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Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

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The future of data: A 5-pillar approach to modern data management

CIO

In todays economy, as the saying goes, data is the new gold a valuable asset from a financial standpoint. A similar transformation has occurred with data. More than 20 years ago, data within organizations was like scattered rocks on early Earth.

Data 167
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What is a data engineer? An analytics role in high demand

CIO

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

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The evolution of data science, data engineering, and AI

O'Reilly Media - Data

The O’Reilly Data Show Podcast: A special episode to mark the 100th episode. This episode of the Data Show marks our 100th episode. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them.

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What is a data engineer? An analytics role in high demand

CIO

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. The data engineer role.

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The top 15 big data and data analytics certifications

CIO

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 190
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Handling real-time data operations in the enterprise

O'Reilly Media - Data

Getting DataOps right is crucial to your late-stage big data projects. Data science is the sexy thing companies want. The data engineering and operations teams don't get much love. The organizations don’t realize that data science stands on the shoulders of DataOps and data engineering giants.