Data Engineering
Extract
Transform & Load
Is your data collecting in your Ecommerce and advertising platforms? If so, do you know what to do next to join your data together, then model and pipe it into visualization software? Extracting your data from its source platform is the first, and sometimes most difficult, step in the ETL process.
Whether you need help implementing a third-party data extraction tool or are looking for an experienced team to set up a locally hosted open-source solution for extracting your data, our team has you covered. Our analysts are proficient with extraction tools like Supermetrics, Airbyte, and more – and can work with you to find a solution that suits your needs.
- Data Extraction
- Data Transformation & Modeling
- Data Storage & Loading
- End-To-End Data Pipelines
A Few of Our Tools
Extract
Is your data collecting in your Ecommerce and advertising platforms? If so, do you know what to do next to join your data together, then model and pipe it into visualization software? Extracting your data from its source platform is the first, and sometimes most difficult, step in the ETL process.
Whether you need help implementing a third-party data extraction tool or are looking for an experienced team to set up a locally hosted open-source solution for extracting your data, our team has you covered. Our analysts are proficient with extraction tools like Supermetrics, Airbyte, and more – and can work with you to find a solution that suits your needs.
Transform
With the introduction of Google Analytics 4, businesses of all sizes were suddenly thrust into the world of big data. A major roadblock for many of the companies looking to report on their raw GA4 data is the massive lift it takes to unnest and model it for visualization and reporting. And this roadblock isn't just unique to GA4.
Our data team can help you build out custom attribution channels for your acquisition data, join your raw Google Analytics product information with your Ecommerce platform sales data, or even clean up a messy exported data source. Whatever your needs, having a team at your back to accurately model your data is more important than ever.
Store & Load
As the age of big data creeps onto more businesses, it's critical that you have a strategy to effectively and efficiently store your data. Regulations like HIPAA affecting data storage practices for healthcare companies, and data privacy laws like the GDPR governing where and what data companies can store, make it even more important to have a data storage solution.Â
When you work with us, our data experts become an extension of your team. We can help with all your data needs, whether it’s finding a locally hosted HIPAA-compliant data storage solution, setting up an enterprise-level cloud storage environment, or piping data into visualization software. Our engineers are well-versed in a range of cloud-storage solutions, as well as locally hosted data storage architecture.
End-to-end
Data Pipelines
Getting all your data from point A to point B seems like a simple task, but is typically a lot more complicated than expected. The ETL process starts with extracting data from all your source platforms using either a third-party tool or local solution. That extracted data then needs to be put into data warehouses, where it can be cleaned and modeled before it's sent to cloud storage buckets for long-term storage. The final step involves piping your cleaned data into the visualization software of your choice.
Data engineering is anything but a simple endeavor - and for many, can seem like an impossible task. If you need assistance, our team is ready to set you on the path to accurate and efficient reporting through complete ETL pipelines.
Data Engineering
Solutions
Whether you need an individual project or a complete end-to-end data solution, we've got your back!
Request a proposal to get started.
Our team of experienced engineers is here to design, develop, and maintain your end-to-end data pipelines. No matter what stage you’re at in the ETL process, what data sources you’re working to connect, or what existing platforms your company needs to integrate with, our engineers are well-versed with a wide range of tools to ensure we can assist with any data needs!
DISCOVERY
Our engineers will start by meeting with your team to develop an understanding of your overall business goals and what you plan to accomplish with a complete data pipeline.
DATA EXTRACTION
Once we get to know you, we’ll begin with the first step in the ETL process: data extraction.
Whether you're using a third-party data extraction tool like Supermetrics, or need a more versatile in-house tool like Airbyte to extract your source data, our engineers will connect to and extract all your source platform data into your desired storage environment.
DATA TRANSFORMATION & MODELING
Once we extract your data out of the source platforms and into your data lake or warehouse, the next step is to model the data for streamlined reporting.
Data modeling can be done pre or post-storage, depending on your specific business needs.
If you want to cut down on storage costs, modeling specific views is an option. But if the value of having all your raw data outweighs the cost of storing it, exporting all raw data into storage buckets before modeling is a great option.
DATA STORAGE & LOADING
Making sure you choose a storage method that fits your business goals is critical to balancing cost with efficiency. The team at Cypress North will walk you through all your cloud and local storage options so you can make the most informed decision possible.
Our team is well-versed in the Google Cloud suite, Snowflake data warehouses, and more! We'll make sure whatever storage solution we land on meets your business needs, is sustainable for the long run, and is compatible with all your visualization and reporting tools.
Sick and tired of base Google Analytics 4? Can't stand the constant sampling, estimated and aggregated data, query limits, and lack of attribution models? Well, you're in luck because Google provides the option to export all your raw underlying GA4 data right into BigQuery completely for free!
Utilizing complex SQL queries against the raw data allows us to report on our true numbers without query limitations, join data from advertising and ecommerce platforms directly with GA4, build custom attribution models that fit your specific business needs, filter unwanted spam and regional traffic using custom filtering criteria, and much more.
