Architecture Data Engineering Industry System Architecture 
CIO
APRIL 27, 2023
The past year was rough for the tech industry, with several companies reporting layoffs and the looming threat of a recession. For technologists with the right skills and expertise, the demand for talent remains and businesses continue to invest in technical skills such as data analytics, security, and cloud. as of January.
CIO
SEPTEMBER 2, 2022
The US financial services industry has fully embraced a move to the cloud, driving a demand for tech skills such as AWS and automation, as well as Python for data analytics, Java for developing consumer-facing apps, and SQL for database work.
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CIO
AUGUST 31, 2022
The US financial services industry has fully embraced a move to the cloud, driving a demand for tech skills such as AWS and automation, as well as Python for data analytics, Java for developing consumer-facing apps, and SQL for database work.
Cloudera
FEBRUARY 1, 2024
This applies to modern generative AI solutions that are particularly reliant on trusted, accurate, and context-specific data. Implementing the right platform is half the battle won — so congratulations on your choosing Cloudera’s industry-leading hybrid data platform for building your data solutions on a foundation of trusted data.
Lacework
NOVEMBER 1, 2023
This article addresses privacy in the context of hosting data and considers how privacy by design can be incorporated into the data architecture. In many cases we see that customers prefer to have their data stored and managed locally in their home region, both for reasons of regulatory compliance and also business preference.
Kentik
APRIL 3, 2018
In this post, we dig a little deeper with real-world examples of how Kentik helps our financial industry customers solve these challenges. The kprobe agent produces flow data enriched with performance metrics and layer 7 details which are stored in the Kentik Data Engine.
Altexsoft
AUGUST 21, 2019
Data obsession is all the rage today, as all businesses struggle to get data. But, unlike oil, data itself costs nothing, unless you can make sense of it. Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data.
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