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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

By collecting data from store sensors using AWS IoT Core , ingesting it using AWS Lambda to Amazon Aurora Serverless , and transforming it using AWS Glue from a database to an Amazon Simple Storage Service (Amazon S3) data lake, retailers can gain deep insights into their inventory and customer behavior.

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

AWS Big Data

Amazon Athena supports the MERGE command on Apache Iceberg tables, which allows you to perform inserts, updates, and deletes in your data lake at scale using familiar SQL statements that are compliant with ACID (Atomic, Consistent, Isolated, Durable). The first task performs an initial copy of the full data into an S3 folder.

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

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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Build a data lake with Apache Flink on Amazon EMR

AWS Big Data

The Amazon EMR Flink CDC connector reads the binlog data and processes the data. Transformed data can be stored in Amazon S3. We use the AWS Glue Data Catalog to store the metadata such as table schema and table location. Verify all table metadata is stored in the AWS Glue Data Catalog.