As we know that ,All component configurations are classified broadly into 3 sections-
1)basic
2)Meta_info 
3)Resource-configuration

*Basically Enrichment  component helps users to enrich the incoming data from in-event by querying lookup table in RDBMS and MongoDB and currently it supports 5 drivers :-
1)MySql
2)Ms-SQL
3)MongoDB
4)Postgres
5)Oracle

It has to be configure in a way that-suppose we are reading a data from mongo collection through pymongo reader and joining with one more table of mysql by Enrichment component,so in both Pymongo reader & Enrichment component we need to specify database name and table name which we are gonna join.

*During the Component configuration,There is one option in Meta_info, Conditions: Select conditions type (Remove or Blank option)

Remove [works like an inner join]– This option completely removes the row If any row of incoming data is not matching with query condition of the lookup table. Only passes the matching records.

Blank – This option sets blank for the cell of a row if complete incoming data from the in-event and matching rows from the lookup table.

"@data." is used to refer to the data coming from the previous event. For e.g.: If the data coming from the previous event is department_id and the column in the master table is dept_id then in the where clause we write as

Query which has to be entered in a format:-

SELECT department_name,location_id FROM departments WHERE department_id = @data.department_id     = Valid

Asterisk (*) is not valid in the select statement
SELECT * FROM departments WHERE department_id = @data.department_id       = Invalid

What is the difference between writing a script in connection and components?

Use python script component.
Paste below script in meta information script section.
Provide URL where csv file is located.

import pandas as pd
def read_data():
   url = 'http://example.com/path/to/file.csv'
   df = pd.read_csv(url)
   return df

Use python script component.
Paste below script in meta information script section.
Provide connection credentials.

import pandas as pd
from sqlalchemy import create_engine
def sql_write(data):

   # Connect to the database
   engine = create_engine('mysql+pymysql://user:password@host/database')

   # Store the data in the database
   data.to_sql('table_name', engine, if_exists='replace')

Use python script component.
Paste below script in meta information script section.

import pandas as pd
from pymongo import MongoClient

def myconnect(data):
  df = data
  client = MongoClient("mongodb://localhost:27017/") #connection_string
  mydatabase = client.db_name #dbname
  mycollection = mydatabase.collection_name #collection_name
 
  mycollection.insert_many(df.to_dict('records'))
  client.close()

Using python script component.
Paste below script in meta information script section.
Provide connection credentials.

import pandas as pd
import paramiko
def myconnect():
   ssh = paramiko.SSHClient()
   ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
   ssh.connect('localhost',port=22,username='user',password='123456')
   #print("connected suceessfully to SFTP Server")
   sftp_client = ssh.open_sftp()
   sftp_client.chdir('/home/user/folder_name')
   df= pd.read_csv(sftp_client.open('/home/user/folder_name/file_name.csv'))
   sftp_client.close()
   ssh.close()
   return df

Use python script component.
Under meta information paste below script in script section.
Provide required credentials.
import pandas as pd
from pymongo import MongoClient
def myread():
    client = MongoClient("mongodb://localhost:27017/") #connection_string
    mydatabase = client.db_name#cdatabase_name
    mycollection = mydatabase.collection_name #collection_name
    cursor = mycollection.aggregate([ { '$project': { '_id': 0}}]) #aggregate_query
    df = pd.DataFrame(list(cursor))
    client.close()
    return df
myread()

Paste below script in meta information script section.

Provide required credentials.

import pandas as pd

from sqlalchemy import create_engine
def read_data():
   engine = create_engine('mysql://root:root@localhost/db')
                       #dbsystem://username:password@host/db
   df = pd.read_sql('SELECT * from table_name', con=engine)
   return df 
 

What happens when we check Notify GV Update in the filter?

What happens when we click on the check box of Auto Update GV in the filter?