We partnered with Databricks to benchmark SQLMesh against dbt Core™ on key transformation tasks — and our tests showed that SQLMesh outperforms dbt by 9x in both speed of execution and compute cost. Read the full study to see why SQLMesh (and our hosted solution, Tobiko Cloud) are the efficient, future-proof, and smarter alternatives to dbt for data engineering.
We break down dbt’s acquisition of SDF, and how syntax checks and faster compile fall short of addressing gaps in dbt’s architecture — especially as these features are not open-source. Read about how this contrasts with Tobiko’s state management in SQLMesh and our ethos of growing with our open-source community.
dbt's new microbatch incremental models in version 1.9 are a step forward but come with limitations that require advanced handling. They don't have state or history, leading to data gaps and incomplete results.
Migrating from one data warehouse to another is challenging and full of manual work. Learn how SQLMesh can help your company automate this process.
Learn about how SQLMesh saves money with cron and partitions.
Step by step guide for column level lineage within your dbt project.
How can you tell if two SQL queries are the same? Can you do better than just checking if two statements are textually equivalent?
How we're trying to build a trustworthy and thriving open source community.
Although companies heavily rely on SQL / data pipelines to power applications and make decisions, testing is usually an afterthought. However, systematic testing of data is critical to creating trustworthy and reliable pipelines. This post highlights the three categories of tests for data: unit tests, table diffs, and audits.
Writing efficient and correct incremental pipelines is challenging. While many folks do take on the challenge of writing incremental models, it is viewed as an advanced use case which could discourage teams from adopting incremental loads.