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The Difference Between Data Architecture and Enterprise Architecture

erwin

Although there is some crossover, there are stark differences between data architecture and enterprise architecture (EA). That’s because data architecture is actually an offshoot of enterprise architecture. The Value of Data Architecture. Data Architecture and Data Modeling.

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Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

Below we’ll go over how a translation company, and specifically one that provides translations for businesses, can easily align with big data architecture to deliver better business growth. How Does Big Data Architecture Fit with a Translation Company? That’s the data source part of the big data architecture.

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AI Trends Are Shaping Sales

TDAN

The sales industry has been witnessing the rise of AI and automation over many years and 2023 will not be an exception. To stand out in a competitive industry, businesses must invest in revamping their existing sales processes and crafting a modern sales strategy that aligns with the sales predictions for 2023.

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Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

In this example, we have multiple files that are being loaded on a daily basis containing the sales transactions across all the stores in the US. The following day, incremental sales transactions data are loaded to a new folder in the same S3 object path. The following screenshot shows sample data stored in files.

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Build a secure data visualization application using the Amazon Redshift Data API with AWS IAM Identity Center

AWS Big Data

For instance, a global sports gear company selling products across multiple regions needs to visualize its sales data, which includes country-level details. To maintain the right level of access, the company wants to restrict data visibility based on the users role and region.

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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Data teams are confused as to whether they should get on the bandwagon of just one of these trends or pick a combination. First, we describe how data mesh and data fabric could be related.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

This architecture is valuable for organizations dealing with large volumes of diverse data sources, where maintaining accuracy and accessibility at every stage is a priority. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?