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

datapine

The rise of SaaS business intelligence tools is answering that need, providing a dynamic vessel for presenting and interacting with essential insights in a way that is digestible and accessible. The future is bright for logistics companies that are willing to take advantage of big data.

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How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

These connections empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and engines. No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data. group by year. order by year desc; year.

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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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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

While aggregating, summarizing, and aligning to a common information model, all transformations must not affect the integrity of data from its source. Fortunately, Tricentis has a product called ToscaDI, which is used to automate the measurement of data integrity across many different data sources.