To carry out analysis on the data that you pull from a Shopify Webhook, you’ll have to move it to a fully-managed data warehouse that allows you to extract valuable, actionable insights from it. This is where Google BigQuery comes in. It is a highly scalable tool that will act as a central repository of data and vastly simplify your data analysis.
In this article, you’ll go through two methods to seamlessly move data from Shopify Webhook to BigQuery. If you’re looking for a no-code connector to replicate data from Shopify Webhook to BigQuery in minutes, you can take a look at Hevo. On the other hand, if you’d like to use custom code to build a pipeline, we have a section listing the steps required for the same. Either way, we’ve got you covered. Let’s dive in!
What is Shopify Webhook?
Shopify Webhook allows you to run code in response to a specific event in your Shopify store or stay on top of Shopify data. Webhooks are a handy alternative to continuously polling for changes to the data present in a store.
For instance, a webhook can inform your app when a merchant modifies a product in the Shopify admin. Your app can then execute an action when a product change takes place.
You can make and manage a Shopify Webhook by leveraging the Shopify admin dashboard. The admin dashboard despite being highly intuitive doesn’t allow the developers to perform automated tasks on their webhooks. Users need to log in to the admin interface manually to execute webhook-related tasks.
As a solution to this problem, Shopify opened up its Webhook API allowing programmatic interaction with Shopify Webhooks, thus expanding the scope of what you can achieve with it.
Use Cases of Shopify Webhooks
Here are a few common use cases of Shopify Webhooks:
- Gathering data for data warehousing.
- Removing customer data from a database for app uninstalls.
- Integrating with accounting software.
- Sending notifications to pagers and IM clients.
- Informing shipping companies about orders and filtering order items.
What is Google BigQuery?
Google BigQuery is Google’s data warehousing solution. As a part of the Google Cloud Platform, it deals in SQL, similar to Amazon Redshift. Google BigQuery helps businesses pick the most appropriate software provider to assemble their data, based on the platform the business leverages.
You can easily interact with Google BigQuery through its web user interface, through a command-line tool. Google also provides various client libraries that you can choose from to interact with Google BigQuery through your application.
Key Features of Google BigQuery
- Federated Query: Google BigQuery employs a novel method for sending a query statement to an external database and receiving the results as a temporary table if the user’s data is stored in Bigtable, GCS, or Google Drive.
- Flexible Scaling: You don’t have to explicitly tweak the cluster with BigQuery since computing resources are automatically adjusted according to the workload, and it can easily extend storage to Petabytes on demand. Patching, updates, computing, and storage resource scaling are all handled by Google BigQuery, making it a fully managed service.
- Programming Access: Google BigQuery may be easily accessed in applications using Rest API queries, Client Libraries such as Java, .Net, Python, the Command-Line Tool, or the GCP Console. It also includes query and database management tools.
What is the Importance of Shopify Webhook to BigQuery Integration?
As your app continues to grow, it can become difficult to track traffic from Shopify’s platform. Hence, if you need to manage large volumes of event notifications for a reliable and scalable system, you can configure subscriptions to send these webhooks to Google Cloud Pub/Sub.
You’ll find it to be easier than the traditional method of going through HTTPS. On top of this, as your app continues to grow, it’ll be difficult to keep track of all the event notifications in various small data repositories. The straightforward solution is to opt for a central repository of data that can scale to meet the needs of your growing company. Google BigQuery is the ideal choice for you if you want a managed, scalable, and efficient data warehouse.
Therefore, all you need to do is connect your Shopify Webhooks to Google BigQuery and you’re set to analyze your data to drive key business decisions.
In the following sections, we’ll be taking a look at two Shopify Webhook BigQuery connector methods. The first method will illustrate how you can leverage an easy, no-code, automated Data Pipeline for the connection. The second method will walk you through the usage of custom scripts (geared towards people with more coding experience) to achieve the same. Pick the one you feel most comfortable with!
If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc can change, creating and managing these pipelines can be a soul-sucking exercise.
Hevo’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes.
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Shopify Webhook BigQuery Migration Methods
Here are two methods you can implement for seamless Shopify Webhook to Bigquery migration:
Method 1: Using Hevo as a Shopify Webhook to BigQuery Connector
Hevo is a fully-managed, Automated No-code Data Pipeline that can load data from 150+ Sources(including 40+ free sources) such as Shopify Webhook to BigQuery.
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Hevo can also enrich and transform the data into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Using Hevo, Shopify Webhook to BigQuery Migration can be done in the following 2 steps:
Configure Shopify Webhook as a Source
- Step 1: From the list of sources offered to you, you can choose Shopify as the source.
- Step 2: Next, you need to enter the pipeline name and click continue.
- Step 3: In this step, you’ll have the option to choose the destination if you’ve already created it. You can either choose an existing destination or create a new one by clicking on the ‘Create Destination’ button.
- Step 4: On the final settings page, you’ll have the option of selecting ‘Auto-Mapping’ and JSON parsing strategy. If you’re interested in learning more about the JSON parsing strategy, you can click here.
- Step 5: Click Continue. You should be seeing a webhook URL that gets generated on the screen.
- Step 6: Next, you need to copy the generated webhook URL and add it to your Shopify account. If you’d like to get a more detailed guide on how webhooks work in Shopify, you can click here.
Configure BigQuery as a Destination
To set up Google BigQuery as a destination in Hevo, follow these steps:
- Step 1: In the Asset Palette, select DESTINATIONS.
- Step 2: In the Destinations List View, click + CREATE.
