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Tencent Cloud WeData

Release Notes
Dynamic Release Record (2026)
Product Introduction
Product Overview
Product Advantages
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Product Features
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Purchase Guide
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Product Version Purchase Instructions
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Preparations
Overview of Account and Permission Management
Add allowlist /security groups (Optional)
Sign in to WeData with Microsoft Entra ID (Azure AD) Single Sign-On (SSO)
Operation Guide
Console Operation
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Studio
Data Development
Data Analysis
Data Science
Data Governance (with Unity Semantics)
API Documentation
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Introduction
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Smart Ops Related Interfaces
Project Management APIs
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Data Asset - Data Dictionary APIs
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Data Exploration APIs
Asset APIs
Metadata Related Interfaces
Task Operations APIs
Data Security APIs
Instance Operation and Maintenance Related Interfaces
Data Map and Data Dictionary APIs
Data Quality Related Interfaces
DataInLong APIs
Platform Management APIs
Data Source Management APIs
Data Quality APIs
Platform Management APIs
Asset Data APIs
Data Source Management APIs
Data Types
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WeData API 2025-08-06
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Glossary

Practical Tutorial

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最終更新日: 2026-03-31 10:54:49

Tutorial 1: Allow the EMR Engine Network Security Group

For the initial use of the EMR engine in WeData Studio, you need to check if the security group used by EMR allows access to the WeData Studio network segment (30.22.32.0/19). If not, perform the following operations on the security group:
1. Enter the EMR Console, select specific engine > instance information > configure security group.

2. Click the specific security group, add inbound rule: 30.22.32.0/19 TCP:8888, then click confirm.


Tutorial 2: Refer to Another Python File in a Notebook

1. Define a function add_numbers in the b.py file, return the value of a+b.

def add_numbers(a,b):
return a+b
2. In the a.ipynb file, refer to b.py, call the functions defined in file b, and output the return value.

import b
result = b.add_numbers(3, 4)
print(result)
# 7

Tutorial Three: Reading a CSV File in a Directory in Notebook

1. Click folder > upload to upload the CSV file you need to use to the directory.

2. Read the CSV file in the directory through code in the Notebook and print it.
file_path = "/Workspace/Workspace/Users/celiacui/sample_data.csv" #replace with your csv file path
df = spark.read.csv(file_path, header=True, inferSchema=True)
#print df
display(df)

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