Google BigQuery (Cloud Destinations)
MetaRouter makes it easy to send your data to Google BigQuery. Once you follow the steps below, your data will be routed through our platform and pushed to BigQuery in the appropriate format. Before we get started, there are a couple important things to note about this integration.
- You will not immediately see events in your BigQuery upon configuration. The first time your data is loaded, it will take some added time before data will start flowing as our system establishes a connection.
- Data is not streamed into BigQuery in real time as it is for our other destinations. Rather, our loader operates on an hourly basis and each hourly job is queued up 15 minutes after the next hour starts. For example, the load for 2-3 PM will queue at 3:15 PM.
Now, to the good stuff!
BigQuery is Google Cloud Platform's custom take on a traditional Postgres database. In Google's words, "Google BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure." It's cost-effective at nearly any level, capable of scaling from gigabytes to petabytes without a loss in performance.
This guide will explain how to integrate BigQuery into MetaRouter's platform as a destination, allowing you to leverage Google's technology to access, store, and query your customer data.
Our connector periodically runs an ETL (Extract - Transform - Load) process that pulls raw event data in our data bucket, processes and transforms those raw events into a structured format, and then inserts structured event data from our bucket into your BigQuery cluster.
Note: If you already have your dataset created, you can skip this step
Once you've logged into your GCP account and activated BigQuery, it's time to create your BigQuery dataset.
Follow Google's guide to get your dataset created. Make sure that you keep this dataset name handy for when you go to put your information in on MetaRouter.
Note: Each unique
track event will create a new table, and each property sent creates a new column in that table. For this reason, think about creating a detailed tracking plan to make sure that all events being passed to MetaRouter are necessary and consistent.
Note: If you already have your auth json, you can skip this step
Now that you have your dataset created, it is time to get GCP authentication set up. This is accomplished through creating a service account.
You can see the steps for getting this set up here.
Once completed, you should have the following items ready to go:
- BigQuery dataset created (keep dataset name handy for the next part)
- The Data Location you selected
- Downloaded the JSON file with the key for you service account
- GCP credentials environment variables set up
If this list looks good, you are ready to jump over to the MetaRouter UI and create the destination!
Ensure that you give the Service Account the BigQuery Editor role in order for us to have proper permissions to load data into your cloud.
In the MetaRouter app, head to the pipeline you will be adding BigQuery to, and under Destinations click New Destination. From there, click on Google BigQuery and create a name for your destination (e.g. "Production BigQuery").
Now it is time to input the information from GCP and BigQuery into the Destination Details. Under Project ID, enter the GCP project you have the desired BigQuery dataset tied to. Then, under Data Location enter the location you selected when setup up your dataset (step 1). The location will most often be "Default", but if you selected another region, enter it here. Please use the region key (e.g. "US" or "europe-west3"). Lastly, under BigQuery Dataset, enter the name of your existing or newly created dataset (step 1).
Note: Make sure you only enter the dataset name, not the full dataset id
With those details in, you can go on to Create Connection. This is where you are granting MetaRouter the ability to interface with your GCP account using that service account JSON. Under Friendly Name, enter in your connection name (e.g. Production GCP Connection). Then, copy the contents of that service account JSON file into the field for Google Cloud Auth JSON. Once that is completed, hit Save at the bottom of the form.
That's it! You'll now be receiving a live stream of data from your application into your BigQuery dataset.
If you run into any issues using this or any of our destinations, feel free to reach out to us at email@example.com. Happy routing!
Your BigQuery Database will need to be free to receive traffic from our IP Address:
18.104.22.168. Please ensure that if you are restricting traffic, to allow access from this IP address for proper loading of data.
We infer data types based on the value of the data that we receive. For instance, a
sent_at field will be inferred as a timestamp, the text "red" as a string, etc.
If you would like to explicitly declare data types or perform other customizations for data that is loaded into BigQuery, please reach out to firstname.lastname@example.org.