Instagram is the go-to place for hundreds of millions of people to share their videos, photos, and stories daily, making it one of the most popular social media platforms today. With the advent of social media platforms, Gen Z prefers searching on Instagram and TikTok over Google Maps and Search, which is an indication that these social media platforms are here to stay.
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. This in turn can help you with data-driven decisions.
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.
Table of Contents
- What is Instagram Business?
- What is Google BigQuery?
- Why connect Instagram Business to BigQuery?
- Methods to Connect Instagram Business to BigQuery
What is Instagram Business?
Instagram is a visual social media platform that places a heavy emphasis on video and photo content. This can seem difficult for brands without a visual product, but they can get ahead of the competition by setting up a Business account on Instagram.
Instagram allows you to set up a business account for free. You can gain access to the following insights with your Instagram business account:
- Instagram Analytics/Insights
- Instagram Shop/Tagging Products in Posts
- Multiple Sections to Expand in Your Profile
- Instagram Advertising
- The Ability to Connect to a Scheduling App
You can leverage the Instagram Business account to increase revenue, improve brand visibility, and establish and measure audience engagement. It offers an excellent way to look for customers where they’re already spending their time. It can also offer actionable audience insights to use with all your marketing plan strategies.
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.
Google BigQuery uses a Columnar Storage format that is optimized for analytical queries to store data. BigQuery displays data in tables, rows, and columns, with full database transaction semantics support (ACID). To ensure high availability, BigQuery storage is automatically replicated across multiple locations.
Key Features of Google BigQuery
- 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.
- Storage: Google BigQuery leverages Google’s global storage system, Colossus, to store and optimize your data for free and with no downtime. To store data, Google BigQuery uses the opinionated Capacitor format in Colossus, which achieves various enhancements behind the scenes while burning a large amount of CPU/RAM, all without affecting query performance or imposing a bill limit.
- 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.
- 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.
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.
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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
- Method 2: Using Custom Scripts as Instagram Business to BigQuery Connector
Method 1: Using Hevo for Instagram Business to BigQuery IntegrationSIGN UP HERE FOR A 14-DAY FREE TRIAL
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, 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.
- Using a Service Account to connect:
- 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.
Deliver Smarter, Faster Insights with your Unified Data
Using manual scripts and custom code to move data into the warehouse is cumbersome. Changing API endpoints and limits, ad-hoc data preparation, and inconsistent schema make maintaining such a system a nightmare. Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work seamlessly.
- Wide Range of Connectors: Instantly connect and read data from 150+ sources including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
- 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.
- Near Real-Time Replication: Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.
- 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: Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
- 24×7 Customer Support: With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
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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.
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.
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 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.
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