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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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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

Amazon Redshift is a cloud data warehousing service that provides high-performance analytical processing based on a massively parallel processing (MPP) architecture. Building and maintaining data pipelines is a common challenge for all enterprises. The table refresh can be full or incremental based on the configuration.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Analytics use cases on data lakes are always evolving. In this post, we show you how you can use the Iceberg add_files procedure for an in-place data upgrade.

Data Lake 102
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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

Traditionally, customers used batch-based approaches for data movement from operational systems to analytical systems. A batch-based approach can introduce latency in data movement and reduce the value of data for analytics. usually a data warehouse) needs to reflect those changes in near real-time.

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Unleash the power of Snapshot Management to take automated snapshots using Amazon OpenSearch Service

AWS Big Data

in Amazon OpenSearch Service , we introduced Snapshot Management , which automates the process of taking snapshots of your domain. Snapshot Management helps you create point-in-time backups of your domain using OpenSearch Dashboards, including both data and configuration settings (for visualizations and dashboards).

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

In the following sections, we discuss the most common areas of consideration that are critical for Data Vault implementations at scale: data protection, performance and elasticity, analytical functionality, cost and resource management, availability, and scalability. Automation Automation is a major benefit of Data Vault.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 102