Initially, web tracking and third-party cookies were the foundation of Universal Analytics. However, in today's digital world, users can quickly discover and purchase products from mobile apps in addition to websites. They also desire improved privacy regulations and greater data control. Google consequently unveiled GA4 as a new method of evaluation in the post-cookie era.
The most recent Google update states that fresh data will no longer be processed by Universal Analytics as of July 1, 2023. Your Universal Analytics properties won't continue to gather data after this period.
It's time to start using GA4 if you haven't already. You can gather more historical and user data in GA4 if you put up your property early.
It is designed to handle large amounts of data and is built on Google's infrastructure, which allows it to process large queries quickly, even on massive datasets. It offers a pay-as-you-go pricing model, which means you only pay for the queries you run and the amount of data you store.
Saving historical data is important because it allows you to track changes over time and make more informed decisions based on past performance.
Here are some specific reasons why saving historical data is important:
Overall, saving historical data is essential for understanding how your business has performed in the past, identifying areas for improvement, and making informed decisions about the future.
Data from Universal Analytics can be saved in a variety of methods and later compared to GA4 in reports.
Being well-known- We work in marketing. Spreadsheets are great.
Simple Exports - The majority of UA reports can be saved.Google Sheets or XLSX files.
Cost- Using your preferred programme will probably cost you nothing extra.
Cons:
Scale - Depending on the level of detail required by their data requirements, larger, more popular sites might require multiple sheets.
Versatility - While it is possible to email or share individual files, working with spreadsheets across bigger teams has drawbacks.
Manual Reload - You are forced to physically export your UA reports from the web UI each time you need to refresh the data unless you automate a download or export.
Cons:
More money must be spent on specialised data storage options.
For most Google customers, BigQuery is their preferred storage option. However, BigQuery has some benefits when integrating with other Google products like Google Ads, Data Studio, and Sheets. Of course, you can use Amazon or Microsoft cloud products as well.
Pros:
Once you've established the proper API connections, it's simple to consume big data sets.
With the Google Analytics Reporting API, you have more freedom to select the precise data sets you require for your upcoming reports.
The cost of cloud storage is reasonable.
BigQuery is a cloud-based data warehouse provided by Google Cloud Platform that allows users to store and analyse large volumes of data quickly and easily. It is a highly scalable and cost-effective solution for storing and processing data, and it can handle both structured and unstructured data.
There are several reasons why you might choose to use BigQuery to save historical data over other alternatives:
Overall, BigQuery can be a powerful tool for storing and analysing historical data, especially when combined with other Google Cloud Platform services like Google Analytics. Its scalability, speed, and cost-effectiveness make it a popular choice for businesses of all sizes.
When planning data transfer in BigQuery, there are several factors to consider, such as the volume and frequency of the data transfers, the data format, the data source and destination, and the performance requirements.
[Picture credits: Google]
Here are some steps you can follow to plan data transfer in BigQuery:
By following these steps, you can plan a data transfer process that meets your performance, security, and compliance requirements, while also optimising cost and efficiency.
To formulate queries in BigQuery, you can use SQL-like syntax to select, filter, aggregate, and manipulate data in your datasets. Here are some basic steps to follow when formulating queries in BigQuery:
By following these steps, you can formulate and execute queries in BigQuery to extract insights from your data and answer important business questions.
Below is an example of a simple query in BigQuery that calculates the total revenue for each product category in a hypothetical e-commerce dataset:
SELECT |
In this example, the query selects the "product_category" and calculates the total revenue for each category by multiplying the "price" and "quantity" fields together and summing the result. It then groups the results by "product_category" and orders them in descending order based on the total revenue. The query assumes that the e-commerce data is stored in a table named "my_table" in a dataset named "my_dataset" in the "my_project" project.
This query could be used by a business to understand which product categories are generating the most revenue, helping them to make decisions around inventory, marketing, and product development.
It is a highly scalable and cost-effective solution for storing and processing data, and it can handle both structured and unstructured data. With its support for SQL, integrations with other Google Cloud Platform services, and machine learning capabilities, BigQuery is a powerful and versatile solution for businesses of all sizes looking to compile and examine a lot of info..
By leveraging the benefits of BigQuery, businesses can gain valuable insights into their operations, identify areas for improvement, and make data-driven decisions that can help drive growth and success.
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