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

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications.

Big Data 275
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Stream data to Amazon S3 for real-time analytics using the Oracle GoldenGate S3 handler

AWS Big Data

Oracle GoldenGate for Oracle Database and Big Data adapters Oracle GoldenGate is a real-time data integration and replication tool used for disaster recovery, data migrations, high availability. Configure GoldenGate for Oracle Database and extract data from the Oracle database to trail files.

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Unlock scalable analytics with a secure connectivity pattern in AWS Glue to read from or write to Snowflake

AWS Big Data

This involves creating VPC endpoints in both the AWS and Snowflake VPCs, making sure data transfer remains within the AWS network. Use Amazon Route 53 to create a private hosted zone that resolves the Snowflake endpoint within your VPC. Refer to Editing AWS Glue managed data transform nodes for more information.

Analytics 100
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Use AWS Glue to streamline SFTP data processing

AWS Big Data

Access to an SFTP server with permissions to upload and download data. If the SFTP server is hosted on Amazon Elastic Compute Cloud (Amazon EC2) , we recommend that the network communication between the SFTP server and the AWS Glue job happens within the virtual private cloud (VPC) as pictured in the preceding architecture diagram.

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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

To run HiveQL-based data workloads with Spark on Kubernetes mode, engineers must embed their SQL queries into programmatic code such as PySpark, which requires additional effort to manually change code. host') export PASSWORD=$(aws secretsmanager get-secret-value --secret-id $secret_name --query SecretString --output text | jq -r '.password')

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues. The groundwork of training data in an AI model is comparable to piloting an airplane. ELT tools such as IBM® DataStage® facilitate fast and secure transformations through parallel processing engines.

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Amazon Redshift data ingestion options

AWS Big Data

The currently available choices include: The Amazon Redshift COPY command can load data from Amazon Simple Storage Service (Amazon S3), Amazon EMR , Amazon DynamoDB , or remote hosts over SSH. This native feature of Amazon Redshift uses massive parallel processing (MPP) to load objects directly from data sources into Redshift tables.

IoT 101