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 Cleaning in SQL

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1. Import Data: First, import the Excel data into a SQL database table using a tool like SQL Server Management Studio. 

2. Identify Missing Values: Use SQL queries to identify any missing or null values in the dataset. This helps in understanding the extent of missing data and planning for imputation or removal.

3. Remove Duplicates: Utilize SQL's 'DISTINCT' keyword or 'GROUP BY' clause to identify and remove duplicate rows from the dataset. This ensures that each observation is unique.

4. Standardize Data Formats: Use SQL functions like UPPER, LOWER, TRIM, etc., to standardize text formats and remove leading or trailing spaces. This ensures consistency in the data.

5. Correct Data Types: Convert data types of columns as needed using SQL's CAST or CONVERT functions. For example, convert string representations of numbers to actual numeric types.

6. Handle Outliers: Identify and handle outliers using SQL queries. This might involve filtering out extreme values or applying statistical techniques for outlier detection.

7. Normalize Data: Normalize the data if necessary to reduce redundancy and improve data integrity. This might involve splitting data into separate tables and establishing relationships between them. 

8. Validate Constraints: Validate data against defined constraints such as foreign key constraints, unique constraints, etc., to ensure data integrity and consistency.

9. Impute Missing Values: If appropriate, impute missing values using techniques like mean imputation, median imputation, or predictive modeling. 

10. Review and Validate: Finally, review the cleaned dataset to ensure that it meets the quality standards and is ready for analysis. Validate the results against the original Excel file to ensure accuracy.

Hope it helps!

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