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

datapine

In addition to driving operational efficiency and consistently meeting fulfillment targets, logistics providers use big data applications to provide real-time updates as well as a host of flexible pick-up, drop-off, or ordering options. Use our 14-days free trial today & transform your supply chain!

Big Data 275
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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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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

The system ingests data from various sources such as cloud resources, cloud activity logs, and API access logs, and processes billions of messages, resulting in terabytes of data daily. This data is sent to Apache Kafka, which is hosted on Amazon Managed Streaming for Apache Kafka (Amazon MSK).

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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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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform.

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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.

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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. the Flink table API/SQL can integrate with the AWS Glue Data Catalog.