Articles written by the team, about the questions we run into on engagements: data, AI, cloud. Without jargon whenever possible.
CI/CD for data pipelines, Data Quality as Code, observability: the three pillars of DataOps, the tools (dbt, Great Expectations, Soda, Monte Carlo, OpenTelemetry) and Logbrain's recommended practices.
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Data Mesh: the 4 principles, the 3 pitfalls that sink projects, and the success factors observed in banking, industry and retail. Lessons from Logbrain's field experience.
A project, a question, a doubt about an architecture: email us or book a slot. The first conversation is for understanding, not selling.
