var email = "example@gmail.com";  
var vin = sdk.getContext( 'vinnotfleet' );
//alert(vin);
window.open('mailto:example@gmail.com');

import pyodbc
import pandas as pd

# Connect to the ClickHouse database
conn = pyodbc.connect(
   'Driver={ClickHouse ODBC Driver};'
   'Server=localhost;'
   'Port=9000;'
   'Database=mydatabase;'
   'UID=user;'
   'PWD=password'
)

# Create a cursor
cursor = conn.cursor()

# Execute a SELECT statement to retrieve data from a table
cursor.execute('SELECT * FROM mytable')

# Fetch the rows from the cursor and create a DataFrame
df = pd.DataFrame.from_records(cursor.fetchall())

# Close the cursor and connection
cursor.close()
conn.close()

# Print the DataFrame
print(df)

on the right click properties of the document, the open doc URL is not available and getting a message like - Token is not generated, how to generate the token and get the open doc URL

This feature is for pulling the previously moved versions of a pipeline that are committed by the user. This can help a user significantly to recover the lost pipelines or avoid unwanted modifications made to the pipeline.

ETL (extract, transform, load) is a process used to move data from one or more sources to a destination system, typically for the purpose of preparing the data for analysis or reporting. ETL involves three main steps:
 
·        Extract: The first step in an ETL process is to extract the data from one or more sources. This may involve connecting to a database, reading files from a local or remote file system, or accessing data from a streaming platform such as Apache Kafka.
 
·        Transform: The next step is to transform the data to fit the needs of the destination system. This may involve tasks such as filtering, aggregating, or reshaping the data, as well as applying data cleansing and data validation processes.
 
·        Load: The final step in the ETL process is to load the transformed data into the destination system. This may involve writing the data to a database, writing it to a file system, or streaming it to another system.
 
ETL is often used in data pipelines to move data from various sources to a destination system for further analysis or reporting. The destination system may be a data warehouse, a big data processing platform, or another type of data storage or analysis system. ETL is typically used when the destination system has specific requirements for the data, such as a specific data format or schema, or when the data needs to be cleaned or transformed before it can be loaded into the destination system.

The alerts on this page let you know about actions you've taken on the platform. For instance, you'll be alerted for confirmation if you add a data store or data set. 

How to give user restriction to the data in platform

(12-23-2022, 11:22 AM)harshabalan Wrote: How to give user restriction to the data in platform

The Data as an API  feature exposes data to authorized end consumers and applications by providing external data access and functionality through APIs.

The Data Sheet stores data in a visually appealing & easy-to-use spreadsheet.
 It can act as a powerful database that allows to remove the data silos and store or share the structured data across.

While editing the columns name, i have deleted the column by mistake, how can I undo this and bring back the column