Keeping track of all your business data and using this data to drive business decisions to boost your company can be a cumbersome task if you don’t have it available in an analysis-ready format. You can leverage Data Warehouses like Google BigQuery to move all your data to a central location, where you can easily deep-dive to extract actionable insights. Connecting Instagram API BigQuery opens doors to powerful analytics and marketing optimization, which helps in the growth of business.
In this article, we’ll go through two methods to move data from Instagram Business to BigQuery: using Custom Scripts, and through a no-code Data Pipeline, Hevo.
Methods to Connect Instagram Business to BigQuery
Here are the two methods you can use to set up Instagram Business to BigQuery migration:
Method 1: Using Hevo for Instagram Business to BigQuery Integration
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Hevo is a fully-managed, Automated No-code Data Pipeline that can load data from 150+ Sources(including 40+ free sources) such as Instagram Business to BigQuery. Hevo is the only real-time ELT No-code data pipeline platform that cost-effectively automates data pipelines that are flexible to your needs.
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Hevo’s fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. Using Hevo, Instagram Business to BigQuery Migration can be done in the following 2 steps:
Configure Instagram Business as a Source
- Step 1: Pick the PIPELINES option from the Asset Palette.
- Step 2: To create a new pipeline, click the + sign in the Pipelines List View.
- Step 3: On the Select Source Type page, choose Instagram Business as your source.
- Step 4: On the page that allows you to configure your Instagram Business account, select the option to + ADD INSTAGRAM BUSINESS ACCOUNT.
- Step 5: You can access your Instagram Business account by logging in with the Facebook account that is associated with your Instagram account (s). Through the user’s Facebook login, Hevo can connect to the associated Instagram Business account.
- Step 6: After selecting the Instagram Business account(s) whose data you wish to copy, proceed by clicking the Next button.
- Step 8: To grant Hevo access to the data, enable all four of the available options, and then click the Done button.
- Step 9: Click the OK button when the confirmation dialogue appears.
- Step 10: Enter the following information into the corresponding fields on the Configure your Instagram Business Source page:
- Name of Pipeline: A one-of-a-kind moniker for the pipeline, not exceeding 255 characters in length.
- Instagram Business Account: Your business page on Instagram. The default value is all.
- Historical Sync Duration: This refers to the amount of time that must pass before historical information can be absorbed.
- Step 11: Simply select the TEST & CONTINUE button.
- Step 12: Proceed by configuring the data ingestion and the destination, respectively.
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.
- 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.
For more information on how you can use Hevo connectors to complete this integration process, refer to the documentation links:
Check out how Hevo can help you:
- In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
- Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
- Transparent Pricing: Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs.
- Security: Discover peace with end-to-end encryption and compliance with all major security certifications including HIPAA, GDPR, and SOC-2.
Method 2: Using Custom Scripts as Instagram Business to BigQuery Connector
In this Instagram Business Bigquery connector method, you’ll be moving the data to Redshift first using a custom script. Next, after you’ve moved the data into Redshift, you can move it into Google BigQuery as mentioned below.
Step 1: Moving Data from Instagram Business to Redshift
The method is pretty straightforward. First, you gather the data from Instagram Business, transform the data so that it can be understood by Amazon Redshift, and finally, load the data to Amazon Redshift.
You can leverage the Instagram Graph API to extract the desired data. Next, you’ll have to set up CRON jobs to get newly updated data as it occurs regularly in your Instagram Business account. For mapping Instagram Business JSON files generated by Graph API, choose the fields you want and import them to create a Redshift table that matches this schema.
For further information, you can refer to the Instagram Business to Redshift article.
Step 2: 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.
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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.
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- 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.
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- 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:
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Why Connect Instagram Business to BigQuery?
Over a billion people use Instagram every month, and close to 90% of them follow at least one business. This highlights the importance of using Instagram for business, to stay ahead of your competition. Instagram has come a long way, from being a photo-sharing app to allowing you to market your business using hashtags, videos, and photos.
Querying massive volumes of data from Instagram Business might be tedious without the right infrastructure and hardware. With Google BigQuery, you can leverage super-fast, SQL-like queries against petabytes of data courtesy of Google infrastructure’s processing power. Since Google BigQuery is a no-setup, scalable service, it can provide real-time business insights into your data.
Apart from clarity, moving data from Instagram Business to BigQuery would also allow you to own your data and reporting. This can allow you to stay ahead of your competition and make data-driven business decisions.
Limitations of connecting Instagram Business to BigQuery Using Custom Scripts
Connecting Instagram Business to BigQuery using custom scripts accompanies certain limitations
- Human Errors:
Data transfers from Instagram to BigQuery might be vulnerable to human errors like data preparation, extraction, transformation and loading. This results in incorrect data uploads, inaccuracies and inconsistencies.
- Programming Experience:
Transferring data from Instagram to BigQuery might need some technical or programming expertise as it demands a proficiency in programming languages, APIs, data formats and data manipulation techniques.
Conclusion
This blog talks about the two methods that you can use for seamless Instagram Business Bigquery integration: using Custom Scripts, and a no-code Data Pipeline solution, Hevo. It also gives a brief introduction to Instagram Business and Google BigQuery before delving into the Instagram Business Bigquery migration steps.
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 Instagram Business– 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.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!
Amit is a Content Marketing Manager at Hevo Data. He enjoys writing about SaaS products and modern data platforms, having authored over 200 articles on these subjects.