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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Solution overview Typically, you have multiple accounts to manage and provision resources for your data pipeline. Every time the business requirement changes (such as adding data sources or changing data transformation logic), you make changes on the AWS Glue app stack and re-provision the stack to reflect your changes.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Financial efficiency: One of the key benefits of big data in supply chain and logistics management is the reduction of unnecessary costs. Using the right dashboard and data visualizations, it’s possible to hone in on any trends or patterns that uncover inefficiencies within your processes.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format.