If Delta Lake Uses Immutable Files, How Do UPDATE, DELETE, and MERGE Work?

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Listen and Watch here One of the most common questions data engineers ask is: if Delta Lake stores data in immutable Parquet files, how can it support operations like UPDATE , DELETE , and MERGE ? The answer lies in Delta Lake’s transaction log and its clever file rewrite mechanism. 🔍 Immutable Files in Delta Lake Delta Lake stores data in Parquet files, which are immutable by design. This immutability ensures consistency and prevents accidental corruption. But immutability doesn’t mean data can’t change — it means changes are handled by creating new versions of files rather than editing them in place. ⚡ How UPDATE Works When you run an UPDATE statement, Delta Lake: Identifies the files containing rows that match the update condition. Reads those files and applies the update logic. Writes out new Parquet files with the updated rows. Marks the old files as removed in the transaction log. UPDATE people SET age = age + 1 WHERE country = 'India'; Result: ...
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                                             Data Modelling - Star vs Snowflake Schema!!


Today, we'll dive into data modeling concepts, specifically focusing on star and snowflake schemas. 

 In a star schema, we have a central fact table surrounded by dimension tables. The fact table contains quantitative data, usually numerical metrics or measures, while the dimension tables contain descriptive attributes that provide context to the measures. The fact table is connected to the dimension tables through foreign key relationships, forming a star-like shape.

In a snowflake schema, the dimension tables are normalized, meaning that they are further broken down into multiple related tables. This results in a more complex network of relationships, resembling the branches of a snowflake. While this normalization can save storage space and reduce data redundancy, it can also lead to increased query complexity due to the need for additional joins.

Q In what scenarios would you prefer using a snowflake schema over a star schema, and vice versa?"

"Choosing between a star and snowflake schema depends on various factors such as the nature of the data, query patterns, and performance requirements. A star schema is simpler and easier to understand, making it suitable for scenarios where performance and simplicity are prioritized. On the other hand, a snowflake schema may be preferred in scenarios where data integrity and storage optimization are critical, and the additional complexity introduced by normalization is acceptable."

Now, let's consider a hypothetical scenario where you're tasked with designing a data warehouse for an e-commerce company. Would you opt for a star or snowflake schema, and why?"

"In the case of an e-commerce company, where performance and ease of querying are paramount, I would lean towards a star schema. The simplicity and denormalization of the star schema would facilitate efficient querying of sales data and analytics. However, I would consider normalizing certain dimension tables in a snowflake-like fashion if there are large, frequently updated attributes that could benefit from reduced redundancy and improved data integrity."

Hope it helps!


#datamodel #starschema #snowflake #datawarehouse

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