Datastream MySQL to BigQuery

Join Sign in

Datastream MySQL to BigQuery

1 hour 30 minutes 1 Credit


Google Cloud selp-paced labs logo


Datastream is a serverless and easy-to-use Change Data Capture (CDC) and replication service that allows you to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency. In this lab you'll learn how to replicate data from your OLTP workloads into BigQuery, in real time.

You will begin by deploying MySQL on Cloud SQL and import a dataset using the gcloud command line. Then, in the Cloud Console UI, you will create and start a Datastream stream and a Dataflow job for replication. The replication uses a Dataflow template to enable continuous replication of data, along with Cloud Storage and Pub/Sub for buffering data.

Although you can easily copy and paste commands from the lab to the appropriate place, students should type the commands themselves to reinforce their understanding of the core concepts.

What you'll do

  • Prepare a MySQL Cloud SQL instance using the Google Cloud Console

  • Create a GCS bucket to be used in replication

  • Create a Pub/Sub topic, subscription, and a GCS Pub/Sub notification policy

  • Import data into the Cloud SQL instance

  • Create a Datastream connection profile referencing the MySQL DB

  • Create a Datastream connection profile referencing the GCS destination

  • Create a Pub/Sub resources and a GCS Pub/Sub notification policy

  • Create a Datastream stream and start replication

  • Create a BigQuery dataset

  • Deploy a Dataflow job to replicate data


  • Familiarity with standard Linux environments

  • Familiarity with change data capture (CDC) concepts


Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

  1. Click Activate Cloud Shell Activate Cloud Shell icon at the top of the Google Cloud console.

  2. Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

  1. (Optional) You can list the active account name with this command:

gcloud auth list


ACTIVE: * ACCOUNT: To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project


[core] project = <project_ID>

Example output:

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Set your Project ID variable:

PROJECT_ID=<Replace with your Project ID>

Create a Cloud SQL database instance

Run the following command in Cloud Shell to create a Cloud SQL for MySQL database instance:

MYSQL_INSTANCE=mysql-db DATASTREAM_IPS=,,,, gcloud sql instances create ${MYSQL_INSTANCE} \ --cpu=2 --memory=10GB \ --authorized-networks=${DATASTREAM_IPS} \ --enable-bin-log \ --region=us-central1 \ --database-version=MYSQL_8_0 \ --root-password password123

This script creates the database in us-central1. For other regions, be sure to replace the IPs below with the right Datastream Public IPs for your region.

Once the database instance is created, make a note of the instance's public IP - you'll need this later when creating Datastream's connection profile.

Click Check my progress to verify the objective. Create a Cloud SQL database instance

Create a Cloud Storage bucket

Run the following to create a Cloud Storage bucket with the same name as your project:

gsutil mb gs://${PROJECT_ID}

Create Pub/Sub resources

Run the following to create Pub/Sub resources:

gcloud pubsub topics create datastream gcloud pubsub subscriptions create datastream-subscription --topic=datastream gsutil notification create -f "json" -p "data/" -t "datastream" "gs://${PROJECT_ID}"

Click Check my progress to verify the objective. Create cloud storage bucket and Pub/Sub resources

Import a SQL file into MySQL

Open a file named create_mysql.sql in vim or your favorite editor, then copy the text below into your file:

CREATE DATABASE IF NOT EXISTS test; USE test; CREATE TABLE IF NOT EXISTS test.example_table ( id INT NOT NULL AUTO_INCREMENT PRIMARY KEY, text_col VARCHAR(50), int_col INT, created_at TIMESTAMP ); INSERT INTO test.example_table (text_col, int_col, created_at) VALUES ('hello', 0, '2020-01-01 00:00:00'), ('goodbye', 1, NULL), ('name', -987, NOW()), ('other', 2786, '2021-01-01 00:00:00');

Next, run the following commands to copy this file into the Cloud Storage bucket you created above (make sure you do not load the file into the data/ directory), make the file accessible to your Cloud SQL service account, and import the SQL command into your database:

SERVICE_ACCOUNT=$(gcloud sql instances describe ${MYSQL_INSTANCE} | grep serviceAccountEmailAddress | awk '{print $2;}') gsutil cp create_mysql.sql gs://${PROJECT_ID}/resources/create_mysql.sql gsutil iam ch serviceAccount:${SERVICE_ACCOUNT}:objectViewer gs://${PROJECT_ID} gcloud sql import sql ${MYSQL_INSTANCE} gs://${PROJECT_ID}/resources/create_mysql.sql --quiet

Click Check my progress to verify the objective. Import a SQL file into MySQL

Create Datastream resources

Now that all the initial resources are deployed, create the Datastream connection profiles and stream to begin replication.

