How To Get Started With ETL Processes In Apache Age

In this article we are going to describe what is an ETL process,what is the ETL process in Apache AGE and also list various tools that Apache Age can use for data integration.

What is an ETL process?
ETL stands for Extract,Transform,Load and it refers to a process used in data integration and data warehousing. The ETL process is a series of steps that are followed to extract data from various sources, transform it into a format that is suitable for analysis or storage, and then load it into a target database, data warehouse, or data lake.

For an ETL process to take place these are the steps/process that should be followed:

Step 1: Extraction Of Data From Sources
This is the first step in this step data is collected and extracted from various sources which include database and Apis and in Apache Age you can use the following to export and import data through CSV,SQL,JSON or XML

Here is a sample example of how you can extract data in Apache Age.
After identifying the data we can write a program to extract the data from the source file and this can be easily be done with Java or python.

e.g. extracting csv data into the Apache Age graph using python.

def main():

    # Specify the path to your CSV file.
    csv_file = 'People.csv'
    read_csv(csv_file)
main()

Above the main() function we will define the read_csv() function.

import csv

def read_csv(csv_file):

    # Read the CSV file.
    with open(csv_file, 'r') as file:
        reader = csv.reader(file)

        # Get the header row and go to the next row.
        header = next(reader)
        print(header)

        # Print each row.
        for row in reader:
            print(row)

This function will open the csv_file and create an csv.reader object, which then it will allow us to iterate over the rows of the CSV file.

Step 2: Transform The Data
In this step we will define the schema and in this we will have to understand the structure of the Apache Age database where you want to load the data.

Next we will have to apply the necessary transformation to the extracted data this may include data manipulation operations and then prepare the data for loading

sample example of how to transform data from extracted data into Apache Age from csv in python

e.g

def insert_data_into_age(age_client, rows, label_name):
   #Inserts the data from a list of rows into Apache Age.

    for row in rows:
        node = age_client.create_node(label_name)

        for i, value in enumerate(row):
            node.set_property(i, value)

        age_client.save_graph()

Step 3: Load the Data
In this step we are going to load the data into the target destination in which our target destination is the database.This step involves placing the processed data into a structure that allows for efficient querying and analysis.

In this step to be able to load the data into Apache Age we are going to first have to connect to the database

and to be able to connect to the database we are going to use the following code statement:

Next we are going to load the data we will first, import these two other libraries: psycopg2 and age. After this, update the main() function

def main():

    csv_file = 'People.csv'
    create_csv(csv_file)

    new_csv_file = 'people.csv'
    GRAPH_NAME = 'csv_test_graph'
    NODE_LABEL = 'Products'

    conn = psycopg2.connect(host="localhost", port="5432", 
    dbname="dbname", user="username", password="password")
    age.setUpAge(conn, GRAPH_NAME)

    path_to_csv = '/path/to/csv/' + new_csv_file
    load_csv_nodes(path_to_csv, GRAPH_NAME, conn, NODE_LABEL)

main()

The updated main() function now will connect to the database and to AGE's graph.

def load_csv_nodes(csv_file, graph_name, conn, node_label):

    with conn.cursor() as cursor:
        try :
            cursor.execute("""LOAD 'age';""")
            cursor.execute("""SET search_path = ag_catalog, "$user", public;""")
            cursor.execute("""SELECT create_vlabel(%s, %s);""", (graph_name, node_label,) )
            cursor.execute("""SELECT load_labels_from_file(%s, %s, %s)""", (graph_name, node_label, csv_file,) )
            conn.commit()
            print(f"CSV file loaded to AGE successfully!")

        except Exception as ex:
            print(type(ex), ex)
            conn.rollback()

and at last you have loaded the csv data into the Apache Age which can further be used to for data management and analysis.

Some of the best data integration tools that can be used with Apache Age include:

Apache Spark: Spark is a unified analytics engine for large-scale data processing. It can be used for a wide range of workloads, including ETL, machine learning, and streaming data processing. Spark is well-suited for Apache AGE because it can scale to handle large datasets and provides a variety of features for data processing, such as data cleansing, transformation, and loading.

Apache Kafka: Kafka is a distributed streaming platform that can be used to publish, subscribe to, store, and process streams of records in real time. Kafka is a good choice for ETL in Apache AGE because it can be used to stream data from a variety of sources into Apache AGE in real time.

Apache Hadoop:
Apache Hadoop is another popular big data processing framework that is widely used for storing and processing large amounts of data. By integrating Apache Age with Hadoop, you can store and process graph data on Hadoop's distributed file system (HDFS). This integration is made possible by the Apache Hadoop Connector for Apache Age, which allows you to read and write graph data between Apache Age and Hadoop.

Conclusion
In conclusion Apache Age can be easily be the best graph database for any ETL process because it simplifies the process and seamless transformation of data thus be a good tool for data integration and data management