Benchmarks · Analytics engineering and data-team survey
dbt Labs State of Analytics Engineering 2024: coverage and access
Research data-team priorities, governance and quality pain, analytics-engineering workflows, and buying triggers for transformation, catalog, testing, and data-product tools. This is unusually close to the target market: data practitioners shipping governed, reusable analytics and data products.
What it covers
Geography and granularity
Geography: Use the geography stated by the source; coverage can vary by dataset and release.
Granularity: Research data-team priorities, governance and quality pain, analytics-engineering workflows, and buying triggers for transformation, catalog, testing, and data-product tools.
Source type: Analytics engineering and data-team survey
Potential use
Research data-team priorities, governance and quality pain, analytics-engineering workflows, and buying triggers for transformation, catalog, testing, and data-product tools. This is unusually close to the target market: data practitioners shipping governed, reusable analytics and data products.
Access and cost
Public download
Public official report page describing the research; the page presents a report download, which may require a contact form.
Cost: Current price was not established in this review; check the provider's access terms.
Update cadence: Release cadence was not normalized in this review; check the source's dates, update notes, and API limits before comparing periods.
- Provider page ↗
- Documentation and access details ↗Check the provider's current terms and instructions before use
Interpret carefully
Limits that affect the decision
Publisher sponsorship and the dbt-centered analytics-engineering audience may bias tool/workflow representation; do not treat it as a census of all data teams.
Release cadence was not normalized in this review; check the source's dates, update notes, and API limits before comparing periods.
This record summarizes source documentation; Jon Bishop did not conduct an independent audit of the underlying dataset.