What is BigQuery?
The BigQuery service from Google Cloud Platform allows you to produce, manage, distribute, and query data, as well as perform data warehousing, analytics, and machine learning. With BigQuery, you can analyze large amounts of data and use cloud-based parallel computing to fulfil your big data demands.
It enables you to run petabyte-scale queries and receive results in seconds, as well as providing the advantages of a fully managed system. Since it is built on serverless architecture, you don’t have to worry about infrastructure or hardware administration. Additionally, BigQuery automatically replicates your data across multiple sites. In terms of company data, this guarantees exceptional stability and performance.
Unlike most other cloud-based data warehouses, this Google solution requires little to no upkeep. Therefore, you can access Google’s Data Warehouse from any location where Google Cloud is available.
Export cloud billing data to BigQuery
GCP’s Billing Exports feature enables users to export detailed data that can be used as BigQuery datasets. Now you’re in position to access your Cloud Billing data with Google BigQuery for extensive analysis, or you can visualize your data with Google Data Studio. Data can also be exported to a JSON file using the GCP Billing Export function, for use with other tools.
1. Sign in to your Google Cloud Console, and select the particular project you would like to export.
2. From the navigation menu in the top left, select Billing.
3. On the Billing page, a prompt arises. Select
GO TO LINKED BILLING ACCOUNT to continue with the same, or
MANAGE BILLLING ACCOUNTS to choose a different billing account.
4. Once the billing account is selected, open the navigation menu>Billing export>BigQuery export.
5. From the Project list, choose the project that contains your BigQuery dataset. You’ll be requested to establish a BigQuery dataset if you don’t already have one.
How to create a dataset if you don’t have one
- In the Google Cloud Console, go to the Google BigQuery Page.
- In the Explorer Panel, choose the Project for which you wish to build a dataset.
+Create Datasetfrom the drop-down menu.
- Certain fields, such as Dataset Id, data location, and data expiry, will be required. You will be able to perform billing exports after you have your dataset ready.
Things to keep in mind
- This should only be done once per billing account, not once every project.
- When you arrange the export to BigQuery, Google begins exporting the data, meaning you won’t be able to access the data before that particular day.
- If you’re an MSP and don’t want your clients to be charged for BigQuery, create an internal project in Google that you can use to bill any charges to your account rather than the client’s.
BigQuery Pricing & Costs
There are various different ways to utilize the free tiers in BigQuery to ensure minimal expenditure. Using BigQuery to store consumption data is normally cheap, here are a few ways you can optimize your BigQuery usage.
- It’s free to load data into the selected dataset; this activity makes use of BigQuery’s pooled resources to import data in batches.
- The cost of exporting and analyzing Cloud Billing data with BigQuery is determined by how much data you stream, store, and query.
- In the same query, avoid joining the same subquery several times. Rather, save this subquery as an intermediate table and query it from there. The cost of storage is substantially lower than the cost of recurrent querying. Start with the biggest table when combining two tables for optimal performance.
- Before sending the query to production, you should evaluate the expenses with Pricing Calculator. On-demand inquiries are priced according to the amount of bytes read, and we may determine the price depending on the number of bytes read.
- Instead of using the query, you should utilize the preview option to look at the sample data. BigQuery’s preview function is completely free.
Google Cloud Platform’s BigQuery service lets you create, manage, distribute, and query data, as well as execute data warehousing, analytics, and machine learning. You may analyze massive volumes of data with BigQuery and employ cloud-based parallel computing to meet your big data needs.
Users may export detailed Google Cloud billing data to a BigQuery dataset using GCP Billing Export to Google BigQuery (such as Utilization, Projected Costs, and Unit Prices). You may now use Google BigQuery to perform deep analysis on your Cloud Billing data, or you can use Google Data Studio to display it. The GCP Billing Export method may also export data to a JSON file for usage with other tools.
In conclusion, BigQuery is a great symbiotic tool to use with your GCP billing datasets. We must keep in mind that for anything to display the desired results, all the applicable steps need to be implemented thoroughly. Utilizing BigQuery should be done with comprehensive knowledge in order to avoid expenditure on redundant data and processes.