![]() □ Interlude music plays as you install Docker □ Since this tutorial is for Mac, follow the Docker installation instructions for Mac. To keep this tutorial manageable, I am going to completely “hand wave” the Docker setup. Yes, if you’re keeping score at home, that means we are using one open-source framework packaged in another open-source framework packaged in yet another open-source framework. The best thing about Airbyte for our Minimum Viable Data Stack is that they make running the open-source code so easy because it is packaged in yet another software framework called Docker. Much of it is based on the open-source work of another ETL tool called Stitch had been pushing before they got acquired by Talend. I chose Airbyte for this demo because it is open source which means it’s free to use as long as you have a computer or a server to run it on. Then we’ll run a couple of analytical queries to whet your appetite! Setting up an ETL Tool (Airbyte) Put simply, ETL just means, “connecting to a data source, structuring the data in a way that it can be stored in database tables, and loading it into those tables.” There’s a lot more to it if you really want to get into it, but for our purposes, this is all you’ll need to know right now.įor this part of the tutorial, we are going to use an open-source ETL tool called Airbyte to connect to Hubspot and load some Contact data into the same Postgres database we set up before. This process is called ETL, short for Extract, Transform, Load. This post will demonstrate how to connect directly to a data source so that you can automatically load data as it becomes available. ![]() But that pattern is slow and requires you to continually upload new data as new data becomes available. That’s a good start! You could follow that pattern to do some interesting analysis on CSV files that wouldn’t fit in Excel or Google Sheets. We set up a Postgres database instance on a Mac personal computer, uploaded a CSV file, and wrote a query to analyze the data in the CSV file. In the first post in the Minimum Viable Data Stack series, we set up a process to start using SQL to analyze CSV data. You can even send custom dimensions from Google Sheets.ĪugSetting up Airbyte ETL: Minimum Viable Data Stack Part II I recently presented on how to get consistent metrics across Google Analytics, your ads platforms, and Hubspot called Marketing as a Data Product: Operational Analytics for Growth which shows how I did this. It feels a lot better to me than living and dying by button clicks. Sure the signal is imperfect (all models are) but it’s a lot stronger than the far-less-frequent conversion events. With reverse ETL data integration for Google Analytics, We can map these thresholds against custom dimensions to measure the volume of high-quality traffic a given channel is bringing in and evaluate channels/costs against traffic quality. With Clearbit Reveal and account-based scoring, we can put score-based thresholds on the traffic coming in (for example high/mid/low score). Firing a conversion based on an event gives us a really shallow view of success. But as you know, it was built for ecommerce-not for B2B SaaS. GA is the platform we trust for web analytics and a quick way to understand attribution. This is finally where Google Analytics comes in. So to advance our experimentation towards the top of the funnel, we need valid signals early in the journey. Growth is a process of expanding what works by experimenting and iterating. The problem is that those metrics are only helpful for understanding the funnel after a trial is started. Lead scores based on demographics, activity, content consumption, or product usage have been really helpful for us to aggregate signals into a few metrics that show how similar new leads are to past customers. It should come as no surprise that it’s easy for us to lose the connection between early marketing efforts and later sales outcomes. It’s a freemium product in the middle of multiple stakeholders and touchpoints. Here’s what I’m thinking.Ĭensus, as you’d imagine, has a long sales cycle. I could write a long list of game-changing applications for creating custom dimensions in Google Analytics from data in a Snoflake data warehouse but I’ve been swirling around one that I find particularly interesting. Last week, Census released our Google Analytics integration. Like many data geeks, Google Analytics was the thing that first sparked my curiosity. ApWriting Custom Dimension to Google Analytics from Snowflake DB
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