Xebia
JANUARY 19, 2023
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. The authors state that the target audience is technical people and, second, business people who work with technical people. Nevertheless, I strongly agree.
InfoQ Culture Methods
JULY 17, 2020
To mix the power of the data and the importance of people to offer business intelligence is a key point nowadays. Innovation is not only about the most advanced technology, management and processes are the new era of startups' innovation. The result is not only the most imporant thing, the way you do it more important.
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CIO
JUNE 6, 2023
On the other hand, a business that needs efficiency to scale may be better served by a central team that provides functions like data governance, platform engineering, architecture, and data engineering to all areas of the business. Heavily regulated industries tend to centralize.
TechCrunch
AUGUST 4, 2022
Company co-founder and CEO Michael Driscoll says he started the company in 2020 with the premise that the business intelligence was broken. He and his team of engineers, most of whom had came from his team at Snap, went to work on building a better solution for a broader audience. “I
CIO
JANUARY 10, 2023
Hiring tech talent in 2023 means navigating an uncertain economy, the effects of widespread tech industry layoffs, and candidates who want to work for a company with a mission and workplace culture that align with their values, including diversity, equity, and inclusion. IT leaders say the best approach is to focus on adaptability.
TechCrunch
NOVEMBER 10, 2020
Explo , a member of the Y Combinator Winter 2020 class, which is helping customers build customer-facing business intelligence dashboards, announced a $2.3 They are in the process of thinking about how they can build a culture as they bring in distributed employees. million seed round today.
CIO
MARCH 27, 2024
“Le azioni successive per il miglioramento della data quality possono essere sia di processo che applicative e includono la definizione di un modello organizzativo intorno alla data governance , assegnando ruoli e compiti chiari alle varie figure coinvolte (data scientist, data engineering, data owner, data steward, eccetera)”.
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