Exploring Semi-Structured Data in Snowflake | Streamlining Discovery and Schema Evolution

In the digital era, data is the new gold, but not all of it lies in neatly organized vaults. A significant portion resides in complex, nested semi-structured data, presenting a formidable challenge to businesses aiming to harness its power through platforms like Snowflake.

This data, while rich in information, lacks a rigid, predefined schema, making its discovery, organization, and integration into data analytics platforms a daunting task. This creates several challenges, such as understanding what semi-structured data - whether that be Avro, Parquet, JSON, ORC - reside in your data lake, through to creating tables to store this data.

These are tedious, time-consuming manual activities, which come with a high risk of introducing errors. Once you understand your data and have built the schemas to store it, there is then the constant battle of schema drift to deal with, where the data's structure can change without notice, leading to data pipelines breaking, delays and lost opportunities.

In this video we break the problem down into 3 individual challenges and look at the specific Snowflake capabilities which help us solve each challenge. Each solution is a piece of the puzzle which will ultimately come together at the end to deliver an elegant and effective approach to the collection of challenges.

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Видео Exploring Semi-Structured Data in Snowflake | Streamlining Discovery and Schema Evolution канала Mastering Snowflake