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data_fetcher [2025/04/25 23:40] – external edit 127.0.0.1data_fetcher [2025/06/06 01:46] (current) – [Data Fetcher] eagleeyenebula
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 ====== Data Fetcher ====== ====== Data Fetcher ======
-* **[[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 or remote databases. It ensures a seamless data-fetching process that is extensible to support new data sources while maintaining clean, structured, and reusable code.+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.
  
 +{{youtube>PW3c5CAOtCw?large}}
 +
 +-------------------------------------------------------------
 +
 +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.
 ===== Overview ===== ===== Overview =====
  
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 1. **Ease of Access**: 1. **Ease of Access**:
-   Simplify the process of retrieving input data from multiple sources.+   * Simplify the process of retrieving input data from multiple sources.
 2. **Reusability**: 2. **Reusability**:
-   Provide a reusable module that can adapt to various workflows.+   * Provide a reusable module that can adapt to various workflows.
 3. **Debuggability**: 3. **Debuggability**:
-   Allow easy troubleshooting of input issues using detailed logs.+   * Allow easy troubleshooting of input issues using detailed logs.
 4. **Scalability**: 4. **Scalability**:
-   Lay the foundation for fetching from larger systems like databases, APIs, or cloud storages.+   * Lay the foundation for fetching from larger systems like databases, APIs, or cloud storages.
  
 ===== System Design ===== ===== System Design =====
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 ==== Core Class: DataFetcher ==== ==== Core Class: DataFetcher ====
  
-```python+<code> 
 +python
 import logging import logging
  
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         logging.info("Data fetched successfully.")         logging.info("Data fetched successfully.")
         return data         return data
-```+</code>
  
 ==== Design Principles ==== ==== Design Principles ====
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 ==== Example 1: Fetching Data from a Local File ==== ==== Example 1: Fetching Data from a Local File ====
  
-Use the `fetch_from_file` method to retrieve data from a given file path.+Use the **fetch_from_file** method to retrieve data from a given file path.
  
-```python+<code> 
 +python
 from data_fetcher import DataFetcher from data_fetcher import DataFetcher
  
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 except Exception as e: except Exception as e:
     print(f"An error occurred: {e}")     print(f"An error occurred: {e}")
-```+</code>
  
 **Expected Output**: **Expected Output**:
 +<code>
 The contents of the file will be printed if the file exists; otherwise, an error message will be displayed. The contents of the file will be printed if the file exists; otherwise, an error message will be displayed.
 +</code>
 ==== Example 2: Fetching from a Non-Existent File ==== ==== Example 2: Fetching from a Non-Existent File ====
  
 Handle errors gracefully when attempting to fetch from a file that does not exist. Handle errors gracefully when attempting to fetch from a file that does not exist.
  
-```python+<code> 
 +python
 from data_fetcher import DataFetcher from data_fetcher import DataFetcher
  
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 except Exception as e: except Exception as e:
     print(f"Unhandled error occurred: {e}")     print(f"Unhandled error occurred: {e}")
-```+</code>
  
 **Error Logging Output**: **Error Logging Output**:
-```+<code>
 ERROR - FileNotFoundError: No such file or directory 'non_existent_file.txt' ERROR - FileNotFoundError: No such file or directory 'non_existent_file.txt'
-``` +</code>
  
 ==== Example 3: Logging Integration ==== ==== Example 3: Logging Integration ====
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 Enable logging to track file-fetching operations. Enable logging to track file-fetching operations.
  
-```python+<code> 
 +python
 import logging import logging
 from data_fetcher import DataFetcher from data_fetcher import DataFetcher
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 except Exception as e: except Exception as e:
     print(f"An error occurred: {e}")     print(f"An error occurred: {e}")
-```+</code>
  
 **Log File Output (data_fetcher.log)**: **Log File Output (data_fetcher.log)**:
-```+<code>
 2023-10-10 14:31:11 - INFO - Fetching data from file: sample_data.txt... 2023-10-10 14:31:11 - INFO - Data fetched successfully. 2023-10-10 14:31:11 - INFO - Fetching data from file: sample_data.txt... 2023-10-10 14:31:11 - INFO - Data fetched successfully.
-``` +</code>
  
 ==== 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 =====
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 ===== Conclusion ===== ===== Conclusion =====
  
-The **Data Fetcher** is a lightweight yet powerful system for integrating data retrieval into workflows. Its modular design ensures simplicity while remaining highly extensible for fetching data from various sources. With logging, error handling, and advanced fetching capabilities, this system is suited for modern data-driven applications.+The **Data Fetcher** is a lightweight yet powerful system for integrating data retrieval into workflows, offering a clean and efficient solution for accessing structured and unstructured data across diverse environments. Its modular architecture ensures simplicity in core design while providing the flexibility to adapt to a wide range of data sources, including local storage, cloud services, APIs, and remote databases. Whether you're working with small-scale datasets or large, distributed systems, the Data Fetcher streamlines the process of ingesting and normalizing data, serving as a reliable backbone for scalable data pipelines. 
 + 
 +Equipped with built-in logging, error handling, and customizable retry logic, the system is engineered for resilience in real-world conditions where network latency, API rate limits, or transient failures can otherwise disrupt operations. It supports parallel fetching, caching strategies, and conditional querying, enabling optimized performance for both batch and real-time data workflows. Designed with developer experience in mind, the Data Fetcher promotes reusable configurations and a plug-and-play adapter system, making it easy to extend functionality without bloating the core. This makes it an ideal choice for modern data-driven applications, from analytics dashboards and AI pipelines to automation platforms and microservice ecosystems.
data_fetcher.1745624454.txt.gz · Last modified: 2025/04/25 23:40 by 127.0.0.1