Databases**:
-   Extend the class to support connections to remote SQL databases (e.g., PostgreSQL, MySQL) using libraries like `psycopg2` or `mysql-connector`.+   * Extend the class to support connections to remote SQL databases (e.g., **PostgreSQL**, **MySQL**) using libraries like **psycopg2** or **mysql-connector**.
  
 2. **Cloud Data Fetching**: 2. **Cloud Data Fetching**:
-   Add methods to fetch data from AWS S3, Google Cloud Storage, or Azure Blob Storage using their respective SDKs.+   * Add methods to fetch data from **AWS S3**, **Google Cloud Storage**, or Azure Blob Storage using their respective **SDKs**.
  
 3. **Streaming Large Data Files**: 3. **Streaming Large Data Files**:
-   Implement streaming support for reading large files line by line to optimize memory usage. +   * Implement streaming support for reading large files line by line to optimize memory usage. 
- +<code> 
-   ```python+   python
    @staticmethod    @staticmethod
    def fetch_from_file_stream(file_path):    def fetch_from_file_stream(file_path):
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            for line in file:            for line in file:
                yield line.strip()                yield line.strip()
-   ```+</code>
  
 4. **Data Transformation**: 4. **Data Transformation**:
-   Provide optional transformation pipelines to preprocess data during fetch operations.+   * Provide optional transformation pipelines to preprocess data during fetch operations.
  
 ===== Use Cases ===== ===== Use Cases =====
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 1. **Data Ingestion Pipelines**: 1. **Data Ingestion Pipelines**:
-   Fetch raw data for preprocessing and processing in AI/ML workflows.+   * Fetch raw data for preprocessing and processing in AI/ML workflows.
 2. **Database Queries**: 2. **Database Queries**:
-   Retrieve tabular data from local or remote database systems.+   * Retrieve tabular data from local or remote database systems.
 3. **Configuration File Management**: 3. **Configuration File Management**:
-   Read and parse configuration, environment, or logging files.+   * Read and parse configuration, environment, or logging files.
 4. **Integration with APIs**: 4. **Integration with APIs**:
-   Extend the class to fetch data from REST/GraphQL APIs for streaming live data into workflows.+   * Extend the class to fetch data from **REST/GraphQL APIs** for streaming live data into workflows.
  
 ===== Future Enhancements ===== ===== Future Enhancements =====
data_fetcher.1748609177.txt.gz · Last modified: 2025/05/30 12:46 by eagleeyenebula