Building ETL Pipeline and Orchestrate with Airflow(Composer) and Snowflake: Batch Processing of Weather Data on GCP

Introduction: In the era of big data, managing and transforming raw data into actionable insights is pivotal for any business aiming to thrive. This article delves into modern Extract, Transform, Load (ETL) processes, emphasizing the integration of cutting-edge tools like Apache Airflow, the significant role of cloud computing, and the advantages of advanced data warehousing solutions like Snowflake.

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ETL Fundamentals: Batch vs. Streaming

Batch Processing:

• Definition: Accumulates data in large blocks at scheduled intervals.
• Use Cases: Perfect for processing extensive data volumes periodically, e.g., daily sales or monthly financial reports.
• Advantages: Manages large data volumes efficiently and can be more economical.

Streaming Processing:

• Definition: Processes data continuously, in real-time, as it flows into systems.
• Use Cases: Crucial for time-sensitive operations such as live market tracking, real-time fraud detection, or traffic management.
• Advantages: Enables immediate responses to data insights, promoting agile decision-making.

Key ETL Tools: Kafka and Apache Airflow

Apache Kafka:

• Overview: A robust distributed event streaming platform that handles records in real-time.
• Core Features: Offers high throughput, built-in partitioning, replication, and fault tolerance.
• Integration: Central to connecting disparate data sources with real-time analytics engines.

Apache Airflow:

• Overview: An open-source tool designed for orchestrating complex data workflows.
• Core Features: Supports dynamic pipeline generation and comprehensive scheduling.
• Operational Insights: Features a rich UI to monitor live pipelines and manage operations efficiently.

The Role of Cloud Computing in ETL

• Impact of Cloud Services: Cloud computing enhances ETL processes by providing scalable resources on-demand, reducing administrative overhead, and facilitating global accessibility for teams.

Data Warehousing: Snowflake's Edge

• Snowflake Architecture: Separates compute from storage, allowing scalable and cost-effective data management.
• Innovative Features: Offers data sharing, cloning, and historical data analysis to enhance governance and recovery.
• Comparative Analysis: Examines Snowflake against other solutions like Google BigQuery and Amazon Redshift, focusing on features such as query optimization and data operations.

Practical Application: API Data Scraping Using Apache Airflow

• Setup: Configure Airflow with necessary plugins and establish connections for API integration.
• Workflow Creation: Create Directed Acyclic Graphs (DAGs) to outline and manage ETL tasks.
• Operations: Utilize custom Python operators to extract data and transform it efficiently.
• Loading: Use Airflow's Snowflake Operator to load processed data directly into Snowflake.
• Scalability: Discuss Airflow’s scalability features like Celery or Kubernetes Executors for managing larger data volumes or more complex workflows.

Methodology

data gathering

as todays words the webscaring from apis is a comment way of data gathering whan of theses api for api casting can be open-meteo.com this web also is easy to use by selecting the favourble attirbute can geterate code

how ever for retring data hirstorical data we can use the code below

This code snippet demonstrates how to automate the process of fetching, organizing, and storing weather data using Python libraries like openmeteo_requests, requests_cache, pandas, and retry_requests.

Setup API Client with Caching and Retry Mechanisms:

• requests_cache.CachedSession creates a caching layer for HTTP requests, set to expire after 3600 seconds (1 hour). This prevents repeated requests to the same URL within this timeframe from hitting the server again, instead returning the cached response, which reduces server load and speeds up data retrieval.
• retry_requests.retry wraps the caching session to add automatic retry capabilities, configured to retry up to 5 times with an increasing backoff delay. This is useful for handling intermittent network issues or server errors without failing the entire data fetch operation.

API Request Configuration:

• The script sets parameters for fetching weather data from the Open-Meteo API, specifying geographical coordinates (latitude and longitude), the range of dates for which data is needed, and a list of specific weather variables like temperature, UV index, sunrise and sunset times, etc.
• Additional parameters define units for wind speed and precipitation, as well as the timezone, to ensure the data is contextually accurate.

Fetching Data:

• The openmeteo_requests.Client, configured with the retry_session, is used to send a request to the API. The response contains weather data for the specified location and period.

