TFX 8 | Ingesting Presto Database in TFX Pipeline
When building machine learning pipelines, extracting and ingesting data from various sources is a critical step. In this article, we will explore how to ingest data from a Presto database into a TFX pipeline.
TFX, short for TensorFlow Extended, is an end-to-end platform for deploying production machine learning pipelines. It provides a suite of tools for data validation, transformation, model training, and serving. One common use case in machine learning pipelines is to extract data from a database, transform it, and load it into the pipeline for further processing.
Why Ingest from a Presto Database?
Presto is an open-source distributed SQL query engine for running interactive analytic queries on various data sources. It allows users to query data where it resides, without the need for moving or copying it into a separate storage system. Ingesting data directly from a Presto database allows for real-time access to the latest data without the overhead of data movement.
Setting up the Presto Connection
To ingest data from a Presto database into a TFX pipeline, you first need to establish a connection to the database. This can be done using the PrestoQuery component provided by TFX. Here’s an example of how to configure a PrestoQuery component in a TFX pipeline:
“`
example_query = ”’
SELECT column1, column2
FROM table_name
WHERE condition
”’
presto_query = PrestoQuery(
query=example_query,
presto_node=NodeConfig(
hostname=’presto.example.com’,
port=8080,
user=’user’,
catalog=’hive’,
schema=’default’
)
)
“`
Integrating Presto Query into TFX Pipeline
Once you have set up the PrestoQuery component, you can integrate it into your TFX pipeline for further processing. You can use the output of the PrestoQuery component as input data for other TFX components such as data validation, feature engineering, and model training.
Conclusion
Ingesting data from a Presto database into a TFX pipeline allows for real-time access to the latest data without the need for data movement. By setting up a connection to the Presto database and integrating the PrestoQuery component into your TFX pipeline, you can streamline the process of data ingestion and accelerate the development of machine learning models.
