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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). However, joint optimization is possible by increasing both $x_1$ and $x_2$ at the same time.

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Understanding The Value Of Column Charts With Examples & Templates 

datapine

2) Pros & Cons Of Column Charts 3) When To Use A Column Graph 4) Types Of Column Charts 5) Column Graphs & Charts Best Practices 6) Column Chart Examples Data visualization has been a part of our lives for many many years now. They are easy to understand: Column graphs are one of the easiest visualizations to understand.

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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset. Dataset details The test dataset contains 104 columns and 1 million rows stored in Parquet format. On the AWS Glue console, under ETL jobs in the navigation pane, choose Visual ETL.

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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

Additionally, it enables cost optimization by aligning resources with specific use cases, making sure that expenses are well controlled. The policies attached to the Amazon MWAA role have full access and must only be used for testing purposes in a secure test environment.

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Best practices to implement near-real-time analytics using Amazon Redshift Streaming Ingestion with Amazon MSK

AWS Big Data

Establish connectivity between an Amazon QuickSight dashboard and Amazon Redshift to deliver visualization and insights. For optimized performance of the streaming materialized view and to reduce storage usage, occasionally purge data from the materialized view using delete , truncate , or alter table append.

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Run Spark SQL on Amazon Athena Spark

AWS Big Data

Running SQL on data lakes is fast, and Athena provides an optimized, Trino- and Presto-compatible API that includes a powerful optimizer. We cover some common and advanced SQL commands used in Spark SQL, and show you how to use Python to extend your functionality with user-defined functions (UDFs) as well as to visualize queried data.

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Use Snowflake with Amazon MWAA to orchestrate data pipelines

AWS Big Data

Customers rely on data from different sources such as mobile applications, clickstream events from websites, historical data, and more to deduce meaningful patterns to optimize their products, services, and processes. If you’re testing on a different Amazon MWAA version, update the requirements file accordingly.