Remove Big Data Remove Data Integration Remove Operational Reporting
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How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture

AWS Big Data

The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. 2 GB into the landing zone daily.

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Use AWS Glue to streamline SFTP data processing

AWS Big Data

In today’s data-driven world, seamless integration and transformation of data across diverse sources into actionable insights is paramount. This connector provides comprehensive access to SFTP storage, facilitating cloud ETL processes for operational reporting, backup and disaster recovery, data governance, and more.

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Migrate data from Azure Blob Storage to Amazon S3 using AWS Glue

AWS Big Data

Regarding the Azure Data Lake Storage Gen2 Connector, we highlight any major differences in this post. AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning, and application development.

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Migrate data from Google Cloud Storage to Amazon S3 using AWS Glue

AWS Big Data

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning, and application development. Conclusion In this post, we showed how to use AWS Glue and the new connector for ingesting data from Google Cloud Storage to Amazon S3.

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An Overview of Real Time Data Warehousing on Cloudera

Cloudera

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

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3 Easy Steps to Finding Patterns in Big Data

Jet Global

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What is a Data Pipeline?

Jet Global

Batch processing pipelines are designed to decrease workloads by handling large volumes of data efficiently and can be useful for tasks such as data transformation, data aggregation, data integration , and data loading into a destination system. What is the difference between ETL and data pipeline?