Processing the API Response:

• The script prints basic geographical and timezone information for verification and debugging purposes.
• It accesses the daily data provided by the API, iterating through each variable (using the order they were requested in) to extract them as NumPy arrays for further processing.

Organizing Data into a DataFrame:

• A pandas.DataFrame is constructed to organize the weather data neatly. The index is set as a date range covering the entire period for which data was fetched, using the start and end timestamps provided by the API.
• Each weather variable is added to the DataFrame as a column, making the data ready for analysis or reporting.

Storing Data:

• The final DataFrame, containing structured and clean weather data, is printed to the console and saved to a CSV file named data.csv. This file can be used for further analysis, shared with others, or integrated into other data systems for more extensive data exploration or visualization tasks.

the composer which is fullly manged by air flow is perfect tool for orchastrate and bulideinf

Conclusion: Modern ETL processes are instrumental in transforming raw data into valuable insights. Utilizing Kafka for real-time data streaming, Airflow for orchestrating workflows, and Snowflake for advanced data warehousing ensures that businesses can maximize their data potential. This integrated approach not only offers agility and scalability but also enhances the ability to derive deep insights, providing a competitive edge in the marketplace.

Additional Steps: The full setup includes configuring Snowflake and Google Cloud Platform (GCP), employing GCP Composer—a fully managed Airflow service—for workflow management, and seamlessly integrating these technologies to enhance data operations from extraction to analytics.

Airflow(DAG)

This code example showcases how to build and orchestrate an ETL pipeline using Apache Airflow, with the specific task of fetching daily weather data using the Open-Meteo API, processing that data, and loading it into a Snowflake database. Below, we'll outline the overall structure and functionality of this ETL process:

Airflow DAG Definition and Setup

DAG Definition:

• A Directed Acyclic Graph (DAG) in Airflow represents a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies.
• This script defines a DAG named weather_data_to_snowflake_dag which is scheduled to run daily. The DAG starts its execution based on the current time minus one day to ensure it processes data for the previous day, aligning with the start_date set in the DAG's default arguments.

Default Arguments:

• start_date: Sets the starting point of the DAG to one day in the past. This scheduling ensures that the data fetched and processed is always for the day prior to the current date.
• catchup: False - This setting ensures that Airflow does not attempt to catch up with previous runs if the DAG was turned off and missed some executions according to the schedule.

Task Definition and Workflow

PythonOperator - Fetch Weather Data:

• fetch_weather_data: This function is defined to retrieve weather data for the specified location and date range. It utilizes the openmeteo_requests library to make an API call to the Open-Meteo service.
• The data fetching process includes setup for retry logic and caching using requests_cache and retry_requests to handle potential API call failures and to cache results for efficiency.

SnowflakeOperator - Load Data into Snowflake:

• After fetching the data, another task named load_to_snowflake_task is defined to load this data into a Snowflake database. This task uses a predefined SQL statement to insert data into the WEATHERFORCASTING.BRISTOL.WEATHER_DATA table.
• It uses the ti.xcom_pull function to retrieve data passed from the fetch_weather_data task. This method demonstrates how Airflow allows for passing data between tasks using XComs (cross-communication).

Process Details:

Data Fetching:

• The fetch_weather_data function fetches data for "yesterday" using dynamic dates defined in the script. This is achieved by calculating yesterday as the current date minus one day.
• The API parameters include geographic coordinates and a specific set of weather data attributes like temperature, UV index, sunrise and sunset times, etc. This data is requested for batch processing daily.

Error Handling:

• A helper function safe_get is used to safely extract data from the response. It checks and retrieves data while handling exceptions, ensuring the pipeline's robustness against data inconsistencies or missing fields.

Data Processing:

• After retrieving the data, it's processed and organized into a dictionary, formatted for easy loading into Snowflake. Each attribute like temperature, sunrise time, etc., is converted to appropriate data types to match the Snowflake table schema requirements.

Data Loading:

• The load_to_snowflake_task executes an SQL command to insert the data into Snowflake. This step illustrates the integration capability of Airflow with Snowflake, allowing seamless data loading following transformation.

