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

datapine

Winkenbach said that his data showed that “deliveries in big cities are almost always improved by creating multi-tiered systems with smaller distribution centers spread out in several neighborhoods, or simply pre-designated parking spots in garages or lots where smaller vehicles can take packages the rest of the way.”

Big Data 275
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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

Overview of Gartner’s data engineering enhancements article To set the stage for Gartner’s recommendations, let’s give an example of a new Data Engineering Manager, Marcus, who faces a whole host of challenges to succeed in his new role: Marcus has a problem.

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How Encored Technologies built serverless event-driven data pipelines with AWS

AWS Big Data

The amount of data and the number of power plants they need to collect data are rapidly increasing over time. For example, the volume of data required for training one of the ML models is more than 200 TB. Younggu Yun works at AWS Data Lab in Korea. The first step was to convert GRIB to the Parquet file format.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

These techniques allow you to: See trends and relationships among factors so you can identify operational areas that can be optimized Compare your data against hypotheses and assumptions to show how decisions might affect your organization Anticipate risk and uncertainty via mathematically modeling.

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And the winners are…. Congratulations to the Sixth Annual Data Impact Awards winners

Cloudera

Toshiba Memory’s ability to apply machine learning on petabytes of sensor and apparatus data enabled detection of small defects and inspection of all products instead of a sampling inspection. FairVentures’ Data Lab improves data access and availability to drive innovation and analytic discoveries by actuaries and data scientists.

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Eight Top DataOps Trends for 2022

DataKitchen

In 2022, data organizations will institute robust automated processes around their AI systems to make them more accountable to stakeholders. Model developers will test for AI bias as part of their pre-deployment testing. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate.

Testing 245
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Four Ways Telcos Can Realize Data-Driven Transformation

Cloudera

Large 5G networks will host tens of millions of connected devices (somewhere in the 1,000x capacity compared to 4G), each instrumented to generate telemetry data, giving telcos the ability to model and simulate operations at a level of detail previously impossible.