Data Engineering Google Cloud Marketing 
CIO
FEBRUARY 19, 2025
The early part of 2024 was disappointing when it comes to ROI, says Traci Gusher, data and analytics leader at EY Americas. Some of the best use cases Ive seen have been in marketing, and thats just one area. Marketing communications is a great area for AI, he says. But now were actually starting to see real benefits, she says.
Xebia
NOVEMBER 11, 2022
We were very happy to see such a wide variety of roles from the data field attending, this really helped the engagement during sessions and increased the knowledge sharing factor of the event.” said Jelle Portegies, digital marketer for Xebia. What is the Google Cloud Data Platform Workshop?
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TechCrunch
DECEMBER 9, 2020
that was building what it dubbed an “operating system” for data warehouses, has been quietly acquired by Google’s Google Cloud division. Mining data for insights and business intelligence typically requires a team of data engineers and analysts. Dataform, a startup in the U.K.
Altexsoft
JUNE 25, 2019
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.
TechCrunch
OCTOBER 25, 2021
“It is a very crowded market,” Y42 founder and CEO Hung Dang said. ” This means Y42 wants to give business intelligence teams and data analysts a single tool that helps them bridge the gap between doing some basic data analysis and hiring multiple full-time data engineers who can maintain a modern data stack.
TechCrunch
MAY 3, 2022
“There were no purpose-built machine learning data tools in the market, so [we] started Galileo to build the machine learning data tooling stack, beginning with a [specialization in] unstructured data,” Chatterji told TechCrunch via email.
TechCrunch
MAY 10, 2022
They can build their own systems from data to deployment using low-level APIs that give them the flexibility machine learning tasks typically require at the cost of complexity. ” The market for synthetic data is bigger than you think. Indeed, while worldwide spending on AI technologies was estimated at $35.8
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