The possibilities are endless with the GA4 BigQuery data, but the path to accurately and efficiently reporting on this information is near impossible without an experienced team at your side. Some of the roadblocks with the raw exported data include...
- Nested Data
- No pre-built metrics like "Sessions" (these must be modeled into the data)
- Multiple Acquisition Source Fields
- Item vs Purchase Level Ecommerce Data
- Incorrect Native CPC Attribution
Luckily, the team at Cypress North is experienced with these problems and is here to help you find a solution! Our data team has accurately and efficiently modeled exported GA4 BigQuery data for various clients, and connected this data to enterprise-level cloud storage environments and visualization software for the most accurate GA4 reporting possible.
DISCOVERY
We'll meet with your team to understand your goals for utilizing the raw GA4 data, and develop a plan to get you on your way to accurate and efficient GA4 reporting.
If you haven't set up the BigQuery export, we'll also walk you through how to start collecting data! If you need the data sent into an existing cloud storage environment before modeling, that's no problem either.
DATA MODELING
Once we understand your reporting needs, our team will begin building out your custom GA4 data model.
Whether you just need the basic GA4 metrics modeled out, are looking to build a custom attribution model to report on conversion data, or need your ecommerce or advertising platforms joined into the data, we'll make sure we model all the data you'll need for reporting and analysis.
STORE & LOAD
Need your data loaded into a cloud or local storage environment? Want some help loading data into visualization software and building out enterprise-level reports? Our team is here to assist with storing your GA4 data in a local or cloud storage bucket, and transitioning the modeled data into visualization software like Tableau or Power BI.
In 2023, Google replaced Universal Analytics (GA3) with Google Analytics 4. Google still stores your historically collected data within the interface... for now. But all historical data currently stored in Universal Analytics will be permanently deleted by July 2024.
Our team of expert analysts developed a Python script to extract all your historical data right through the Google Analytics Reporting API. We rigorously analyze the exported data, correcting any inconsistencies we come across to ensure your final export contains the most accurate data possible. The extracted data is stored in various CSVs and visualized in a custom dynamic Looker Studio dashboard!
WHAT'S INCLUDED
- Cleaned and validated historical Universal Analytics data, exported from the Google Analytics Reporting API.
- Multiple CSVs with your raw data, perfect for long-term storage.
- A dynamic multi-page Looker Studio report with views showcasing your key historical data.
Making sure your data storage architecture is balancing cost with efficiency is an ever-mounting problem for companies with the rise of big data. The team at Cypress North is well-versed in the Google Cloud suite, Snowflake data warehouses, and more! We'll make sure whatever storage solution we land on meets your business needs, is sustainable for the long run, and is compatible with all your visualization and reporting tools.
DISCOVERY
Our team will meet with you to understand your business goals and come up with a plan to store your data in a cost-effective and efficient environment.
IMPLEMENTATION
Whether you have an existing cloud storage environment that needs to be optimized and reconfigured for new data sources, are looking for a team to set up an ETL process around an existing storage system, or even if you need a complete data storage solution built from scratch, our team will find a custom solution that fits your business needs.
Need some more information?
Talk to Jack and find a custom solution that's right for you!
Jack Novorr
Data Engineering Resources
Data Lakes & Data Warehouses: What Are They? (& Why Your Company Probably Needs Both)
Data lakes and data warehouses have gained increased interest from organizations in recent years for their ability to support a single source of truth for data-driven decision-making across various departments. Understanding the strengths and applications of each is important not […]
How to Save Universal Analytics Data
All historical data from Google’s Universal Analytics will be deleted on July 1, 2024. Learn more about what your options are for backing it up before it’s gone for good.
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Exploring a New Dataset With Python Part II: Using Seaborn To Visualize Data
Welcome to Part II of Exploring a new dataset with Python! If you missed Part I: The Basics, you can check it out here. In this article, we’ll be returning to our animal mug company’s dataset to continue our exploratory […]
Python & Google Analytics: Prepare Data For Analysis [ Jupyter Notebook Template]
To utilize the power of Python to manipulate and analyze your Google Analytics data, the first step is to transform your data into a clean DataFrame that’s ready for analysis. In this article, I’m going to show you how to […]
Pandas Groupby Function: Python for Digital Marketing
If you’ve been following along with our Python for Digital Marketing posts, you’ve imported data from Google Analytics into a Jupyter Notebook and may have even combined it with another dataset. If you’ve never been here in your entire life […]
Strip Query Strings From URL Data: Python For Digital Marketing
If you’ve ever spent any time in a Google Analytics account, you’re all too familiar with the fact that the data isn’t always pretty. One exceptionally common scenario that us marketers run into all the time is page data being […]
Using Python To Combine Datasets For Digital Marketing
Working in digital marketing, there are several reasons why you might want to combine two datasets from two different sources together. It could be combining Google Analytics data from two different accounts or properties, or turning two different reports into […]
