Google Analytics to Redshift: 2 Easy Methods

Many businesses worldwide use Google Analytics to collect valuable data on website traffic, signups, purchases, customer behavior, and more. Given the humongous amount of data that is present on Google Analytics, the need to deeply analyze it has also become acute. Naturally, organizations are turning towards Amazon Redshift, one of the widely adopted Data Warehouses of today, to host this data and power the analysis. In this post, you will learn how to move data from Google Analytics to Redshift.

Overview of Google Analytics

Google Analytics is an incredibly powerful Web Analytics service that reports and tracks website traffic and user behavior. It provides relevant insights on how users are interacting with your site, thus helping you optimize marketing strategies for a better user experience.

Use Cases

• Behavior Tracking: It can track users’ activities on your website, which involve page views, session duration, and bounce rates, in order to get a sense of the level of user engagement.
• Conversion Tracking: It is possible to measure and analyze conversions, goals, and views of things like form submissions or product purchases to evaluate marketing effectiveness.

Overview of Redshift

Amazon Redshift is a fully managed cloud data warehouse service from AWS that is designed for large-scale data storage and analysis. It enables fast querying and analysis of vast amounts of data using a columnar storage architecture and parallel processing capabilities.

Use Cases

• Big Data Analytics: It can run complex queries and complex analyses over large datasets, guaranteeing very high performance.
• Data Integration: You can combine data from various sources, such as transactional databases, log files, and external data sources, for comprehensive analysis.

Advantages of Migrating GA Data to Redshift

Here, I have highlighted some of the major advantages of migrating your data from Google Analytics to Redshift.

• High performance: Utilizing column-store and parallel processing within Redshift can provide higher performance for queries over large volumes of data from GA.
• Advanced Analytics: You can run advanced, custom queries and analyses—well beyond the native capabilities of GA—with the power of SQL and a library of analytical functions.
• Scalability: You can easily scale storage and compute resources to handle growing data volumes and increasing analytical demands.

Methods to Connect Google Analytics to Redshift

Here are the methods you can use to connect Google Analytics to Redshift in a seamless fashion:

Method 1: Using Hand Coding to Connect Google Analytics to Redshift

Pre-Migration Steps

• Audit of Source Data: Before data migration begins, Google Analytics event samples should be reviewed to ensure that the engineering team is completely aware of the schema. Business teams should coordinate with engineering to clearly define the data that needs to be made available. This will reduce the possibility of errors due to expectation mismatch between business and engineering teams
• Backup of all Data: In the case of a failed replication, it is necessary to ensure that all your GA data may be retrieved with zero (or minimal) data loss. Also, plans should be made to ensure that sensitive data is protected at all stages of the migration.

Manual Migration Steps

• Step 1: Google Analytics provides an API, the Google Core Reporting API, that allows engineers to pull data. As such, most of the data that is returned is combined into a consolidated JSON format, which is incompatible with Redshift. 
• Step 2: The scripts would need to pull data from GA to a separate object, such as a CSV file. Meanwhile, to prepare the Redshift data warehouse, SQL commands must be run to create the necessary tables that define the database structure. The aforementioned CSV file must then be loaded to a resource that Redshift can access.
• Step 3: Amazon S3 cloud storage service is a good option. There is some amount of preparation involved in configuring S3 for this purpose. The CSV file must then be loaded into the S3 that you configured. The COPY command must be invoked to load the data from the CSV file and into the Redshift database. 
• Step 4: Once the transfer is complete queries should be run on the newly populated database to test if the data is accurate and complete. This would re-ensure that the data load was successful. Having been verified, a cron job should be set up to run with reasonable frequency, ensuring that the Redshift database stays up to date. Say you have different Google Analytics views set up for Website, App, etc. You would have to end up repeating the above process for each of these.

This concludes this method of manually coding the migration from Google Analytics to Redshift.

Limitations of using Hand Coding to Connect Google Analytics to Redshift

I have listed down some limitations of manually migrating Google Analytics data to Redshift.

• Manual coding for data replication is time-consuming and expensive.
• It raises concerns about the quality of the code and the number of iterations required to get it right.
• There can be difficulty in identifying changes in the Google Analytics Reporting API JSON format.
• There is a potential for missed deadlines and budget overruns due to multiple tests and script rewrites.
• As data volumes grow, manually coded solutions may struggle to scale efficiently, potentially leading to performance bottlenecks and increased complexity in managing the data pipeline.

Method 2: Using Hevo Data to Connect Google Analytics to Redshift

Using Hevo Data Integration Platform, you can seamlessly replicate data from Google Analytics to Redshift with 2 simple steps:

• Step 1: Connect Hevo to Google Analytics to set it up as your source by filling in the Pipeline Name, Account Name, Property Name, View Name, Metrics, Dimensions, and the Historical Import Duration.

• Step 2: Load data from Google Analytics to Redshift by providing your Redshift databases credentials like Database Port, Username, Password, Name, Schema, and Cluster Identifier along with the Destination Name.

Hevo takes up all the grind work ensuring that consistent and reliable data is available for Google Analytics to Redshift setup.

What Can You Achieve By Replicating Data from Google Analytics to Redshift?

• Analyze Demographics: Determine which demographic groups contribute the most to the usage of specific product features.
• Assess Marketing Impact: Evaluate how paid sessions and goal conversion rates fluctuate in relation to marketing spend and cash inflow.
• Identify Valuable Segments: Pinpoint and analyze your most valuable customer segments for targeted marketing and strategy.

Conclusion

This blog talks about the two methods you can use to connect Google Analytics to Redshift in a seamless fashion. Data and insights are the keys to success in business, and good insights can only come from correct, accurate, and relevant data. Hevo, a 100% fault-tolerant, easy-to-use Data Pipeline Platform ensures that your valuable data is moved from Google Analytics to Redshift with care and precision. Try a 14-day free trial to explore all features, and check out our unbeatable pricing for the best plan for your needs.

FAQs about Google Analytics Redshift Integration