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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 1

BizAcuity

Despite cost-cutting being the main reason why most companies shift to the cloud, that is not the only benefit they walk away with. Cloud washing is storing data on the cloud for use over the internet. While that allows easy access to users, and saves costs, the cloud is much more and beyond that. More on Kubernetes soon.

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It’s not your data. It’s how you use it. Unlock the power of data & build foundations of a data driven organisation

CIO Business Intelligence

The second will focus on the growth in volume and type of data required to be stored and managed, and the ways in which value can be extracted from data. The third will examine the challenges of realising that value, the attributes of a successful data-driven organisation, and the benefits that can be gained.

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A hybrid approach in healthcare data warehousing with Amazon Redshift

AWS Big Data

Regardless of the division or use case it is related to, dimensional data models can be used to store data obtained from tracking various processes like patient encounters, provider practice metrics, aftercare surveys, and more. Although data lakes resemble data vaults, a data vault provides more features of a data warehouse.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Within the context of a data mesh architecture, I will present industry settings / use cases where the particular architecture is relevant and highlight the business value that it delivers against business and technology areas. A Client Example.

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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.