As businesses strive to create more effective marketing campaigns, many are turning to social media platforms like Facebook to reach their target audiences. While Facebook provides a wealth of data about its users, analyzing this data can be difficult and time-consuming. Keeping tabs on your Facebook page becomes essential to see whether your posts perform well or if followers engage with your content.
By integrating Facebook Page Insights with BigQuery, companies can access and analyze all the data that Page Insights collects in a single place. This allows businesses to quickly and easily identify trends in their Page’s data and decide how to improve their marketing efforts.
In this blog, you’ll learn about the features of Facebook Page Insights and BigQuery. You’ll also learn how to integrate Facebook Page Insights to BigQuery.
Table of Contents
- What are Facebook Page Insights?
- What is Google BigQuery?
- Why Integrate Facebook Page Insights to BigQuery?
- Methods to Integrate Facebook Page Insights to BigQuery
Managing Facebook Page
What are Facebook Page Insights?
Facebook Page Insights is a tool that provides insights into a Facebook page’s performance. It includes data on likes, reach, impressions, and engagement. Page Insights can help businesses understand what content is resonating with their audience and improve their Facebook marketing. This also includes data on how many people are viewing your Page, types of posts, and demographics.
It can help you track your progress over time. Are you getting more Page views? Are people engaging more with your content? But more importantly, Facebook Page Insights can help you understand your audience better. What kind of content do they respond to? What time of day are they most active? What demographics do they fall into? This data can be extremely valuable in helping you create content that resonates with your audience and helps you grow your business.
Key Features of Facebook Page Insights
The Facebook Page Insights dashboard displays top metrics. You can see a snapshot of your page’s performance on any given day and adjust your strategy accordingly. Top metrics include:
- Page Views: The number of times Facebook and non-Facebook users have viewed your page.
- Actions on the Page: Number of actions (such as clicking on a website link or asking for directions to your store) taken by users on your business page.
- Page Likes: Number of Facebook users who liked your business page.
- Post Engagement: How many Facebook users liked, reacted, shared, or commented on your post.
- Post Reach: The number of Facebook users that saw your post.
You also have video insights, which can tell you how your videos perform. Key metrics include:
- Minutes Viewed: The total time users have spent watching your videos.
- Audience Retention: How long have you maintained your video’s audience in a video.
- Video Views: The number of users who watched your video for three seconds or more.
What is Google BigQuery?
BigQuery is a fully managed data processing and storage service offered by Google and a part of the Google Cloud Platform. BigQuery is designed to handle extensive data sets, up to billions of rows. It uses a columnar data storage format and allows users to write SQL-like queries. In addition, users can also separate storage and compute resources to run queries on data where it’s stored.
BigQuery pricing is based on three factors: analysis, storage, and data ingestion & extraction.
BigQuery Analysis pricing is based on usage and is measured in terabytes (TB) processed. It’s available both on-demand and flat rate. You can view your usage in the BigQuery console, and the cost will be displayed in your monthly statement. The pricing for storage is based on the amount of data you store in BigQuery, which costs $5/TB/month. BigQuery pricing for data ingestion and extraction is based on the amount of data you ingest and extract from BigQuery. The cost is $1 per TB ingested and $2 per TB extracted.
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
Google BigQuery is a powerful cloud-based data warehouse that offers several features that can benefit businesses. Here are five of the top features of BigQuery:
- Multi-cloud Functionality: With BigQuery, you can query data stored in any cloud, including Amazon S3, Google Cloud Storage, and Azure Storage. This can be helpful if you want to consolidate data from multiple cloud-based data sources. BigQuery supports multi-cloud functionality by separating storage and compute resources, allowing it to run computations where the data is stored.
- Built-in ML Integration: BigQuery includes built-in machine learning capabilities. This means you can use BigQuery to train and deploy machine learning models. SQL practitioners can quickly build ML models in BigQuery itself.
- Geospatial Analysis: BigQuery offers powerful geospatial analysis capabilities. This can be helpful for businesses that need to analyze data that has a spatial component. It can convert longitudes and latitude columns into geographical points. You can visualize geospatial data through Google Earth Engine, Jupyter Notebook, and BigQuery Geo Viz.
- Flexibility: BigQuery supports a wide range of data types and can be easily integrated with other Google products, such as Data Studio.
- Automated Data Transfer Servicer: With BigQuery’s automated data transfer service, you can automate data transfer from one Google cloud platform to BigQuery. You can also add data backfills to compensate for any outages during ingestion. You can access this data transfer service from Cloud Console, bq command-line-tool, and BigQuery Data Transfer Service API.
Why Integrate Facebook Page Insights to BigQuery?
The integration of Facebook Page Insights to Bigquery will guarantee improved business intelligence for Facebook advertising campaigns. Faster access to accurate data will enable you to make quick business decisions. Improve your data’s control for trend visualization in real-time. As a result of Facebook Page Insights to BigQuery Integration, you will save money on Facebook Page Insights and increase ROI.
