Introduction

In this article, we will explore how to use Tecton to build feature pipelines that pull data from and materialize data to the Google Cloud Platform using BigQuery, Bigtable, and Dataproc. We will discuss the installation process, creating a workspace, defining feature pipelines, creating batch data sources, creating feature views, materializing feature views, creating a feature service, and applying changes and materializing data. Additionally, we will explore how to access data and monitor the progress of our feature-serving pipeline.

Installing the Tecton CLI

To get started with Tecton, we need to install the Tecton CLI. In a terminal window, you can use the command pip install tecton==0.6 to install the latest version of the Tecton CLI. Once the installation is complete, we can proceed to log into Tecton.

Logging Into Tecton

To log into Tecton and verify our credentials, we can use the command tecton login. This will redirect us to the Tecton web UI, where we can log in and proceed to create our workspace.

Creating a Workspace

Workspaces in Tecton are environments that manage collections of feature pipelines and services. To create our workspace, we can use the command tecton workspace create Tutorial workspace. This will create a workspace named "tutorial workspace" that we can use for our feature pipelines.

Defining Feature Pipelines

Feature pipelines in Tecton are defined declaratively in Python files. These Python files are commonly backed up by a git repository. To begin developing our feature pipeline, we need to run the command tecton init to initialize a new repo. This will create a new repository where we can define our feature pipelines.

Creating a Batch Data Source

To ingest data into our feature pipeline, we need to create a batch data source. This data source will tell Tecton to ingest data from a public BigQuery data set. We can configure the pipeline to ingest specific data to build our feature pipeline. In this example, we are interested in modeling search terms by geographic region.

Creating Feature Views

Feature views in Tecton describe the transformations that we apply to our data. In our feature view, we are creating a feature view called "scores by region". We use the @batch_feature_view decorator to determine data configurations, aggregations, transformations, and metadata for this feature view. We can also define how we want to materialize the feature view by setting the offline=True parameter to store the data in the offline store in Google Cloud Storage and online=True parameter to store the data in the online store in Redis for low latency retrieval.

Materializing Feature Views

To materialize our feature views, we need to apply our changes in a Python environment with Dataproc. We can create a new file called "repo.py" and define how Tecton ingests, transforms, and materializes features in this file. Tecton will automatically incorporate any Python files within this directory and its subdirectories. Once our changes are ready, we can run the tekton apply command in our terminal to push the changes to Tecton and begin the materialization process.

Creating a Feature Service

A feature service in Tecton groups related feature views together so that they can be accessed with a single API call. We can define a feature service in Tecton to make our feature views accessible for model training and prediction. By creating a feature service, we have built a complete feature-serving pipeline.

Applying Changes and Materializing Data

To apply changes and materialize our data, we need to run the tekton apply command in our terminal. This command pushes all the changes in our repo to Tecton and starts the materialization process. Tecton will generate and update our feature views in Google Cloud Storage and Redis.

Accessing Data and Monitoring Progress

Once our data has been materialized, we can access our data using simple curl requests to fetch feature vectors from the online store with low latency. We can also write Python scripts to fetch historical data from the offline store for model training. In the Tecton web UI, we can monitor the serving status of our feature service and the status of individual Dataproc jobs that Tecton created and ran to materialize the data.

➤ Pros:

Tecton provides a declarative way to define feature pipelines.
Materializing feature views in both offline and online stores allows for efficient data retrieval.
The Tecton CLI makes it easy to manage changes to the repository and apply those changes to Tecton.

➤ Cons:

The installation process and setup might be complex for beginners.
Working with multiple data sources and platforms can be challenging and require extensive configuration.

Highlights:

Tecton allows you to build feature pipelines that pull data from and materialize data to the Google Cloud Platform.
You can use BigQuery, Bigtable, and Dataproc to ingest, transform, and materialize data for model training and prediction.
Tecton provides a declarative approach to define feature pipelines, batch data sources, feature views, and feature services.
You can monitor the progress of your feature-serving pipeline in the Tecton web UI.

FAQ

Q: What is Tecton? A: Tecton is a platform that allows you to build feature pipelines for machine learning models. It provides a declarative way to ingest, transform, and materialize data from various data sources.

Q: What are feature pipelines? A: Feature pipelines are a set of processes that pull data from data sources, apply transformations, and materialize the resulting features for model training and prediction.

Q: How can I monitor the progress of my feature-serving pipeline? A: You can monitor the progress of your feature-serving pipeline in the Tecton web UI. It provides information about the serving status of feature services and the status of individual materialization jobs.

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