Today, organizations generate, store and manage huge volumes of data. Storing and querying data of such scale can be costly and time-consuming, especially for an organization that doesn’t have the appropriate Infrastructure. To overcome this hurdle, Google introduced BigQuery which is an Enterprise Data Warehouse that leverages the processing power of Google’s Infrastructure to enable super-fast SQL queries. It allows you to move data from your database/desired source to Google BigQuery for optimized performance.
Google offers a way to control and analyze your costs through the Reports Module and BigQuery. Google BigQuery allows you to automatically export your daily usage and cost estimates. One of the greatest advantages is that you can filter projects, dates, products, and services separately.
Upon a complete walkthrough of this article, you will gain a decent understanding of Google BigQuery along with the unique features that it offers. You will also learn how to perform GCP Billing Export to BigQuery in a seamless manner. Read along to learn more about the process of GCP Billing Export to BigQuery!
Table of Contents
- Introduction to Google BigQuery
- GCP Billing Export to Google BigQuery
- Steps to Create a Dataset for the Billing Data
- Steps to Perform GCP Billing Export to BigQuery
- Basic hands-on experience with Google Cloud Console.
Introduction to Google BigQuery
Google BigQuery is a robust Cloud-based Data Warehouse and Analytics platform. It is a serverless platform that does not require the installation of any software or the maintenance and management of large infrastructure. This is a very cost-effective solution for a growing business as it eliminates the need for large server rooms and the investment in hardware that is required in the case of traditional On-Premise databases. You can query Terabytes and Petabytes of data in a matter of just a few minutes using Google BigQuery’s Scalable and Distributed Analytics Engine.
Google BigQuery is Serverless and built to be highly Scalable. Google leverages its existing Cloud architecture to successfully manage a Serverless design, as well as various data models that enable users to store dynamic data. It also supports Machine Learning (ML) operations by allowing users to use the BigQuery ML functionality. BigQuery ML allows users to develop and train various Machine Learning Models by querying data from the desired database using built-in SQL capabilities.
Key Features of Google BigQuery
Some of the key features of Google BigQuery are as follows:
- Scalability: To provide consumers with true Scalability and consistent Performance, Google BigQuery leverages Massively Parallel Processing(MPP) and a Highly Scalable Secure Storage Engine. The entire Infrastructure with over a thousand machines is managed by a complex software stack.
- Serverless: The Google BigQuery Serverless model automatically distributes processing across a large number of machines running in parallel, so any organization using Google BigQuery can focus on extracting insights from data rather than configuring and maintaining the Infrastructure/server.
- Storage: Google BigQuery uses a Columnar architecture to store mammoth scales of datasets. Column-based Storage has several advantages, including better Memory Utilization and the ability to scan data faster than typical Row-based Storage.
- Integrations: Google BigQuery as a part of the Google Cloud Platform (GCP) supports seamless integration with all Google products and services. Google also offers a variety of Integrations with numerous third-party services, as well as the functionality to integrate with application APIs that are not directly supported by Google.
For further information on Google BigQuery, you can click here to check out their official website.
GCP Billing Export to Google BigQuery
GCP Billing Export to Google BigQuery allows you to automatically export detailed Google Cloud billing data (such as Usage, Cost Estimates, and Pricing Data) to a Google BigQuery dataset. Then you can use Google BigQuery to access your Cloud Billing data for detailed analysis, or you can visualize your data with a tool like Google Data Studio. GCP Billing Export method can also be used to export data to a JSON file. In the later section of this article, you will learn about the steps involved in performing GCP Billing Export to BigQuery.
Advantages of Exporting your Billing Data to Google BigQuery
There are numerous advantages to be gained from exporting your Billing Data to Google BigQuery. Some of them are listed below:
- Better Security: It is not necessary to grant analysts Billing permissions in order for them to analyze cost patterns.
- Visualization in Google Data Studio: The underlying Google BigQuery data can be visualized in Google Data Studio Dashboards, which can then be shared with the appropriate stakeholders. As a result, these stakeholders are not required to log in to the Google Cloud Platform in order to monitor spending patterns.
- Querying Capabilities: Using SQL in Google BigQuery allows you to break down Billing data into more customizable formats, as well as connect that data to other data sources.
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Steps to Create a Dataset for the Billing Data
If you don’t have a pre-existing dataset to house the Billing data then you will be prompted to create a new dataset to perform GCP Billing Export to BigQuery. Follow the steps given below to create a new dataset in Google BigQuery:
- Step 1: Navigate to the Google BigQuery Page in the Google Cloud Console.
- Step 2: Select the Project in the Explorer Panel for which you want to create a dataset.
- Step 3: Click on the +Create Dataset button.
- Step 4: You will be prompted to fill certain fields such as Dataset Id, data location, and data expiration. Once you have a dataset ready, you will be good to perform GCP Billing Export.
Steps to Perform GCP Billing Export to BigQuery
Now that you have a basic understanding of GCP Billing Export, you are ready to perform GCP Billing Export to BigQuery. Follow the steps given below to do so:
- Step 1: Sign In to the Google Cloud Platform Console and select your internal project.
- Step 2: Navigate to the main menu in the top left corner and select the Billing option.
- Step 3: If you have multiple Billing accounts, Navigate to the Linked Billing Account option to manage the billing for the project you’ve chosen. Click on Manage Billing Accounts to switch to a different billing account.
- Step 4: Choose the Billing account for which you want to export data.
- Step 5: Then click on Billing Export.
- Step 6: Once you click on Billing export, you will get two options- “File Export” and “BigQuery Export”. Select BigQuery Export.
- Step 7: From the Project list, select the project where your Google BigQuery dataset is stored. If you don’t already have a BigQuery dataset, you’ll be asked to create one. To create a new BigQuery dataset you can follow the steps mentioned in the previous section.
- Step 8: Once you have created the dataset, navigate back to the Billing Export section and choose the Billing account for which you want to export the data.
- Step 9: Click on the Edit Settings button and choose the dataset that you have just created.
- Step 10: Click on Save.
In this article, you learned about Google BigQuery along with the salient features that it offers. You also learned about the steps required to export your Google Cloud Platform Billing data to Google BigQuery. With your Data Warehouse, Google BigQuery live and running, you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.Visit our Website to Explore Hevo
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