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Methods to Integrate Facebook Page Insights to BigQuery
- Method 1: Using Hevo to Set Up Facebook Page Insights to BigQuery
- Method 2: Using Custom Code to Move Data from Facebook Page Insights to BigQuery
Method 1: Using Hevo to Set Up Facebook Page Insights to BigQuery
Hevo provides an Automated No-code Data Pipeline that helps you move your Facebook Page Insights to BigQuery. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ data sources(including 40+ free sources)but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss.
Using Hevo, you can connect Facebook Page Insights to BigQuery in the following 2 steps:
- Step 1: Configure Facebook Page Insights as the Source in your Pipeline by following the steps below:
- Step 1.1: In the Asset Palette, select PIPELINES.
- Step 1.2: In the Pipelines List View, click + CREATE.
- Step 1.3: Select Facebook Page Insights on the Select Source Type page.
- Step 1.4: Click + ADD FACEBOOK PAGES ACCOUNT on the Configure your Facebook Pages Account page.
- Step 1.5: To continue as Company Name>, log into your Facebook Pages account and click Continue.
- Step 1.6: Click Next after choosing one or more pages whose data you want to copy.
- Step 1.7: Enable all of the permissions and click Done to approve Hevo.
- Step 1.8: In the Confirmation dialogue, click OK.
- Step 1.9: Enter the following information on the Configure your Facebook Pages Source page:
- Pipeline Name: A name for the Pipeline that is unique.
- Pages: Choose one or more pages whose information you want to copy
- Historical Sync Duration: The amount of time that the historical data must be ingested.
- Step 1.91: TEST & CONTINUE is the button to click.
- Step 1.92: Set up the Destination and configure the data ingestion.
- Step 2: To set up Google BigQuery as a destination in Hevo, follow these steps:
- Step 2.1: In the Asset Palette, select DESTINATIONS.
- Step 2.2: In the Destinations List View, click + CREATE.
- Step 2.3: Select Google BigQuery from the Add Destination page.
- Step 2.4: Choose the BigQuery connection authentication method on the Configure your Google BigQuery Account page.
- Step 2.5: 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 2.6: 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 2.5: Click Test Connection to test connectivity with the Amazon Redshift warehouse.
- Step 2.6: Once the test is successful, click SAVE DESTINATION.
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Method 2: Using Custom Code to Move Data from Facebook Page Insights to BigQuery
This method of Facebook Page Insights to BigQuery Integration requires technical knowledge. Page Insights feature of Facebook includes information about the number of people who have seen a Page’s posts, the number of people who have liked a Page’s posts, and the number of people who have shared a Page’s posts. BigQuery is a data analysis platform from Google that allows businesses to analyze large datasets.
By integrating Facebook Page Insights to BigQuery, teams can quickly combine their marketing, CRM, and social media data. Businesses will also be able to calculate the ROI for each campaign with Facebook Page Insights to BigQuery integration. Combine customer relationship management and sales data to calculate the ROI of your campaigns.
Steps to integrate Facebook Page Insights to BigQuery:
Export Facebook Page Insights
- Click Pages in the left menu in your News Feed.
- Go to your Page.
- Click Insights in the left menu.
- Choose from Page, Post, and Video data.
- Click Export Data in the top right.
- Go to Layout and select Make New Custom Layout for custom reports.
- Enter a sheet name and then click Add.
- Select the information you’d like to add to your sheet.
- When you’ve finished selecting your custom data, click Apply.
- Select a data type, file format, and date range.
- Click Export Data again.
Importing Data to Google BigQuery
There are 2 ways to load data from Facebook Page Insights to BigQuery: batch loading and streaming. Batch loading allows you to load the source data into a table in a single batch operation. With streaming, you can frequently send smaller batches of data in real-time, so the data is instantly available for querying.
You will create a load job to load the CSV files in Google BigQuery. The source data can have formats such as Avro, JSON, ORC, Parquet, etc.
Before you can start the load job, you’ll have to give the following IAM permissions:
bigquery.tables.create bigquery.tables.updateData bigquery.tables.update bigquery.jobs.create
You’ll also have to include these permissions in the predefined IAM roles:
roles/bigquery.dataEditor roles/bigquery.dataOwner roles/bigquery.admin (includes the bigquery.jobs.create permission) bigquery.user (includes the bigquery.jobs.create permission) bigquery.jobUser (includes the bigquery.jobs.create permission)
Follow these steps to load CSV data:
- Go to the BigQuery page in Google Cloud Console.
- Select a dataset from the Resource section.
- Click Create Table in the Dataset Info section.
- On the Create Table page, select Upload in the Source section.
- Browse the file from your local files.
- Click Open.
- Select your file’s format in the File Format section.
- On the Create Table page, Go to the Destination section.
- Enter an appropriate dataset name.
- Next, enter the name of the table you’re creating in BigQuery.
- Enter schema definition in the Schema section.
- Click Create Table.
By integrating Facebook Page Insights to BigQuery, businesses can gain quick and easy access to the data they need to make informed decisions about their marketing efforts. However, manually downloading and uploading reports to BigQuery through load data jobs is time-consuming. It also doesn’t allow users to see reports and insights in real-time. Alternatively, you can use a low/no-code platform like Hevo to access Facebook Page Insights in real-time in Google BigQuery.Visit our Website to Explore Hevo
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