Jump Start Solution: Analytics lakehouse

Last reviewed 2023-11-20 UTC

This guide helps you understand, deploy, and use the Analytics lakehouse Jump Start Solution. This solution demonstrates how you can unify data lakes and data warehouses by creating an analytics lakehouse to store, process, analyze, and activate data using a unified data stack.

Common use cases for building an analytics lakehouse include the following:

  • Large scale analysis of telemetry data combined with reporting data.
  • Unifying structured and unstructured data analysis.
  • Providing real-time analytics capabilities for a data warehouse.

This document is intended for developers who have some background with data analysis and have used a database or data lake to perform an analysis. It assumes that you're familiar with basic cloud concepts, though not necessarily Google Cloud. Experience with Terraform is helpful.

Objectives

  • Learn how to set up an analytics lakehouse.
  • Secure an analytics lakehouse using a common governance layer.
  • Build dashboards from the data to perform data analysis.
  • Create a machine learning model to predict data values over time.

Products used

The solution uses the following Google Cloud products:

  • BigQuery: A fully managed, highly scalable data warehouse with built-in machine learning capabilities.
  • Dataproc: A fully managed service for data lake modernization, ETL, and secure data science, at scale.
  • Looker Studio: Self-service business intelligence platform that helps you create and share data insights.
  • Dataplex: Centrally discover, manage, monitor, and govern data at scale.
  • Cloud Storage: An enterprise-ready service that provides low-cost, no-limit object storage for diverse data types. Data is accessible from within and outside of Google Cloud and is replicated geo-redundantly.
  • BigLake: BigLake is a storage engine that unifies data warehouses and lakes by enabling BigQuery and open source frameworks like Spark to access data with fine-grained access control.

The following Google Cloud products are used to stage data in the solution for first use:

  • Workflows: A fully managed orchestration platform that executes services in a specified order as a workflow. Workflows can combine services, including custom services hosted on Cloud Run or Cloud Functions, Google Cloud services such as BigQuery, and any HTTP-based API.

Architecture

The example lakehouse architecture that this solution deploys analyzes an ecommerce dataset to understand a retailer's performance over time. The following diagram shows the architecture of the Google Cloud resources that the solution deploys.

Architecture of the infrastructure for the data warehouse solution.

Solution flow

The architecture represents a common data flow to populate and transform data in an analytics lakehouse architecture:

  1. Data lands in Cloud Storage buckets.
  2. A data lake is created in Dataplex. Data in the buckets are organized into entities, or tables, in the data lake.
  3. Tables in the data lake are immediately available in BigQuery as BigLake: tables.
  4. Data transformations using Dataproc or BigQuery, and using open file formats including Apache Iceberg.
  5. Data can be secured using policy tags and row access policies.
  6. Machine learning can be applied on the tables.
  7. Dashboards are created from the data to perform more analytics by using Looker Studio.

Cost

For an estimate of the cost of the Google Cloud resources that the analytics lakehouse solution uses, see the precalculated estimate in the Google Cloud Pricing Calculator.

Use the estimate as a starting point to calculate the cost of your deployment. You can modify the estimate to reflect any configuration changes that you plan to make for the resources that are used in the solution.

The precalculated estimate is based on assumptions for certain factors, including the following:

  • The Google Cloud locations where the resources are deployed.
  • The amount of time that the resources are used.

Deploy the solution

This section guides you through the process of deploying the solution.

Create or choose a Google Cloud project

When you deploy the solution, you choose the Google Cloud project where the resources are deployed. When you're deciding whether to use an existing project or to create a new project, consider the following factors:

  • If you create a project for the solution, then when you no longer need the deployment, you can delete the project and avoid continued billing. If you use an existing project, you must delete the deployment when you no longer need it.
  • Using a new project can help avoid conflicts with previously provisioned resources, such as resources that are used for production workloads.

If you want to deploy the solution in a new project, create the project before you begin the deployment.

To create a project, complete the following steps:

  1. In the Google Cloud console, go to the project selector page.

    Go to project selector

  2. To begin creating a Google Cloud project, click Create project.

  3. Name your project. Make a note of your generated project ID.

  4. Edit the other fields as needed.

  5. To create the project, click Create.

Get the required IAM permissions

To start the deployment process, you need the Identity and Access Management (IAM) permissions that are listed in the following table. If you have the roles/owner basic role for the project in which you plan to deploy the solution, then you already have all the necessary permissions. If you don't have the roles/owner role, then ask your administrator to grant these permissions (or the roles that include these permissions) to you.

IAM permission required Predefined role that includes the required permissions

serviceusage.services.enable

Service Usage Admin
(roles/serviceusage.serviceUsageAdmin)

iam.serviceAccounts.create

Service Account Admin
(roles/iam.serviceAccountAdmin)

resourcemanager.projects.setIamPolicy

Project IAM Admin
(roles/resourcemanager.projectIamAdmin)
config.deployments.create
config.deployments.list
Cloud Infrastructure Manager Admin
(roles/config.admin)

Service account created for the solution

If you start the deployment process through the console, Google creates a service account to deploy the solution on your behalf (and to delete the deployment later if you choose). This service account is assigned certain IAM permissions temporarily; that is, the permissions are revoked automatically after the solution deployment and deletion operations are completed. Google recommends that after you delete the deployment, you delete the service account, as described later in this guide.

