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Fundamentals of Data Engineering

Xebia

The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. This book is as good for a project manager or any other non-technical role as it is for a computer science student or a data engineer.

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Foote Partners: bonus disparities reveal tech skills most in demand in Q3

CIO

Other non-certified skills attracting a pay premium of 19% included data engineering , the Zachman Framework , Azure Key Vault and site reliability engineering (SRE). Close behind and rising fast, though, were security auditing and bioinformatics, offering a pay premium of 19%, up 18.8% since March.

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The 10 most in-demand tech jobs for 2023 — and how to hire for them

CIO

The role typically requires a bachelor’s degree in computer science or a related field and at least three years of experience in cloud computing. Keep an eye out for candidates with certifications such as AWS Certified Cloud Practitioner, Google Cloud Professional, and Microsoft Certified: Azure Fundamentals.

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160+ live online training courses opened for May and June

O'Reilly Media - Ideas

Spotlight on Cloud: Becoming Cloud Native with Jon Collins , May 2. Core Agile , May 9. Spotlight on Data: Data as an Asset with Friederike Schüür and Jen van der Meer , May 20. Agile for Everybody , June 19. Core Agile , July 10. Data science and data tools. Empathy at Work , June 18.

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Seeking Sustainable IT? Use Data Virtualization

TIBCO - Connected Intelligence

Opportunity 4: Migrate to the cloud. Leading cloud providers such as AWS, Microsoft Azure, and Google Cloud have developed world-class cloud data centers whose sustainability levels are difficult for organizations like yours to match because: They optimize server performance and usage elastically with demand, powering down what isn’t needed.

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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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A case for ELT

Abhishek Tiwari

As you can see data transformation before the load is an important and necessary step in this classic ETL model, and with ELT approach we are making data transformation more on-demand. When you execute a query against the data lake, data transformation are performed at run-time or in-memory. Late transformation.

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