Task Dependency:

• The fetch_data_task is set to precede the load_to_snowflake_task in the DAG's workflow, ensuring that the data loading happens only after successful data fetching and processing.

This Airflow script exemplifies a complete ETL process, from extracting data from an external API, transforming it in a format suitable for analysis, to loading it into a Snowflake data warehouse. The use of Python for scripting in Airflow, combined with its robust scheduler and dynamic pipeline capabilities, makes it an ideal choice for automating and managing ETL workflows in cloud environments like GCP.

This text outlines the configuration details for setting up a connection to Snowflake within Apache Airflow. The connection is essential for facilitating the ETL process where Airflow can load processed data into Snowflake. Here's a detailed breakdown of each component involved in the connection configuration:

Connection ID

• snowflake_default: This is the identifier used within Airflow to refer to this particular connection setup. It's used in the Airflow DAG code to specify which connection to use when executing tasks that interact with Snowflake.

Connection Type

• This field should be set to a specific value that identifies the connection as a Snowflake type. This typically requires the Snowflake provider package to be installed in Airflow to recognize and handle Snowflake connections correctly.

Description

• This optional field allows you to add any descriptive text to provide more details about the connection.

Schema

• bristol: Refers to the specific schema in Snowflake where operations will be performed. This schema should exist within the database specified in the connection settings.

Login

• username: The username used to log in to the Snowflake account.

Password

• The password for the Snowflake account, which is hidden for security reasons.

Extra

A JSON object containing additional settings crucial for the Snowflake connection:a

• ccount: The Snowflake account identifier, usually followed by the Snowflake computing region and cloud platform, e.g., "*****.europe-west2.gcp".
• warehouse: Specifies the Snowflake warehouse that provides the necessary compute resources to execute SQL queries.
• database: The name of the Snowflake database being accessed.
• region: The region where your Snowflake data warehouse is located, aligning with data residency requirements.
• role: Defines the Snowflake role that has the necessary permissions to perform operations.
• insecure_mode: A Boolean flag that, when set to false, ensures that OCSP checks are performed to verify the validity of Snowflake's SSL certificates.

Account, Warehouse, Database, Region, Role

• These fields provide specific details to Airflow about where and how to connect to Snowflake, mirroring the information provided in the Extra JSON but made explicit for clarity and possibly for UI display purposes in Airflow.

Private Key (Path) and Private Key (Text)

• These fields allow specifying a path to or directly inputting the private key used for key-pair authentication with Snowflake. It is an alternative to using usernames and passwords, providing enhanced security.

Insecure Mode

• This toggle specifically controls whether Airflow bypasses the Online Certificate Status Protocol (OCSP) checks for Snowflake's SSL certificates. It's recommended to leave this set to false (secure mode) unless there's a specific need to disable it for troubleshooting or network configurations that block OCSP checks.

Each of these settings ensures that Airflow can establish a secure, reliable, and correctly permissioned connection to Snowflake, enabling the seamless execution of SQL operations as defined in the DAG's tasks.

Snowflake

• first two commands set the active database to WEATHERFORCASTING and the schema to BRISTOL. All subsequent operations will occur within this schema.
• next command creates or replaces a weather_data table structured to capture comprehensive daily weather data. Each field is designed to store specific weather parameters:

• date: Non-timezone-specific timestamp of the weather forecast.
• weather_code: Numeric code representing weather conditions.
• temperature_2m_max and temperature_2m_min: Maximum and minimum temperatures at 2 meters altitude.
• apparent_temperature_max and apparent_temperature_min: Perceived temperatures considering humidity and wind.
• sunrise and sunset: Times for sunrise and sunset.
• daylight_duration and sunshine_duration: Measured durations of daylight and sunshine in seconds.
• uv_index_max: Maximum UV index value for the day, indicating the level of solar UV radiation.

Table Comment:

The table includes a comment explaining its purpose, enhancing clarity for other developers or analysts who might work with this database schema.

This structure allows for detailed tracking and analysis of weather conditions, leveraging Snowflake's robust data warehousing capabilities to manage and analyze high volumes of meteorological data effectively.

Result