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Cloudyard is being designed to help the people in exploring the advantages of Snowflake which is gaining momentum as a top cloud data warehousing solution.

Site provides professionals, with comprehensive and timely updated information in an efficient and technical fashion. From basic to Advanced concepts along with the realtime scenario,Weaves the SnowSql, jq parser,Snowflake-Python connector,propositional logic all at one place in order to unveil nuances of realtime flavour and experiences with Snowflake Cloud. Read More

Latest Post

Splitting Large CSV Files in Snowflake Using Snowpark

In data engineering, we often encounter large files that need to be processed in chunks. Using Snowflake’s Snowpark, you can split a large CSV file into smaller parts and handle each as needed. However, while Snowpark provides powerful in-database processing capabilities, splitting files this way may not be the most […]

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Data Security with Snowflake: Row Access, Masking, and Projection Policies

In a financial institution, sensitive information such as Customer Numbers, transaction details, and customer balances are often needed for internal analysis and reporting. However, due to compliance regulations, access to these fields needs to be restricted based on the user’s role. To solve this, we’ll apply Projection Policies to ensure […]

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Tracking Schema Changes in Iceberg Tables Using Metadata Files

Introduction Managing schema changes is a critical aspect of maintaining data integrity and consistency in dynamic data environments. When using Iceberg tables, every Data Definition Language (DDL) operation triggers the generation of a new metadata JSON file that captures the updated structure. Tracking these schema modifications, such as the addition, […]

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Tracking DDL Changes in Snowflake: A Real-World Solution

Recently my colleague Aman and I were discussing a challenge that we were facing in our project: How can we track DDL (Data Definition Language) changes made to tables in Snowflake?. We needed a way to monitor schema modifications, not just to capture what changed but also when those changes […]

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Unlocking the Power of Snowpark DataFrames with Modin

Pandas, a popular Python library, is a fantastic tool for small to moderately sized data, but it struggles with large-scale datasets. Snowpark, combined with Modin, offers a powerful alternative by enabling scalable, distributed operations directly in Snowflake’s cloud infrastructure. This blog will explore the key differences between Pandas DataFrames and […]

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Masking Sensitive Data in External Tables

In this post, we’ll explore the application of masking policies on external tables in Snowflake. While masking policies are commonly implemented on permanent tables, there are scenarios where sensitive data may reside in external sources, such as files in S3, which do not need to be fully ingested into Snowflake. […]

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