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CIOs are rethinking how they use public cloud services. Here’s why.

CIO Business Intelligence

Theres a renewed focus on on-premises, on-premises private cloud, or hosted private cloud versus public cloud, especially as data-heavy workloads such as generative AI have started to push cloud spend up astronomically, adds Woo. Organizations dont have much choice when it comes to using the larger foundation models such as ChatGPT 3.5

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? Building Models. A common task for a data scientist is to build a predictive model. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization. dbt Cloud is a hosted service that helps data teams productionize dbt deployments. Create dbt models in dbt Cloud.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. To achieve this, EUROGATE designed an architecture that uses Amazon DataZone to publish specific digital twin data sets, enabling access to them with SageMaker in a separate AWS account.

IoT 100
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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

In this regard, the enterprise data product catalog acts as a federated portal, facilitating cross-domain access and interoperability while maintaining alignment with governance principles. This model balances node or domain-level autonomy with enterprise-level oversight, creating a scalable and consistent framework across ANZ.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business. These needs are then quantified into data models for acquisition and delivery. This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling.

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

datapine

Big data enables automated systems by intelligently routing many data sets and data streams. In a recent move towards a more autonomous logistical future, Amazon has launched an upgraded model of its highly-successful KIVA robots. Use our 14-days free trial today & transform your supply chain!

Big Data 275