@Maulz  Connecting Python to Cloudera, Hive, and Hue involves using libraries and drivers that interface with HiveServer2  the service that allows remote clients to execute Hive queries.There are several methods to connect Python to Cloudera's ecosystem, particularly to access Hive tables through Hue. I'll detail the most common approaches. Prerequisites Cloudera/Hadoop Cluster: Ensure HiveServer2 is running on your cluster. Default HiveServer2 port: 10000 (verify via Cloudera Manager). Python Environment: Python 3.6+ installed. Authentication: Know your authentication method: Username/password (non-secure). Kerberos (common in enterprise clusters). LDAP. Below is a detailed, step-by-step guide: 2. Install Required Python Libraries Use pip to install: pip install pyhive # Python interface for Hive pip install thrift # Thrift protocol support pip install sasl # SASL authentication (for Kerberos) pip install thrift-sasl # SASL wrapper for Thrift pip install pykerberos # Kerberos support (if needed) For JDBC-based connections (alternative method): pip install JayDeBeApi # JDBC bridge 3. Configure Cloudera/Hive Via Cloudera Manager: Enable HiveServer2 and ensure it’s running. Check HiveServer2 Port (default: 10000). If using Kerberos: Ensure Kerberos is configured in Cloudera. Export your Kerberos keytab kinit -kt <keytab_file> <principal> Connecting Python to Cloudera/Hue/Hive 1.Using PyHive it's a Python library specifically designed to work with Hive from pyhive import hive                                            # Connect to Hive server conn = hive.Connection( host='cloudera_host_name', port=10000,                         # Default HiveServer2 port username='your_username', password='your_password', database='default',               # Your database name auth='LDAP' # Or 'NONE', 'KERBEROS', 'CUSTOM' depending on your authentication setup ) # Create a cursor cursor = conn.cursor() # Execute a query cursor.execute('SELECT * FROM your_table LIMIT 10') # Fetch results results = cursor.fetchall() print(results) # Close connections cursor.close() conn.close() 2. Using the Impala Connection If your Cloudera cluster uses Impala: from impala.dbapi import connect conn = connect( host='cloudera_host_name', port=21050, # Default Impala port user='your_username', password='your_password', database='default' # Your database name ) cursor = conn.cursor() cursor.execute('SELECT * FROM your_table LIMIT 10') results = cursor.fetchall() print(results) cursor.close() conn.close() 3. Integration with Hue Hue is a web UI for Hadoop, but you can programmatically interact with Hive via its APIs (limited). For direct Python-Hue integration: Use Hue’s REST API to execute queries: import requests # Hue API endpoint (replace with your Hue server URL) url = "http://<hue_server>:8888/hue/notebook/api/execute/hive" headers = {"Content-Type": "application/json"} data = { "script": "SELECT * FROM my_table", "dialect": "hive" } response = requests.post( url, auth=('<hue_username>', '<hue_password>'), headers=headers, json=data ) print(response.json()) Troubleshooting Common Issues: Connection Refused: Verify HiveServer2 is running (netstat -tuln | grep 10000). Check firewall rules. Authentication Failures: For Kerberos: Ensure kinit succeeded. For LDAP: Validate credentials. Thrift Version Mismatch: Use Thrift v0.13.0 with Hive 3.x. Logs: Check HiveServer2 logs in Cloudera Manager (/var/log/hive). 4. Best Practices Use connection pooling for high-frequency queries. For Kerberos, automate ticket renewal with kinit cron jobs. Secure credentials using environment variables or Vault. Happy hadooping ... View more