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With the ongoing shift toward Google Analytics 4 across the internet, Google’s BigQuery has also started to step into the spotlight. But unless you are a data engineer or a data scientist or someone who is aware of the architecture behind analytics, BigQuery and the notion of data warehousing may seem foreign to you. But don’t worry, we are here to break it all down for you.
What is BigQuery?
BigQuery is Google’s enterprise data warehouse solution. A data warehouse is a repository for historical and current analytics data and is central to any data analytics system.
In a typical data architecture, data from various sources (whether it’s website, CMS, online store or app) is extracted, transformed into a useful form and then loaded into a data warehouse through a process called ETL (Extract, Transform, Load). Once data is in the data warehouse, it can then be used as a single source for all analysis or reporting needs.
BigQuery provides a robust infrastructure for storing and querying data in the form of tables, making data analysis and visualization a seamless process using SQL. It goes beyond just storing data, as it offers seamless integration with various data sources like Google Sheets, Google Cloud Storage, and Google Drive, enabling diverse utilization of the data across different platforms and applications.
With BigQuery’s built-in machine learning capabilities, organizations can leverage its power to effortlessly create predictive models and unlock valuable insights from their data. This allows businesses to make data-driven decisions and stay ahead in a competitive landscape.
Why use BigQuery?
There are several compelling reasons to utilize BigQuery for your data analytics endeavors.
Google Analytics Export:
For most businesses, the main reason to use BigQuery is it’s integration with Google Analytics 4. BigQuery seamlessly integrates with Google Analytics 4 (GA4), allowing you to export your GA4 data directly into BigQuery. By leveraging this integration, you gain access to the raw Google Analytics data and through custom queries, you can unlock deeper analysis and sometimes more accurate metrics than what is available within GA4’s standard reporting.
Reporting API Limitations:
With the switch to Google Analytics 4, Google have enforced a limit on the number of queries / amount of data you can extract from GA4 for external reporting. This means if you’re using GA4 to power dashboards made in Looker Studio or PowerBI, they will hit this limit often and you’ll be stuck waiting for the limits to reset. By utilising BigQuery, you can avoid these API limitations and make your reports more less prone to breaking.
Data Ownership and Accuracy:
BigQuery enables you to maintain ownership and control over your data. With full autonomy, you can define access permissions, manage encryption keys, and ensure the security and integrity of your valuable information. Additionally, by utilizing BigQuery, you can work with more accurate and detailed data, eliminating sampling biases and improving the precision of your analysis.
Flexibility with Data Formats:
BigQuery supports a wide range of data formats, including structured, semi-structured, and nested data. This flexibility allows you to work with diverse data types, such as JSON, CSV, Avro, Parquet, and more. You can easily load data into BigQuery from various sources, transform it as needed, and perform queries across different data formats. This versatility enables you to integrate and analyze data from multiple systems and sources within a single platform.
Real-Time Data Analysis:
BigQuery offers both batch and streaming ingestion capabilities. With streaming ingestion, you can process and analyze data in real-time as it arrives, allowing you to gain immediate insights and react promptly to changing business conditions. Whether you need to monitor website traffic, analyze user behavior, or perform real-time anomaly detection, BigQuery empowers you to derive insights from streaming data and take timely actions.
How much does BigQuery cost?
BigQuery employs a pricing model based on data storage and data processing. In terms of data storage, Google Cloud will give you the first 10GB of storage free each months. After that, you’ll pay $0.023 per GB (varying per region). Unless you’re a particularly high trafficked website, then it’s unlikely you’ll generate more than 10GB of data from Google Analytics a month.
In terms of querying, BigQuery charges based off of the amount of data that is touched per query at $6.5 per TB. While this can get rapidly complicated (BigQuery charges per column touched and can cache query results for a certain amount of time), in general you’d have to query months or years worth of Google Analytics data on a regular basis to accrue any significant costs.
So if you’re switching to Google Analytics 4, even if you don’t know if you’ll need extensive reporting, with the minimal storage costs on data, we’d recommend getting started with BigQuery to ensure your data is available in case you do want to run analysis on it. Otherwise, if you’re looking for a data warehousing solution, then BigQuery might be the answer for you.
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