Tobiko Data Articles

1. Evolving Large Datasets with SQLMesh

   Learn about what SQLMesh has to offer when it comes to evolving really large datasets.

2. Traps and Pitfalls of Using SQL with Jinja

   The practice of augmenting SQL code with Jinja templates has gained significant traction and is consistently growing in popularity. However, is this truly the optimal approach? In this article, we examine the common challenges that arise when combining SQL and Jinja, while also suggesting a more effective method for metaprogramming in SQL.

3. SQLMesh for dbt Users - Part 2

   Are you using dbt and have heard of SQLMesh, but aren't quite sure what it is? This post will teach you the basics of SQLMesh from a dbt user's perspective to help get you up to speed. Part 2 of 2.

4. Introducing SQLMesh’s Open Source CI/CD Bot

   Introduces the SQLMesh's open source CI/CD bot and how it can be used to automate the deployment of data pipelines.

5. SQLMesh for dbt Users - Part 1

   Are you using dbt and have heard of SQLMesh, but aren't quite sure what it is? This post will teach you the basics of SQLMesh from a dbt user's perspective to help get you up to speed.

6. Data Load Patterns 101: Full Refresh and Incremental

   Explore the two fundamental methods of loading data (Full Refresh and Incremental). Learn about two common approaches for determining what data to load incrementally and how they compare.

7. We Need Even Greater Expectations When Testing Data

   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.

8. Simplicity or Efficiency: How dbt Makes You Choose

   Comparing development experience and runtime efficiency of dbt and SQLMesh.

9. Semantic Understanding of SQL

   Exploring what semantic understanding is and how it helps improve user experience when using SQL.

10. Correctly Loading Incremental Data at Scale

    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.

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