Having an automated PR review for DBTs will be very valuable to ensure DBTs and SQL are following the best practices.
I feel like SQL and data is always a second class citizen compared to code review bots / the vast ecosystem of linters and review tools. As a result, it's great to see that DBT and SQL models will benefit here as well!
Does the Github app integrate with my existing DBT schemas and provide customized recommendations depending on my data models, metrics, etc?
It's very cool that folks can create the blocks from natural language but also inspect the generated code and modify it if desired. Are there any future plans to integrate blocks with existing data sources?
Thank you! Regarding performance, TPU v5e which was benchmarked in the MLPerf results and showed impressive performance/dollar, see https://cloud.google.com/blog/products/compute/performance-p... which has more details. Combined with k8s & GKE, TPU v5e workloads can leverage auto-scaling, for example setting up autoscaling based on traffic, so workloads can scale down rapidly when not in use, increasing cost efficiency.
I feel like SQL and data is always a second class citizen compared to code review bots / the vast ecosystem of linters and review tools. As a result, it's great to see that DBT and SQL models will benefit here as well!
Does the Github app integrate with my existing DBT schemas and provide customized recommendations depending on my data models, metrics, etc?