Data Platform

How a well-architected data platform enables better business decisions across product, finance, and operations — with practical examples of self-service analytics and data democratization at Klivvr.

A deep dive into dbt's incremental materialization strategy, covering when to use incremental models, how to implement them correctly, and how to avoid the common pitfalls that lead to data inconsistencies.

A comprehensive guide to implementing data quality testing across the data pipeline, from schema validation and freshness checks to statistical anomaly detection and business rule enforcement.

Core principles for designing directed acyclic graph (DAG) pipelines that are maintainable, observable, and resilient, with practical examples from production data engineering systems.

A detailed technical comparison of Dataform and dbt for data transformation, covering architecture, workflow, ecosystem, and practical considerations to help teams choose the right tool.

A practical guide to structuring dbt models for maintainability and reliability, covering staging layers, intermediate models, mart design, and testing strategies used in production data platforms.

A technical guide to building real-time analytics pipelines using change data capture, stream processing, and materialized views — with patterns from Klivvr's Data Platform.

A practical comparison of ETL and ELT data integration patterns, covering when to use each approach, transformation strategies, and how Klivvr's Data Platform leverages ELT for scalable data processing.

How fintech companies build effective data governance programs covering data ownership, cataloging, lineage tracking, and regulatory compliance — with practical lessons from Klivvr's Data Platform.

An exploration of modern data warehouse architecture patterns including the medallion architecture, dimensional modeling, and how Klivvr's Data Platform structures its warehouse for scalability and reliability.