View the roles assigned to the service account

These roles are listed here in case an administrator of your Google Cloud project or organization needs this information.

  • roles/biglake.admin
  • roles/bigquery.admin
  • roles/compute.admin
  • roles/datalineage.viewer
  • roles/dataplex.admin
  • roles/dataproc.admin
  • roles/iam.serviceAccountAdmin
  • roles/iam.serviceAccountUser
  • roles/resourcemanager.projectIamAdmin
  • roles/servicenetworking.serviceAgent
  • roles/serviceusage.serviceUsageViewer
  • roles/vpcaccess.admin
  • roles/storage.admin
  • roles/workflows.admin

Choose a deployment method

To help you deploy this solution with minimal effort, a Terraform configuration is provided in GitHub. The Terraform configuration defines all the Google Cloud resources that are required for the solution.

You can deploy the solution by using one of the following methods:

  • Through the console: Use this method if you want to try the solution with the default configuration and see how it works. Cloud Build deploys all the resources that are required for the solution. When you no longer need the deployed solution, you can delete it through the console. Any resources that you create after you deploy the solution might need to be deleted separately.

    To use this deployment method, follow the instructions in Deploy through the console.

  • Using the Terraform CLI: Use this method if you want to customize the solution or if you want to automate the provisioning and management of the resources by using the infrastructure as code (IaC) approach. Download the Terraform configuration from GitHub, optionally customize the code as necessary, and then deploy the solution by using the Terraform CLI. After you deploy the solution, you can continue to use Terraform to manage the solution.

    To use this deployment method, follow the instructions in Deploy using the Terraform CLI.

Deploy through the console

Complete the following steps to deploy the preconfigured solution.

  1. In the Google Cloud Jump Start Solutions catalog, go to the Analytics lakehouse solution.

    Go to the Analytics lakehouse solution

  2. Review the information that's provided on the page, such as the estimated cost of the solution and the estimated deployment time.

  3. When you're ready to start deploying the solution, click Deploy.

    A step-by-step interactive guide is displayed.

  4. Complete the steps in the interactive guide.

    Note the name that you enter for the deployment. This name is required later when you delete the deployment.

    When you click Deploy, the Solution deployments page is displayed. The Status field on this page shows Deploying.

  5. Wait for the solution to be deployed.

    If the deployment fails, the Status field shows Failed. You can use the Cloud Build log to diagnose the errors. For more information, see Errors when deploying through the console.

    After the deployment is completed, the Status field changes to Deployed.

  6. To view and use the solution, return to the Solution deployments page in the console.

    1. Click the Actions menu.
    2. Select View Looker Studio Dashboard to open a dashboard that's built on top of the sample data that's transformed by using the solution.
    3. Select Open BigQuery Editor to run queries and build machine learning (ML) models using the sample data in the solution.
    4. Select View Colab to run queries in a notebook environment.

When you no longer need the solution, you can delete the deployment to avoid continued billing for the Google Cloud resources. For more information, see Delete the deployment.

Deploy using the Terraform CLI

This section describes how you can customize the solution or automate the provisioning and management of the solution by using the Terraform CLI. Solutions that you deploy by using the Terraform CLI are not displayed in the Solution deployments page in the Google Cloud console.

Set up the Terraform client

You can run Terraform either in Cloud Shell or on your local host. This guide describes how to run Terraform in Cloud Shell, which has Terraform preinstalled and configured to authenticate with Google Cloud.

The Terraform code for this solution is available in a GitHub repository.

  1. Clone the GitHub repository to Cloud Shell.

    Open in Cloud Shell

    A prompt is displayed to confirm downloading the GitHub repository to Cloud Shell.

  2. Click Confirm.

    Cloud Shell is launched in a separate browser tab, and the Terraform code is downloaded to the $HOME/cloudshell_open directory of your Cloud Shell environment.

  3. In Cloud Shell, check whether the current working directory is $HOME/cloudshell_open/terraform-google-analytics-lakehouse/. This is the directory that contains the Terraform configuration files for the solution. If you need to change to that directory, run the following command:

    cd $HOME/cloudshell_open/terraform-google-analytics-lakehouse/
    
  4. Initialize Terraform by running the following command:

    terraform init
    

    Wait until you see the following message:

    Terraform has been successfully initialized!
    

Configure the Terraform variables

The Terraform code that you downloaded includes variables that you can use to customize the deployment based on your requirements. For example, you can specify the Google Cloud project and the region where you want the solution to be deployed.

  1. Make sure that the current working directory is $HOME/cloudshell_open/terraform-google-analytics-lakehouse/. If it isn't, go to that directory.

