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Get the guideThe dbt-hive plugin is a powerful tool that can establish connections with Apache Hive and Cloudera Data Platform clusters. This is made possible through the use of the Impyla library. The plugin supports two transport mechanisms: binary and HTTP(S), providing flexibility in the connection process.
from impyla.dbapi import connect
conn = connect(host='your_host', port=21050, auth_mechanism='PLAIN')
The above code snippet demonstrates how to establish a connection using the Impyla library. Replace 'your_host' with the hostname of your Apache Hive or Cloudera Data Platform cluster.
Setting up dbt on yarn in Cloudera Data Platform involves several steps. These include cloning the dbt project, creating the yarn.env file, creating the dbt profiles.yml file, running kinit to get the authentication token, and providing an authentication token to execute dbt.
git clone https://github.com/fishtown-analytics/dbt.git
echo "export HADOOP_CONF_DIR=/path/to/hadoop/conf" > yarn.env
echo "export HIVE_CONF_DIR=/path/to/hive/conf" >> yarn.env
kinit -kt /path/to/keytab/file username
The code above outlines the steps to set up dbt on yarn in Cloudera Data Platform. Replace the paths and username with your specific information.
Providing an authentication token to execute dbt involves running the kinit command with the path to the keytab file and the username as arguments.
kinit -kt /path/to/keytab/file username
The code above shows how to provide an authentication token to execute dbt. Replace the path and username with your specific information.
Apache Hive configurations include setting the mapreduce.framework.name to local for local mode execution and partitioning by a column using the partition_by command.
hive> SET mapreduce.framework.name=local
partition_by: column_name
The code above shows some Apache Hive configurations. Replace 'column_name' with the name of the column you want to partition by.
The dbt documentation provides comprehensive explanations of incremental models. For instance, it explains that an Incremental Insert overwrite without the partition columns completely overwrites the full table and may result in data loss.
{% model incremental %}
{{
config(
materialized='incremental',
unique_key='id',
incremental_strategy='insert_overwrite'
)
}}
SELECT ...
FROM ...
{% endmodel %}
The code above is an example of an incremental model in dbt. The 'insert_overwrite' strategy is used, which may result in data loss if the partition columns are not specified.
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