Data engineering basic project 1 step by step with source code

Designing and implementing an ETL (Extract, Transform, Load) pipeline to ingest data from multiple sources, clean and transform it, and load it into a centralized data warehouse .

The implementation of an ETL pipeline can vary depending on the specific requirements of the project and the tools being used.Step we will take to design and implement an ETL (Extract, Transform, Load) pipeline

1. Identify the data sources: The first step is to identify the data sources that need to be ingested into the pipeline. This can include databases, APIs, flat files, or other sources.
2. Extract the data: Use a tool or program to extract data from the identified data sources. This could involve writing SQL queries to extract data from a database, using APIs to retrieve data from a web service, or reading data from a file.
3. Transform the data: After extracting the data, the next step is to transform it into a format that can be loaded into the data warehouse. This can involve cleaning the data, filtering out irrelevant information, and standardizing the data.
4. Load the data: Once the data has been transformed, it can be loaded into the data warehouse. This may involve writing the data to a file or directly inserting it into a database.
5. Schedule the pipeline: To keep the data warehouse up-to-date, the ETL pipeline needs to be run on a regular basis. This can be done using a scheduler tool like Apache Airflow, or by setting up a cron job.
6. Monitor and troubleshoot: Finally, it is important to monitor the pipeline for errors and issues. This can be done using monitoring tools or by setting up alerts for specific events. When issues arise, troubleshoot and resolve them to ensure the pipeline continues to run smoothly.

Example of building a pipeline from the database postgres and python.

import psycopg2
from psycopg2 import OperationalError
from datetime import datetime, timedelta
        

# Define database connections
source_db = psycopg2.connect(host="source_host",
                             database="source_db",
                             user="source_user",
                             password="source_password")

target_db = psycopg2.connect(host="target_host",
                             database="target_db",
                             user="target_user",
                             password="target_password")        

# Define SQL queries for extraction and loadin
extract_query = "SELECT * FROM source_table"
load_query = "INSERT INTO target_table VALUES (%s, %s, %s)"

# Define the DAG
dag_interval = timedelta(days=1)
dag_start_date = datetime(2023, 5, 9)
dag = DAG('etl_pipeline', start_date=dag_start_date, schedule_interval=dag_interval)g        

# Define the task
def extract_data():
? ? cursor = source_db.cursor()
? ? cursor.execute(extract_query)
? ? data = cursor.fetchall()
? ? cursor.close()
? ? return data


def transform_data(data):
? ? # Transform the data here
? ? transformed_data = data
? ? return transformed_data


def load_data(transformed_data):
? ? cursor = target_db.cursor()
? ? cursor.executemany(load_query, transformed_data)
? ? target_db.commit()
? ? cursor.close()        

extract_task = PythonOperator(task_id='extract_data', python_callable=extract_data, dag=dag
transform_task = PythonOperator(task_id='transform_data', python_callable=transform_data, dag=dag)
load_task = PythonOperator(task_id='load_data', python_callable=load_data, dag=dag)

# Set task dependencies
extract_task >> transform_task >> load_task)        

In this example, we're using psycopg2 to connect to a PostgreSQL source database and target database. The SQL query to extract data is defined in extract_query, and the SQL query to load data is defined in load_query. The ETL pipeline consists of three tasks: extract_task which extracts data from the source database, transform_task which transforms the data, and load_task which loads the transformed data into the target database.

Note that in this example, we're using Python functions to perform the ETL tasks rather than Airflow operators. You can also use Airflow operators such as PostgresOperator or PythonOperator to perform these tasks, depending on your specific requirements.

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