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9 Amazing Application of data engineering in real life

Edureka

These are the ways that data engineering improves our lives in the real world. The field of data engineering turns unstructured data into ideas that can be used to change businesses and our lives. Data engineering can be used in any way we can think of in the real world because we live in a data-driven age.

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Is Your Financial Services Organization Ready to Leverage Generative AI?

Snowflake

According to IDC , “Ultimately, gen AI will be widely adopted only if the data, models and applications that use them are trusted by end users and customers.” Here’s how a robust data strategy can help your financial services organization overcome the barriers and blocks to gain a competitive edge with gen AI.

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Making an AI Investment: How Finance Institutions are Harnessing the Power of AI and Generative AI

Cloudera

Bridgewater Associates leverages GenAI to process data for trading signals and portfolio optimization. Trading and portfolio optimization GenAI can play a pivotal role in trading and portfolio optimization by processing vast amounts of data to generate actionable insights and trading signals.

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DELL/EMC taking the next step with PowerScale and ECS certification on CDP Private Cloud Base

Cloudera

Dell/EMC through their PowerScale and ECS product portfolio have been long time advocates of hybrid solutions. Relevance-based text search over unstructured data (text, pdf,jpg, …). “We are excited this certification will ensure our customers best in class compute and storage solutions for years to come.” .

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The Three Essentials to Get to Responsible AI

Snowflake

Whereas the existing portfolio of AI tools had targeted the more technically minded like data scientists and engineers, new tools like ChatGPT handed the keys to the kingdom to anyone who could type a question. The race for competitive advantage by applying AI to new use cases is on!

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Five Strategies to Accelerate Data Product Development

Cloudera

As a result, organizations need to evaluate the long term product portfolio strategy and how the data platform needs to be defined to realize that product vision, enabling modularity and extensibility. . 4- Compose Data Experiences Organized around Value Propositions, Not Intermediate Data Outputs.

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How Financial Services Companies Optimize Quantitative Research with the Snowflake Data Cloud 

Snowflake

In a recent Nasdaq survey , more than half (60%) of dissatisfied quantitative portfolio managers complained about an inability to quickly test new data sets. It’s clear that traditional asset managers and hedge funds need to drive greater efficiencies in portfolio construction, trade implementation, and risk mitigation.

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