About using Cloudera Data Explorer (Hue) Data Explorer provides a one-stop querying experience in Cloudera that leverages Hive, Impala, and Unified Analytics SQL engines. Accessing and using Cloudera Data Explorer (Hue) in Cloudera Data WarehouseGet started using Data Explorer by analyzing and visualizing your data with Impala and Hive SQL query engines.Viewing Hive query detailsYou can search Hive query history, compare two queries, download debug bundles for troubleshooting, and view query details, a graphical representation of the query execution plan, and DAG information on the Job Browser page in Data Explorer.Viewing Impala query detailsYou can view Impala query details, query plan, execution summary, and query metrics on the new Impala Queries tab on the Job Browser page in Data Explorer, and use this information to tune and optimize your queries. You can also view Impala query profiles on the Impala tab.Terminating Hive queriesIf a query is running for longer than expected, or you have accidentally triggered it, then you can stop the query to free up the resources. Data Explorer also allows you to stop multiple queries at once.Comparing Hive and Impala queries in Cloudera Data Explorer (Hue)You can compare two queries to know how each query is performing in terms of speed and cost-effectiveness. Data Explorer compares various aspects of the two queries, based on which you can identify what changed between the executions of those two queries, and you can debug performance-related issues between different runs of the same query.Enabling stored procedures for Hive in Cloudera Data WarehouseTo create, edit, and drop procedures and functions that are written in Hive Hybrid Procedural SQL (HPL/SQL) using the Data Explorer query editor in Cloudera Data Warehouse, you must enable the hplsql option in the hue-safety-valve field.How to run a stored procedure from Cloudera Data Explorer (Hue) in Cloudera Data WarehouseHPL/SQL allows you to implement business logic using variables, expressions, flow-of-control statements, and iterations. HPL/SQL makes SQL-on-Hadoop more dynamic. You can leverage your existing procedural SQL skills, and use functions and statements to make your typical ETL development more productive. Data Explorer provides a smart interface to run stored procedures.Using Amazon S3 with Cloudera Data Explorer (Hue)Data Explorer can read to and write to an Amazon S3 bucket.Enabling the SQL editor autocompleterAutocompleter provides finely tuned SQL suggestions for Hive and Impala dialects while you enter queries into the editor window. See Brand new Autocompleter for Hive and Impala in the Data Explorer blog.Using governance-based data discoveryData Explorer can use the metadata tagging, indexing, and search features available in Apache Atlas data management. After integrating Data Explorer with Atlas, classifications and indexed entities can be accessed and viewed in Data Explorer. This topic shows you how to use metadata classifications in Data Explorer.Creating tables in Cloudera Data Explorer (Hue) by importing filesUsing Data Explorer Importer, you can create Hive, Impala, and Iceberg tables from CVS and XLSX files. After enabling the File Browser for your cloud provider, you can import the file into Data Explorer to create tables.Supported non-ASCII and special characters in Cloudera Data Explorer (Hue)Auto-generated files may often introduce non-alphanumeric characters in the file and directory names that Data Explorer does not support. This might cause the files or directories to not appear on the Data Explorer File Browser. Review the list of non-alphanumeric, non-ASCII, diacritics (accents) characters supported in Data Explorer for the following operations: upload, create, list in folder, view, and rename.Unsupported features in Cloudera Data Explorer (Hue)Learn about the Data Explorer features that are not supported by Cloudera.Known limitations in Cloudera Data Explorer (Hue)Review the known limitations in Data Explorer.
