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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif

SQL Query: 3 Methods for Calculating Cumulative SUM

SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences.

Top 3 Queries to Calculate Cumulative SUM


Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle)


SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum 

FROM your_table;

This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column.


Using Subqueries (e.g., MySQL, SQLite):


SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum

FROM your_table t1

JOIN your_table t2 ON t1.id >= t2.id

GROUP BY t1.id, t1.value

ORDER BY t1.id;


This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or equal to the id in the second table. It then calculates the sum of the value column for each group of rows with the same ID from the first table.


Using Correlated Subqueries (e.g., MySQL, SQLite):


SELECT id, value, (

    SELECT SUM(value) 

    FROM your_table t2 

    WHERE t2.id <= t1.id

) AS cumulative_sum

FROM your_table t1

ORDER BY id;


This query uses a correlated subquery to calculate the cumulative sum. For each row in the main query, it calculates the sum of the value column for all rows with an id less than or equal to the id of the current row.

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