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How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed. To share the datasets, they needed a way to share access to the data and access to catalog metadata in the form of tables and views.

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Rethinking data analytics as a digital-first driver at Dow

CIO Business Intelligence

In today’s digital world, the ability to make data-driven decisions and develop strategies that are based on data analytics is critical to success in every industry. The spokes — Dow’s businesses and functions — will perform much of their own analytics and data science.

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A Data Prediction for 2025

DataKitchen

Ultimately, there will be an interoperable toolset for running the data team , just like a more focused toolset (ELT/Data Science/BI) for acting upon data. And the tools for acting on data are consolidating: Tableau does data prep, Altreyx does data science, Qlik joined with Talend, etc.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Learn from data scientists about their responsibilities and find out how to launch a data science career. |

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Introducing Amazon MWAA larger environment sizes

AWS Big Data

Running Apache Airflow at scale puts proportionally greater load on the Airflow metadata database, sometimes leading to CPU and memory issues on the underlying Amazon Relational Database Service (Amazon RDS) cluster. A resource-starved metadata database may lead to dropped connections from your workers, failing tasks prematurely.

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Unstructured data management and governance using AWS AI/ML and analytics services

AWS Big Data

But most important of all, the assumed dormant value in the unstructured data is a question mark, which can only be answered after these sophisticated techniques have been applied. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly. The solution integrates data in three tiers.

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DataOps Facilitates Remote Work

DataKitchen

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

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