In the Cloud Console, click the Navigation menu icon on the top left of the screen:


Then navigate to Analytics > Datastream > Connection Profiles


Click Enable to enable the Datastream API.


If you are redirected to the Cloud Console welcome page, navigate back to Datastream as you did above.

Click Check my progress to verify the objective. Enable Datastream API

Create Connection Profiles

Create two connection profiles, one for the MySQL source, and another for the Cloud Storage destination.

MySQL connection profile

In the Cloud Console, navigate to the Connection Profiles tab and click CREATE PROFILE.


Select MySQL Connection profile type.


Use mysql-cp as the name and ID for your connection profile.

Enter the database connection details:

  • The IP and port of the Cloud SQL for MySQL instance created earlier
  • Username: root, password: password123


Leave the encryption as NONE. Click CONTINUE.

Select the IP allowlisting connectivity method, and click CONTINUE.

Click RUN TEST to make sure that Datastream is able to reach the database.


Cloud Storage connection profile

In the Cloud Console, navigate to the Connection Profiles tab and click CREATE PROFILE.

  • Select Cloud Storage connection profile type.
  • Use gcs-cp as the name and ID for your connection profile.
  • Choose the bucket created earlier, and enter /data as the connection profile path prefix.


Click Check my progress to verify the objective. Create Connection Profiles

Create the stream

Create the stream which connects the connection profiles created above and defines the configuration for the data to stream from source to destination.

In the Cloud Console, navigate to the Streams tab and click CREATE STREAM.


  • Use test-stream as the name and ID for your stream.
  • Select MySQL as the source.
  • Click CONTINUE.


Select the mysql-cp created in the previous step. You can test connectivity by clicking RUN TEST, then click Continue once the test passes.


Mark the tables you want to replicate - for this lab, only replicate the test database, then click CONTINUE.


Select the Cloud Storage bucket you created in the previous step, then click CONTINUE.


Do not add any stream path in the next step, you will use the path defined in the Connection Profile.



Finally, validate the stream details by clicking on RUN VALIDATION. Once validation completes successfully, click CREATE AND START.



Click Check my progress to verify the objective. Create Stream

Create a BigQuery dataset

Using Cloud Shell, run the following bq command.

bq mk dataset

Deploy Dataflow job

The Dataflow job can be created from the UI:

Navigation menu > Analytics > Dataflow > Jobs > Create job from template

However, use gcloud to ensure the variables are submitted correctly.

gcloud services enable gcloud beta dataflow flex-template run datastream-replication \ --project="${PROJECT_ID}" --region="us-central1" \ --template-file-gcs-location="gs://dataflow-templates-us-central1/latest/flex/Cloud_Datastream_to_BigQuery" \ --enable-streaming-engine \ --parameters \ inputFilePattern="gs://${PROJECT_ID}/data/",\ gcsPubSubSubscription="projects/${PROJECT_ID}/subscriptions/datastream-subscription",\ outputProjectId="${PROJECT_ID}",\ outputStagingDatasetTemplate="dataset",\ outputDatasetTemplate="dataset",\ outputStagingTableNameTemplate="{_metadata_schema}_{_metadata_table}_log",\ outputTableNameTemplate="{_metadata_schema}_{_metadata_table}",\ deadLetterQueueDirectory="gs://${PROJECT_ID}/dlq/",\ maxNumWorkers=2,\ autoscalingAlgorithm="THROUGHPUT_BASED",\ mergeFrequencyMinutes=2,\ inputFileFormat="avro"

You can see your running job by navigating Navigation Menu > Dataflow > Jobs.

View the Data in BigQuery

The Dataflow job will replicate your data into the BigQuery dataset supplied. View these tables in the BigQuery UI: Navigation Menu > BigQuery.

Click Check my progress to verify the objective. Create a dataset and deploy Dataflow job


Datastream is an important tool in your Data Migration and Analytics toolkit! You have learned the basics of MySQL to BigQuery with Datastream and Dataflow.

Google Cloud training and certification

...helps you make the most of Google Cloud technologies. Our classes include technical skills and best practices to help you get up to speed quickly and continue your learning journey. We offer fundamental to advanced level training, with on-demand, live, and virtual options to suit your busy schedule. Certifications help you validate and prove your skill and expertise in Google Cloud technologies.

Manual Last Updated July 1, 2022

Lab Last Tested July 1, 2022

Copyright 2022 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.