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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

Accurately predicting demand for products allows businesses to optimize inventory levels, minimize stockouts, and reduce holding costs. Solution overview In today’s highly competitive business landscape, it’s essential for retailers to optimize their inventory management processes to maximize profitability and improve customer satisfaction.

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Orchestrate Amazon EMR Serverless jobs with AWS Step functions

AWS Big Data

With EMR Serverless, you don’t have to configure, optimize, secure, or operate clusters to run applications with these frameworks. You can run analytics workloads at any scale with automatic scaling that resizes resources in seconds to meet changing data volumes and processing requirements.

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Amazon EMR on EKS widens the performance gap: Run Apache Spark workloads 5.37 times faster and at 4.3 times lower cost

AWS Big Data

Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). This performance-optimized runtime offered by Amazon EMR makes your Spark jobs run fast and cost-effectively. As of the Amazon EMR 6.5 Amazon EMR 6.10

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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering. Orca addressed this in several ways.

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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. This blog post is co-written with James Sun from Snowflake.