Building an Automated Data Pipeline: From GA4 to Amazon Redshift
In my current role as a Data Engineer, I realized that data is only as good as its availability. Moving data from Google Analytics (GA4) into a format that a business can actually use for strategy is a common challenge.
Here is how I solved this using an AWS-native architecture.
The Architecture
The goal was to create a "Single Source of Truth." I designed a pipeline that moves data from the edge into a centralized warehouse.
Step 1: Extraction with Python
I use Python scripts to interact with the GA4 API. This allows us to pull specific dimensions and metrics that are relevant to our business KPIs.
Step 2: The Landing Zone (Amazon S3)
Raw data shouldn’t go straight into a database. I load …
Building an Automated Data Pipeline: From GA4 to Amazon Redshift
In my current role as a Data Engineer, I realized that data is only as good as its availability. Moving data from Google Analytics (GA4) into a format that a business can actually use for strategy is a common challenge.
Here is how I solved this using an AWS-native architecture.
The Architecture
The goal was to create a "Single Source of Truth." I designed a pipeline that moves data from the edge into a centralized warehouse.
Step 1: Extraction with Python
I use Python scripts to interact with the GA4 API. This allows us to pull specific dimensions and metrics that are relevant to our business KPIs.
Step 2: The Landing Zone (Amazon S3)
Raw data shouldn’t go straight into a database. I load the raw JSON/CSV files into Amazon S3 first.
- Why S3? It acts as a durable, low-cost "Data Lake." If something goes wrong in the later stages, we always have our raw data safely stored in S3.
Step 3: The Warehouse (Amazon Redshift)
From S3, I use the COPY command to ingest data into Amazon Redshift.
- Optimization: I focus on ETL optimization to ensure data accuracy. This process has helped us achieve 98% data accuracy while reducing errors by 35%.
Business Impact
By leveraging AWS, we transformed our reporting process:
- Manual reporting time was cut by 50%.
- Data availability increased by 40%.
- Executives now access real-time insights through Apache Superset dashboards.
Conclusion
Moving from physical networking into cloud data engineering has taught me that automation is the key to scalability. If you are just starting with AWS, mastering S3 and Redshift is a fantastic way to understand how the cloud handles massive amounts of information.