True open source
d8a.tech: You get the ownership of a home-made pipeline: MIT code you can read, fork and run anywhere.
a DIY pipeline: You own it, and you also write, test and maintain every line.
Resources / Comparisons
d8a.tech vs Building Your Own Pipeline
Most data teams start by stitching together the GA4 BigQuery export, Dataform or dbt models and home-made sessionization. d8a.tech gives you the same ownership, with sessionized, documented tables out of the box and no pipeline to maintain.
Which one should you choose?
Build your own if analytics collection is your product and you want to staff it. Choose d8a.tech if you want the ownership of a home-made pipeline (open code, your warehouse, your infrastructure) without writing and maintaining sessionization, attribution and schema handling yourself.
Jump to the feature tableWhat sets d8a.tech apart from a DIY pipeline
No limits
d8a.tech: No daily export cap and no retention window: data lands in your warehouse as it arrives.
a DIY pipeline: Built on the GA4 export: 1M events/day on standard properties and a daily batch.
Modern infrastructure
d8a.tech: One Go pipeline: receive, sessionize, write.
a DIY pipeline: GA4 export, BigQuery, Dataform or dbt models and an orchestrator to keep in sync.
No reimplementation
d8a.tech: Keep your tags and skip writing sessionization and attribution.
a DIY pipeline: Sessions, channel attribution and click-ID parsing are yours to build.
Where a DIY pipeline fits
A home-made pipeline usually starts with the GA4 BigQuery export, then Dataform or dbt models to unnest event parameters, rebuild sessions and attribute traffic, plus an orchestrator and monitoring. It gives full control, at the cost of a pipeline your team now owns forever.
Where d8a.tech is different
d8a.tech is that pipeline, already built and open source. It receives your existing GA4 or Matomo requests, computes sessions and attribution in the pipeline, and writes flat, documented tables to your BigQuery, ClickHouse or Parquet storage.
d8a.tech vs a DIY pipeline, feature by feature
Tick Discuss next to anything you want to cover on a call. We'll start the conversation there.
Feature
d8a.tech
Warehouse-native analytics
a DIY pipeline
GA4 export + Dataform or dbt + your own code
Discuss
At A Glance
d8a.tech Out of the box
a DIY pipeline After you build them
d8a.tech On session close
a DIY pipeline Daily batch, or streaming export at extra cost
d8a.tech d8a.tech and the community
a DIY pipeline Your team
d8a.tech MIT, fork any time
a DIY pipeline Yours
Collection
d8a.tech Yes
a DIY pipeline Via the GA4 export
d8a.tech Yes
a DIY pipeline A second pipeline to build
d8a.tech When requests are redirected
a DIY pipeline Not with the GA4 export
d8a.tech None
a DIY pipeline 1M events/day (standard GA4)
Modeling
d8a.tech Yes
a DIY pipeline Write and test it in Dataform or dbt
d8a.tech Computed in the pipeline
a DIY pipeline Parse and maintain yourself
d8a.tech Yes
a DIY pipeline UNNEST in every model
d8a.tech Yes
a DIY pipeline Whatever you document
d8a.tech Yes
a DIY pipeline Build it yourself
Operations
d8a.tech One Go binary, or receiver + worker
a DIY pipeline Export, warehouse jobs, Dataform or dbt, orchestrator
d8a.tech Yes
a DIY pipeline Not with the GA4 export
d8a.tech BigQuery, ClickHouse, CSV/Parquet
a DIY pipeline BigQuery (GA4 export)
d8a.tech Available
a DIY pipeline Your on-call rotation
AI And Analytics
d8a.tech Cloud
a DIY pipeline Build in your BI tool
d8a.tech Yes
a DIY pipeline Build it yourself
d8a.tech Ask AI
a DIY pipeline No
Sources for a DIY pipeline details:
Questions about d8a.tech vs a DIY pipeline
Isn't building our own pipeline cheaper?
The GA4 export itself can be cheap. The cost is the engineering time to unnest parameters, rebuild sessions, attribute traffic and keep it all working as GA4 changes. d8a.tech is open source, so you can self-host it for free and still skip that work.
Do we lose control compared with a home-made pipeline?
No. The code is MIT-licensed, the data lands in your own warehouse, and you can run it in your own infrastructure. You can read, fork and extend it like your own code.
Can we keep our existing Dataform or dbt models?
Yes. d8a.tech writes plain tables to your warehouse, so Dataform or dbt models can build on them. Most teams can drop the models that only existed to unnest parameters and rebuild sessions.
When should we still build our own?
When collection itself is a core product capability you want to own end to end, and you have the team to maintain it long term.
Comparing us with a DIY pipeline?
Tell us what matters to you and we'll walk you through it on your own stack. Or start with free Cloud and see for yourself.