Remove Computer Science Remove Data Models Remove Data Warehouse
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Data warehouse architecture

Dataconomy

Want to create a robust data warehouse architecture for your business? The sheer volume of data that companies are now gathering is incredible, and understanding how best to store and use this information to extract top performance can be incredibly overwhelming.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Open-source projects, academic institutions, startups and legacy tech companies all contributed to the development of foundation models. It can be used with both on-premise and multi-cloud environments.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly Media

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? Data is at the core of any ML project, so data infrastructure is a foundational concern. Why did something break? Who did what and when?

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Their primary responsibilities include: Data Storage and Management Data Engineers design and implement storage solutions for different types of data, be it structured, semi-structured, or unstructured. They work with databases and data warehouses to ensure data integrity and security.

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From zero to BI hero: Launching your business intelligence career

Dataconomy

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

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From zero to BI hero: Launching your business intelligence career

Dataconomy

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.