  2. In the same directory, create a text file named terraform.tfvars.

  3. In the terraform.tfvars file, copy the following code snippet, and set values for the required variables.

    • Follow the instructions that are provided as comments in the code snippet.
    • This code snippet includes only the variables for which you must set values. The Terraform configuration includes other variables that have default values. To review all the variables and the default values, see the variables.tf file that's available in the $HOME/cloudshell_open/terraform-google-analytics-lakehouse/ directory.
    • Make sure that each value that you set in the terraform.tfvars file matches the variable type as declared in the variables.tf file. For example, if the type that’s defined for a variable in the variables.tf file is bool, then you must specify true or false as the value of that variable in the terraform.tfvars file.
    # This is an example of the terraform.tfvars file.
    # The values in this file must match the variable types declared in variables.tf.
    # The values in this file override any defaults in variables.tf.
    
    # ID of the project in which you want to deploy the solution
    project_id = "PROJECT_ID"
    
    # Google Cloud region where you want to deploy the solution
    # Example: us-central1
    region = "REGION"
    
    # Whether or not to enable underlying apis in this solution.
    # Example: true
    enable_apis = true
    
    # Whether or not to protect Cloud Storage and BigQuery resources from deletion when solution is modified or changed.
    # Example: false
    force_destroy = false
    

Validate and review the Terraform configuration

  1. Make sure that the current working directory is $HOME/cloudshell_open/terraform-google-analytics-lakehouse/. If it isn't, go to that directory.

  2. Verify that the Terraform configuration has no errors:

    terraform validate
    

    If the command returns any errors, make the required corrections in the configuration and then run the terraform validate command again. Repeat this step until the command returns the following message:

    Success! The configuration is valid.
    
  3. Review the resources that are defined in the configuration:

    terraform plan
    
  4. If you didn't create the terraform.tfvars file as described earlier, Terraform prompts you to enter values for the variables that don't have default values. Enter the required values.

    The output of the terraform plan command is a list of the resources that Terraform provisions when you apply the configuration.

    If you want to make any changes, edit the configuration and then run the terraform validate and terraform plan commands again.

Provision the resources

When no further changes are necessary in the Terraform configuration, deploy the resources.

  1. Make sure that the current working directory is $HOME/cloudshell_open/terraform-google-analytics-lakehouse/. If it isn't, go to that directory.

  2. Apply the Terraform configuration:

    terraform apply
    
  3. If you didn't create the terraform.tfvars file as described earlier, Terraform prompts you to enter values for the variables that don't have default values. Enter the required values.

    Terraform displays a list of the resources that will be created.

  4. When you're prompted to perform the actions, enter yes.

    Terraform displays messages showing the progress of the deployment.

    If the deployment can't be completed, Terraform displays the errors that caused the failure. Review the error messages and update the configuration to fix the errors. Then run the terraform apply command again. For help with troubleshooting Terraform errors, see Errors when deploying the solution using the Terraform CLI.

    After all the resources are created, Terraform displays the following message:

    Apply complete!
    

    The Terraform output also lists the following additional information that you'll need:

    • The Looker Studio URL of the dashboard that was deployed.
    • The link to open the BigQuery editor for some sample queries.
    • The link to open the Colab tutorial.

    The following example shows what the output looks like:

    lookerstudio_report_url = "https://lookerstudio.google.com/reporting/create?c.reportId=79675b4f-9ed8-4ee4-bb35-709b8fd5306a&ds.ds0.datasourceName=vw_ecommerce&ds.ds0.projectId=${var.project_id}&ds.ds0.type=TABLE&ds.ds0.datasetId=gcp_lakehouse_ds&ds.ds0.tableId=view_ecommerce"
    bigquery_editor_url = "https://console.cloud.google.com/bigquery?project=my-cloud-project&ws=!1m5!1m4!6m3!1smy-cloud-project!2sds_edw!3ssp_sample_queries"
    lakehouse_colab_url = "https://colab.research.google.com/github/GoogleCloudPlatform/terraform-google-analytics-lakehouse/blob/main/assets/ipynb/exploratory-analysis.ipynb"
    
  5. To view and use the dashboard and to run queries in BigQuery, copy the output URLs from the previous step and open the URLs in new browser tabs.

    The dashboard, notebook, and BigQuery editors appear in the new tabs.

When you no longer need the solution, you can delete the deployment to avoid continued billing for the Google Cloud resources. For more information, see Delete the deployment.

Customize the solution

This section provides information that Terraform developers can use to modify the analytics lakehouse solution in order to meet their own technical and business requirements. The guidance in this section is relevant only if you deploy the solution by using the Terraform CLI.

After you've seen how the solution works with the sample data, you might want to work with your own data. To use your own data, you put it into the Cloud Storage bucket named edw-raw-hash. The hash is a random set of 8 characters that's generated during the deployment. You can change the Terraform code in the following ways:

  1. Dataset ID