In modern data workflows, schema changes are inevitable — new fields, revised tables, and ever-evolving business logic. In this post, I’m sharing a macro-driven strategy that showcases the synergy between DBT and Snowflake, where schema evolution is not a blocker — it becomes a feature. This approach removes the need […]
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Snowflake and DBT (Data Build Tool) are two of the most powerful players in the modern data stack. Traditionally, DBT is known for transformations and Snowflake for its cloud-native warehousing. When combined, DBT handles your transformations and Snowflake provides the storage and compute power. This combination streamlines ETL processes, increases […]
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In today’s cloud-first landscape, the integrity of data pipelines is crucial for operational success, regulatory compliance, and business decision-making.This blog, “Snowflake Data Quality Framework: Validate, Monitor, and Trust Your Data,” will walk you through a Snowflake-native, dynamic, and extensible Data Quality (DQ) Framework — capable of automatically validating data pipelines, […]
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Introduction In modern data pipelines, especially in cloud data platforms like Snowflake, data ingestion from external systems such as AWS S3 is common. However, one critical question that often arises is: How do we ensure the data we receive from the source matches the data we ingest into Snowflake tables? […]
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Data Masking in Snowflake Using Tags, Policies, and Automation: In modern data platforms, data masking and access control are critical pillars of security and compliance — especially with sensitive fields like SSNs, email addresses, and financial metrics. In this blog, we explore how to implement a tag-driven masking and row-level […]
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Snowpark Magic: Auto-Create Tables from S3 Folders — In modern data lakes, it’s common for departments like Finance, Marketing, Sales, etc., to continuously drop data files into their respective folders within an S3 bucket. These files often arrive in CSV format, and over time, teams request new folders or refresh their […]
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