- Step 3: Select Google BigQuery from the Add Destination page.
- Step 4: Choose the BigQuery connection authentication method on the Configure your Google BigQuery Account page.
- Step 5: Choose one of these:
- Using a Service Account to connect:
- Service Account Key file, please attach.
- Note that Hevo only accepts key files in JSON format.
- Go to CONFIGURE GOOGLE BIGQUERY ACCOUNT and click it.
- Using a user account to connect:
- To add a Google BigQuery account, click +.
- Become a user with BigQuery Admin and Storage Admin permissions by logging in.
- To grant Hevo access to your data, click Allow.
- Step 6: Set the following parameters on the Configure your Google BigQuery page:
- Destination Name: A unique name for your Destination.
- Project ID: The BigQuery Project ID that you were able to retrieve in Step 2 above and for which you had permitted the previous steps.
- Dataset ID: Name of the dataset that you want to sync your data to, as retrieved in Step 3 above.
- GCS Bucket: To upload files to BigQuery, they must first be staged in the cloud storage bucket that was retrieved in Step 4 above.
- Enable Streaming Inserts: Enable this option to load data via a job according to a defined Pipeline schedule rather than streaming it to your BigQuery Destination as it comes in from the Source. To learn more, go to Near Real-time Data Loading Using Streaming. The setting cannot be changed later.
- Sanitize Table/Column Names: Activate this option to replace the spaces and non-alphanumeric characters in between the table and column names with underscores ( ). Name Sanitization is written.
- Step 7: Click Test Connection to test connectivity with the Amazon Redshift warehouse.
- Step 8: Once the test is successful, click SAVE DESTINATION.
Method 2: Using Custom Scripts for Shopify Webhook BigQuery Integration
You can use this method if you want to manually code the scripts to set up the connection from Shopify Webhook to BigQuery. Full disclosure, go for this method if you want to spend time building and managing pipelines. But, if you would like to save time for more important tasks, you can pick a solution that does the heavy lifting for you.
Moving Data from Shopify Webhook to Redshift
You can connect Shopify Webhook to Redshift in 6 simple steps:
- First, you’ll need to create a Shopify Webhook by logging into your Shopify account. Here, you’ll have to choose the events for which you want to send data when that event takes place. Once you’ve chosen the data format, URL, and webhook API version, you can move on to the next step.
- Next, you’ll need to retrieve AWS Redshift Cluster Public Key and Cluster Node IP Addresses.
- The Amazon Redshift Cluster Public Key will establish a secure SSL connection between the remote host and the Amazon Redshift cluster.
- The next step in the process involves creating a manifest file on your local machine. The manifest file will contain entries of the SSH host endpoints and the commands to be completed on the machine to send data to Amazon Redshift.
- You can then upload the manifest file to an Amazon S3 Bucket and give read permissions on the object to all the users.
- Finally, to load data into Amazon Redshift you can use the COPY command to connect to your local machine and load the data extracted from Shopify Webhook to Redshift.
If you want an in-depth guide on the steps mentioned above, you can click here.
Moving Data from Redshift to BigQuery
You’ll be leveraging the BigQuery Transfer Service to copy your data from an Amazon Redshift Data Warehouse to Google BigQuery. BigQuery Transfer Service engages migration agents in GKE and triggers an unload operation from Amazon Redshift to a staging area in an Amazon S3 bucket. Your data would then be moved from the Amazon S3 bucket to BigQuery.
Here are the steps involved in the same:
- Step 1: Go to the BigQuery page in your Google Cloud Console.
- Step 2: Click on Transfers. On the New Transfer Page you’ll have to make the following choices:
- For Source, you can pick Migration: Amazon Redshift.
- Next, for the Display name, you’ll have to enter a name for the transfer. The display name could be any value that allows you to easily identify the transfer if you have to change the transfer later.
- Finally, for the destination dataset, you’ll have to pick the appropriate dataset.
- Step 3: Next, in Data Source Details, you’ll have to mention specific details for your Amazon Redshift transfer as given below:
- For the JDBC Connection URL for Amazon Redshift, you’ll have to give the JDBC URL to access the cluster.
- Next, you’ll have to enter the username for the Amazon Redshift database you want to migrate.
- You’ll also have to provide the database password.
- For the Secret Access Key and Access Key ID, you need to enter the key pair you got from ‘Grant Access to your S3 Bucket’.
- For Amazon S3 URI, you need to enter the URI of the S3 Bucket you’ll leverage as a staging area.
- Under Amazon Redshift Schema, you can enter the schema you want to migrate.
- For Table Name Patterns, you can either specify a pattern or name for matching the table names in the Schema. You can leverage regular expressions to specify the pattern in the following form: <table1Regex>;<table2Regex>. The pattern needs to follow Java regular expression syntax.
- Step 4: Click on Save.
- Step 5: Google Cloud Console will depict all the transfer setup details, including a Resource name for this transfer. This is what the final result of the export looks like:
This completes the manual, albeit slightly cumbersome method to connect Shopify Webhook to BigQuery.
This article talks about how you can connect Shopify Webhook to BigQuery using two methods: you can either manually code custom scripts to set up the Shopify Webhook to BigQuery connection or you can leverage a no-code connector that does all the heavy lifting for you, allowing you to focus on more important business matters.
Visit our Website to Explore Hevo
Hevo will automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. Hevo provides a wide range of sources – 150+ Data Sources (including 40+ Free Sources) such as Shopify Webhook- that connect with over 15+ Destinations such as Google BigQuery. It will provide you with a seamless experience and make your work life much easier.
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